Methods for predicting the onset of chorioamnionitis

Abstract

Provided is a method for predicting the onset of chorioamnionitis, whereby it becomes possible to predict the onset of chorioamnionitis with high sensitivity. The method for predicting the onset of chorioamnionitis comprises: acquiring a group of data about the nucleotide sequence for 16S ribosomal RNA gene contained in a sample derived from the vagina of a subject; detecting the presence of a bacterium selected from the bacteria group G1 shown below on the basis of the acquired group of data; and associating the sample with the possibility of the onset of chorioamnionitis on the basis of the number of the detected bacteria species. The bacteria group G1: Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis); Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Streptococcus agalactiae; Anaerococcus.tetradius; Bacteroides fragilis; Gardnerella vaginalis; Mycoplasma hominis; and Sneathia sanguinegens.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the onset of chorioamnionitis. [Background technology]

[0002] Chorioamnionitis is an inflammatory disease caused by bacterial infection of the fetal membrane. Chorioamnionitis is a pathological condition that can lead to preterm birth and is closely related to postnatal outcomes, including complications such as central nervous system disorders and chronic respiratory disorders. Chorioamnionitis is diagnosed by postpartum placental pathological examination, but during pregnancy, it is also diagnosed clinically and using markers of preterm labor, such as granulocyte elastase. However, these methods have low sensitivity for detecting chorioamnionitis, making early detection difficult.

[0003] In relation to the above, a method has been proposed for detecting the presence of chorioamnionitis by detecting specific microorganisms from amniotic fluid (see, for example, JP 2017-209063 A). Summary of the Invention [Problem to be solved by the invention]

[0004] Because many cases of chorioamnionitis lead to premature birth within a short period of time, there is a need for a method for sensitively predicting the onset of chorioamnionitis in a subject. Accordingly, an object of the present invention is to provide a method for predicting the onset of chorioamnionitis that can predict the onset of chorioamnionitis with high sensitivity. [Means for solving the problem]

[0005] The first aspect is a method for predicting the onset of chorioamnionitis, which includes obtaining a set of data on the base sequence of the 16S ribosomal RNA gene contained in a sample derived from a subject's vagina, detecting the presence of bacteria selected from the group of bacteria shown in G1 below based on the obtained set of data, and associating the sample with the possibility of developing chorioamnionitis based on the number of bacterial species detected.

[0006] Bacteria group G1 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Streptococcus agalactiae; Anaerococcus tetradius; Bacteroides fragilis; Gardnerella vaginalis; Mycoplasma hominis; Sneathia sanguinegens.

[0007] A second aspect is a method for detecting chorioamnionitis, which includes obtaining a set of data on the base sequence of the 16S ribosomal RNA gene contained in a sample derived from a subject's vagina, detecting the presence of bacteria selected from the group of bacteria shown in G1 above based on the obtained set of data, and associating the sample with the presence of chorioamnionitis based on the number of bacterial species detected.

[0008] A third aspect is a method for predicting or detecting the onset of chorioamnionitis, comprising: selecting chorioamnionitis-associated bacteria in a group of subjects to prepare a group of chorioamnionitis-associated bacteria; obtaining a group of data on the base sequences of the 16S ribosomal RNA gene contained in a sample derived from the subject's vagina; detecting the presence of bacteria selected from the group of chorioamnionitis-associated bacteria based on the obtained group of data; and associating the sample with the possibility of developing chorioamnionitis or the presence of chorioamnionitis based on the number of bacterial species detected. [Effects of the Invention]

[0009] According to the present invention, a method for predicting the onset of chorioamnionitis that can predict the onset of chorioamnionitis with high sensitivity is provided. DETAILED DESCRIPTION OF THE INVENTION

[0010] As used herein, the term "step" refers not only to an independent step, but also to a step that cannot be clearly distinguished from other steps, as long as the intended purpose of the step is achieved. Hereinafter, embodiments of the present invention will be described in detail. However, the embodiments described below exemplify a method for predicting the onset of chorioamnionitis in order to embody the technical concept of the present invention, and the present invention is not limited to the method for predicting the onset of chorioamnionitis described below.

[0011] Methods for predicting the onset of chorioamnionitis A method for predicting the onset of chorioamnionitis includes a first step of obtaining a set of data on the base sequence of the 16S ribosomal RNA gene contained in a sample derived from the subject's vagina; a second step of detecting the presence of bacteria selected from the bacterial group shown in G1 below based on the obtained set of data; and a third step of associating the sample with the possibility of developing chorioamnionitis based on the number of bacterial species detected.

[0012] Bacteria group G1 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Streptococcus agalactiae; Anaerococcus tetradius; Bacteroides fragilis; Gardnerella vaginalis; Mycoplasma hominis; Sneathia sanguinegens.

[0013] In methods that use amniotic fluid as a sample and analyze the bacterial flora contained therein, bacteria cannot be detected unless the pathology has progressed from chorioamnionitis to amniotic fluid infection. Therefore, while it is possible to detect the presence of chorioamnionitis prenatally in subjects with chorioamnionitis, it is considered difficult to predict the likelihood of chorioamnionitis in subjects without chorioamnionitis. Furthermore, amniotic fluid collection is invasive to the subject and difficult to perform, and multiple amniotic fluid collections are even more risky and difficult to perform. On the other hand, in methods for predicting the onset of chorioamnionitis, the bacterial flora contained in a sample derived from the subject's vagina can be analyzed to predict the likelihood of chorioamnionitis based on the presence or absence of specific bacteria. As described below, the bacterial species to be detected are selected by correlating the vaginal sample with a definitive diagnosis by placental pathology testing after delivery, allowing for highly sensitive prediction of the likelihood of chorioamnionitis.

[0014] 1st process In the first step, a set of data on the base sequence of the 16S ribosomal RNA gene contained in a sample derived from the subject's vagina is obtained. The subject in the method for predicting the onset of chorioamnionitis may be a human pregnant woman, for example, a perinatal pregnant woman, or a perinatal pregnant woman at risk of threatened preterm labor. The vaginal sample may be, for example, a vaginal swab. Vaginal swabs can be collected using commonly used methods, such as commercially available kits, such as the OptiSwab Transport System (Puritan) or Catch-All Sample Collection Swab (Epicentre). The collected sample can be stored, for example, at -80°C.

[0015] A set of data on the base sequences of the 16S ribosomal RNA gene contained in a sample can be obtained, for example, by the following analytical method: extracting DNA from the sample, amplifying the extracted DNA by PCR using a universal primer set for the 16S ribosomal RNA gene to obtain an amplicon (PCR amplification product) of the 16S ribosomal RNA gene, and randomly determining the base sequences of the 16S ribosomal RNA gene contained in the amplicon.

[0016] DNA can be extracted from a sample by a DNA extraction method commonly used in the art. For example, cells can be physically disrupted and lysed by bead treatment using glass beads or the like, and DNA contained in the sample can be extracted using a commercially available DNA extraction kit or the like.

[0017] The extracted DNA is used as a template for PCR amplification using a universal primer set for the 16S ribosomal RNA gene to obtain an amplicon of the 16S ribosomal RNA gene. The region to be amplified by the universal primer set for the 16S ribosomal RNA gene may be any of the variable regions V1 to V9 of the 16S ribosomal RNA gene. For example, the conserved region between the V1 and V2 regions may be used as the amplified region. This allows for easy acquisition of effective phylogenetic information from the 16S ribosomal RNA gene. The universal primer may contain a so-called barcode sequence, and the barcode sequence may be located at either the 5' or 3' end.

[0018] A set of base sequence data is obtained by randomly determining the base sequences of the 16S ribosomal RNA genes contained in the obtained amplicons. The set of base sequence data is composed of a combination of the base sequence of a 16S ribosomal RNA gene specific to a certain bacterial species and the abundance (e.g., number of reads) of oligonucleotides containing that base sequence. Here, "randomly determining" means determining any base sequence contained in a mixture of DNA or other samples to be sequenced as randomly as possible, without selecting or excluding specific sequences. When base sequences are determined randomly in this manner, DNA with a base sequence that is highly concentrated in the extracted DNA or other mixture is more frequently sequenced, while DNA with a base sequence that is less frequently sequenced. Meanwhile, the vaginal microbiota contained in vaginal samples contains multiple types of bacteria, each of which has a base sequence specific to that bacterial species in its 16S ribosomal RNA gene. Therefore, in a group of base sequence data obtained by randomly determining the base sequence of a mixture of DNA, etc., prepared from a vaginal sample, the number of reads of base sequences that match any randomly selected base sequence (for example, a base sequence that matches a base sequence specific to a particular bacterial species) reflects the abundance of bacteria that make up the vaginal microbiota. In other words, the group of base sequence data reflects the structure of the vaginal microbiota.

[0019] The base sequence of the 16S ribosomal RNA gene contained in the amplicon is determined using a so-called next-generation sequencer (NGS). Next-generation sequencers are a term used in contrast to capillary sequencers that use the Sanger method. Next-generation sequencers use sequencing principles such as synthetic sequencing, pyrosequencing, and ligase reaction sequencing. Specific examples of next-generation sequencers include the MiSeq® System (Illmina), HiSeq® System (Illmina), and IonPGM® System (Life Technologies). Amplicon sequencing using next-generation sequencers can be performed according to the manufacturer's recommended protocol. Furthermore, in sequencing analysis, low-quality reads can be removed through quality control, and randomly selected reads can be used as a base sequence data set.

[0020] In the first step, instead of comprehensive analysis using a next-generation sequencer, quantitative PCR may be performed using primer sets specific to each bacterial species constituting the bacterial group to obtain a set of data on the 16S ribosomal RNA gene sequences contained in vaginal samples. Primer sets specific to each bacterial species can be created based on the standard 16S ribosomal RNA gene sequences for each bacterial species obtained, for example, from the Ribosomal Database Project (RDP, http: / / rdp.cme.msu.edu). Specifically, multiple alignment is performed using MEGA (Molecular Evolutionary Genetics Analysis, http: / / www.megasoftware.net) to identify nucleotide sequences in the 16S ribosomal RNA gene that are common within a bacterial species but unique among bacterial species. Primer sets specific to each bacterial species can then be designed for these nucleotide sequences to obtain primer sets. The specificity of the designed primer sets may also be confirmed by in-silico PCR, etc. Examples of specific primer sets include the following:

[0021] [Table 1]

[0022] Quantitative PCR can be any method that can quantitatively obtain PCR amplification products, and known methods such as real-time PCR, digital PCR, etc. In this case, the base sequence data group is a combination of the base sequence of the region to be amplified by a specific primer set and the abundance (e.g., copy number) of oligonucleotides having that base sequence, and is considered to be substantially identical to the base sequence data group obtained by a next-generation sequencer.

[0023] 2nd process In the second step, the presence of a specific bacterium selected from the group of bacteria is detected based on the obtained base sequence data set. This identifies the number of specific bacterial species contained in the sample. The presence of a specific bacterium in a sample can be determined based on the abundance of oligonucleotides containing the base sequence of a 16S ribosomal RNA gene specific to that species. When the base sequence data set is obtained using a next-generation sequencer, the presence of a bacterial species corresponding to a specific base sequence can be determined if the number of reads of an oligonucleotide containing a specific base sequence, or a corrected value obtained by correcting the number of reads of an oligonucleotide containing a specific base sequence by the number of reads of all bacterial species, is equal to or exceeds a predetermined cutoff value. Furthermore, when the base sequence data set is obtained using quantitative PCR, the presence of a bacterial species corresponding to a specific base sequence can be determined if the corrected value obtained by correcting the copy number of an oligonucleotide containing a specific base sequence by the copy number of all bacterial species is equal to or exceeds a predetermined cutoff value.

[0024] The bacterial group may be composed of bacterial species selected by the method for selecting chorioamnionitis-associated bacteria described below. By detecting specific bacteria selected from the bacterial group, the onset of chorioamnionitis can be predicted with high sensitivity. The bacterial group composed of the specific bacteria may be any of G2, G3, or G4 described below, or may be another bacterial group selected by the method for selecting chorioamnionitis-associated bacteria described below. Using the bacterial group represented by G2 can achieve a higher Youden Index and odds ratio. Furthermore, using the bacterial group represented by G3 can achieve a higher specificity and odds ratio. Furthermore, the bacterial group represented by G4 may be used in combination with the bacterial group represented by G1 or G2. By employing another bacterial group selected by the method for selecting chorioamnionitis-associated bacteria described below, it is possible to more appropriately respond to subject groups with different races, lifestyles, diseases, complications, etc.

[0025] Bacteria group G2 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Streptococcus agalactiae; Anaerococcus tetradius.

[0026] Bacteria group G3 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis.

[0027] Bacteria group G4 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Anaerococcus tetradius.

[0028] 3rd process In the third step, the sample is correlated with the likelihood that the subject from which the sample was derived will develop chorioamnionitis based on the number of specific bacterial species detected in the sample. The number of bacterial species used for the correlation is, for example, two or more, and may be two or three. For example, if two or more specific bacterial species are detected in the sample, it is predicted that the subject from whom the sample was collected will have a high risk of developing chorioamnionitis.

[0029] The sensitivity, accuracy, etc. of the chorioamnionitis onset prediction method can be adjusted by combining the bacterial group from which the specific bacteria are selected and the number of specific bacterial species detected in the sample. For example, by correlating the detection of two or more specific bacteria selected from the bacterial group shown in G1 above with the likelihood of onset, the onset of chorioamnionitis can be predicted with high sensitivity. Furthermore, the onset of chorioamnionitis can be predicted using a bacterial group other than the bacterial group shown in G1 as follows. For example, by correlating the detection of three or more specific bacteria selected from the bacterial group shown in G2 above with the likelihood of onset of chorioamnionitis, the onset of chorioamnionitis can be predicted with a high Youden Index and an excellent odds ratio. The detection of two or more specific bacteria selected from the bacterial group shown in G3 above may be correlated with the likelihood of onset of chorioamnionitis. Furthermore, the detection of two or more specific bacteria selected from the bacterial group shown in G4 above may be correlated with the likelihood of onset of chorioamnionitis. The detection of at least one specific bacterium selected from the group of bacteria shown in G4 above may be correlated with the possibility of developing chorioamnionitis, and the detection of at least one specific bacterium selected from the group of bacteria shown in G1 or G2 above may be correlated with the possibility of developing chorioamnionitis.

[0030] Subjects who are predicted to be at high risk of developing chorioamnionitis may undergo additional testing such as amniocentesis, and as necessary, may undergo follow-up observation, therapeutic intervention, and other treatments.

[0031] How to detect chorioamnionitis The method for detecting chorioamnionitis includes a first step of obtaining a set of data on the base sequence of the 16S ribosomal RNA gene contained in a sample derived from the subject's vagina; a second step of detecting the presence of bacteria selected from the group of bacteria shown in G1 above based on the obtained set of data; and a fourth step of associating the sample with the presence of chorioamnionitis based on the number of bacterial species detected.

[0032] The presence of chorioamnionitis in a subject can be detected based on the number of specific bacterial species contained in a sample derived from the vagina. This allows the presence of chorioamnionitis in a subject to be detected without waiting for a definitive diagnosis by placental pathology testing after delivery, making it possible to perform therapeutic intervention based on a diagnosis of chorioamnionitis. In other words, the method for detecting chorioamnionitis may be a method for diagnosing chorioamnionitis in a subject.

[0033] The first and second steps of the chorioamnionitis detection method are the same as those of the chorioamnionitis onset prediction method. In the fourth step, the sample is correlated with the presence of chorioamnionitis in the subject from which the sample was derived based on the number of specific bacterial species detected in the sample. The number of bacterial species used for the correlation is, for example, two or more species, and may be two or three species. For example, when two or more specific bacterial species are detected in the sample, the presence of chorioamnionitis in the subject from which the sample was collected can be detected with high sensitivity.

[0034] The sensitivity, accuracy, etc. of the method for detecting the presence of chorioamnionitis can be adjusted by combining the bacterial group from which the specific bacteria are selected and the number of specific bacterial species detected in the sample. For example, the presence of chorioamnionitis can be detected with high sensitivity by correlating the detection of two or more specific bacteria selected from the bacterial group shown in G1 above with the presence of chorioamnionitis. Furthermore, the presence of chorioamnionitis in a subject may be detected using a bacterial group other than the bacterial group shown in G1, as described below. For example, the detection of three or more specific bacteria selected from the bacterial group shown in G2 above with the presence of chorioamnionitis can be detected with a high Youden Index and an excellent odds ratio. For example, the detection of two or more specific bacteria selected from the bacterial group shown in G3 above may be correlated with the presence of chorioamnionitis. For example, the detection of two or more specific bacteria selected from the bacterial group shown in G4 above may be correlated with the presence of chorioamnionitis. For example, the detection of at least one specific bacterium selected from the group of bacteria shown in G4 above and the detection of at least one specific bacterium selected from the group of bacteria shown in G1 or G2 above may be associated with the presence of chorioamnionitis.

[0035] Methods for treating chorioamnionitis A method for treating chorioamnionitis may include a first step of obtaining a set of data on the base sequence of the 16S ribosomal RNA gene contained in a sample derived from the subject's vagina; a second step of detecting the presence of bacteria selected from the group of bacteria shown in G1 below based on the obtained set of data; a third step of correlating the sample with the possibility of developing chorioamnionitis based on the number of bacterial species detected; and a fifth step of treating a subject at high risk of developing chorioamnionitis.

[0036] By providing appropriate treatment to subjects at high risk of developing chorioamnionitis, it is possible to achieve benefits such as preventing premature birth, extending the duration of pregnancy, preventing maternal infection, preventing neonatal infection, preventing neonatal diseases such as fetal inflammatory response syndrome, neonatal meningitis, neonatal chronic lung disease, periventricular leukomalacia, cerebral palsy, and developmental delay, preventing the occurrence of disabilities, reducing medical and social security costs, and promoting the social participation of the child-rearing generation. Here, appropriate treatment can be any treatment administered for chorioamnionitis, such as treatment, improvement, inhibition of progression (prevention of worsening), prevention, and alleviation of symptoms caused by chorioamnionitis. Specific examples include administration of antibiotics, tocolytics, steroids, labor-inducing drugs, labor induction, cesarean section, hysterectomy, dilation and curettage, and hysterectomy.

[0037] Selection method for chorioamnionitis-associated bacteria The method for selecting bacteria associated with chorioamnionitis includes a sixth step of obtaining a set of data on the base sequences of 16S ribosomal RNA genes contained in samples derived from the vaginas of subjects included in a group of multiple subjects; a seventh step of determining the Blanc classification for chorioamnionitis through a pathological examination of the placenta after delivery of the subject; an eighth step of using machine learning to associate the data on the base sequences of the subjects with the Blanc classification, thereby ranking bacterial species associated with chorioamnionitis; and a ninth step of selecting bacteria associated with chorioamnionitis based on the ranking.

[0038] By applying machine learning to the relationship between the bacterial flora structure contained in samples derived from the vagina of a subject group and the diagnosis of chorioamnionitis by placental pathology testing, it is possible to select chorioamnionitis-associated bacteria present in the vagina that are associated with chorioamnionitis in the subject group. Detecting the selected chorioamnionitis-associated bacteria in a sample derived from the vagina of any subject allows the subject's risk of developing chorioamnionitis to be assessed. This makes it possible to prevent premature birth, extend pregnancy, and prevent neonatal infections associated with chorioamnionitis.

[0039] The subjects in the selection method are the same as the subjects in the prediction method described above. Furthermore, the subject group consisting of multiple subjects may be a group of randomly selected subjects, or may be a group of subjects showing a tendency for similar vaginal bacterial flora. The group of subjects showing a tendency for similar vaginal bacterial flora may be a group of subjects of a common race, a group of subjects with similar lifestyles, a group of subjects with similar diseases, complications, etc. Furthermore, the method for obtaining a data group of the nucleotide sequences of the 16S ribosomal RNA gene contained in the sample is the same as in the first step described above.

[0040] In the seventh step, a pathological examination is performed on the placenta obtained after delivery for each subject to confirm a diagnosis of chorioamnionitis and determine the Blanc classification. Here, the Blanc classification is an index indicating the degree of pathological inflammation in chorioamnionitis. In the eighth step, bacterial species associated with chorioamnionitis are identified and ranked by correlation with the Blanc classification through machine learning of the association between the subject's base sequence data and the Blanc classification. A method appropriately selected from known methods, such as random forest, can be used for machine learning. In the ninth step, chorioamnionitis-associated bacteria are selected based on the ranking. Bacterial species may be selected in descending order of their correlation with chorioamnionitis. The number of bacterial species selected may be, for example, 30, 20, 15, or 3 species. [Example]

[0041] The present invention will be specifically described below with reference to examples, but the present invention is not limited to these examples.

[0042] Example 1 Over a two-year period beginning in May 2014, 66 pregnant women with intact membranes who were admitted to Fukuoka University Hospital with a diagnosis of threatened preterm labor and from whom sufficient specimens could be collected for analysis were included. Written informed consent was obtained from the subjects. After delivery, pathological examination of the placenta was performed, and the patients were divided into two groups: 40 cases of chorioamnionitis (Blanc stage II or higher) and 26 cases of non-chorioamnionitis (Blanc stage I or lower). Vaginal swabs collected from the subjects upon admission were stored at -80°C as specimens.

[0043] Bacteria contained in each sample were lysed using glass beads in a Pathogen Lysis Tube L (QIAGEN), and DNA contained in each sample was extracted using a QIAamp UCP Pathogen Mini Kit (QIAGEN). Using the extracted DNA as a template, PCR was performed under the following conditions using the universal primer set for the V1 and V2 regions of the 16S ribosomal RNA gene to obtain PCR amplification products. The PCR amplification products obtained were stored at 4°C.

[0044] [Table 2]

[0045] [Table 3]

[0046] [Table 4]

[0047] The PCR amplification products (amplicons) of the 66 samples obtained above were simultaneously analyzed using a next-generation sequencer (NGS: MiSeq® sequencer) according to the manufacturer's recommended protocol for 16S amplicon analysis. Sequence data for the PCR amplification products was obtained in Fastq file format. Low-quality reads were removed from the obtained reads through quality control, and adapter sequences and other elements were deleted. Approximately 1,300 reads were randomly selected to obtain a set of base sequence data. The obtained data set was divided into a chorioamnionitis group and a non-chorioamnionitis group and subjected to machine learning (random forest) to identify the top 20 bacterial species highly associated with chorioamnionitis. The identified bacterial species are listed below.

[0048] [Table 5]

[0049] Of these bacteria, 18 species other than Lactobacillus gasseri and Enterococcus avium were frequently detected in the chorioamnionitis group, and the following 15 species could be identified by species name. Hereinafter, these are also referred to as bacterial group G2.

[0050] [Table 6]

[0051] Risk assessment for chorioamnionitis The relationship between the number of bacteria selected from the bacterial group G2 detected in a sample and the risk of developing chorioamnionitis was evaluated. When the detection of two or more species of bacteria belonging to bacterial group G2 was judged to be positive (high risk of developing chorioamnionitis), the sensitivity was 68%, the specificity was 69%, and the Youden Index was 37%. Furthermore, when the detection of three or more species of bacteria belonging to bacterial group G2 was judged to be positive, the sensitivity was 60%, the specificity was 88%, and the Youden Index was 48%. The results are summarized in the table below.

[0052] (Comparative Example 1) The risk of developing chorioamnionitis was evaluated in the same manner, except that the bacterial group G2 was replaced with the bacterial group G0, which was detected in the vagina among the bacteria detected in the amniotic fluid test described in JP 2017-209063 A. When the detection of two or more species of bacteria belonging to bacterial group G0 was judged to be positive, the sensitivity was 53%, the specificity was 77%, and the Youden Index was 29%. Furthermore, when the detection of three or more species of bacteria belonging to bacterial group G0 was judged to be positive, the sensitivity was 18%, the specificity was 100%, and the Youden Index was 18%. The results are summarized in the table below.

[0053] [Table 7]

[0054] Example 2 The union of bacterial groups G0 and G2 was designated bacterial group G1. The risk of developing chorioamnionitis was evaluated in the same manner, except that bacterial group G1 was used instead of bacterial group G2. When the detection of two or more species of bacteria belonging to bacterial group G1 was judged to be positive, the sensitivity was 78%, the specificity was 58%, and the Youden Index was 35%. When the detection of three or more species of bacteria belonging to bacterial group G1 was judged to be positive, the sensitivity was 63%, the specificity was 77%, and the Youden Index was 39%. The results are summarized in the table below.

[0055] [Table 8]

[0056] Example 3 The risk of developing chorioamnionitis was evaluated in the same manner, except that the bacterial group G3 below was used instead of the bacterial group G2. When the detection of two or more species of bacteria belonging to bacterial group G3 was judged to be positive, the sensitivity was 53%, the specificity was 85%, and the Youden Index was 37%. When the detection of three or more species of bacteria belonging to bacterial group G3 was judged to be positive, the sensitivity was 35%, the specificity was 100%, and the Youden Index was 35%. The results are summarized in the table below.

[0057] [Table 9]

[0058] Example 4 The risk of developing chorioamnionitis was evaluated in the same manner, except that the following bacterial group G4 was used instead of the above bacterial group G2. When the detection of two or more species of bacteria belonging to bacterial group G4 was judged to be positive, the sensitivity was 53%, the specificity was 77%, and the Youden Index was 29%. When the detection of three or more species of bacteria belonging to bacterial group G4 was judged to be positive, the sensitivity was 35%, the specificity was 96%, and the Youden Index was 31%. The results are summarized in the table below.

[0059] [Table 10]

[0060] Example 5 The risk of developing chorioamnionitis was evaluated in the same manner, except that instead of the bacterial group G2, at least one species selected from the bacterial group G4 and at least one species selected from the bacterial group G2 were detected. When the detection of two or more species of bacteria belonging to bacterial group G4 or bacterial group G2 was judged to be positive, the sensitivity was 65%, the specificity was 73%, and the Youden Index was 38%. When the detection of three or more species of bacteria belonging to bacterial group G4 or bacterial group G2 was judged to be positive, the sensitivity was 60%, the specificity was 88%, and the Youden Index was 37%. The results are summarized in the table below.

[0061] Example 6 The risk of developing chorioamnionitis was evaluated in the same manner, except that at least one species selected from the bacterial group G4 and at least one species selected from the bacterial group G1 were detected instead of the bacterial group G2. When the detection of two or more species of bacteria belonging to bacterial group G4 or bacterial group G1 was judged to be positive, the sensitivity was 68%, the specificity was 73%, and the Youden Index was 41%. When the detection of three or more species of bacteria belonging to bacterial group G4 or bacterial group G2 was judged to be positive, the sensitivity was 60%, the specificity was 77%, and the Youden Index was 37%. The results are summarized in the table below.

[0062] [Table 11]

[0063] [Table 12]

[0064] Using bacterial groups G1 to G4 detected from vaginal samples was able to predict the onset of chorioamnionitis with higher sensitivity than using bacterial group G0 detected by amniocentesis. For example, detecting two or more bacteria selected from bacterial group G1 predicted the onset of chorioamnionitis with a high sensitivity of 78%. Furthermore, detecting three or more bacteria selected from bacterial group G2 achieved a Youden Index of 48%.

[0065] Comparison of positive and negative groups for predicting chorioamnionitis The characteristics of the positive and negative groups when predicting the onset of chorioamnionitis using bacterial group G1 are shown below. Values are medians, and the Mann-Witney U test and Fisher's exact test were used to test for significance. Note that the number of developmental disorders in 3-year-old children does not include cases of abnormalities, including chromosomal abnormalities.

[0066] [Table 13]

[0067] The disclosure of Japanese Patent Application No. 2019-221539 (filing date: December 6, 2019) is incorporated herein by reference in its entirety. All documents, patent applications, and technical standards described herein are incorporated herein by reference to the same extent as if each individual document, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

Claims

1. Obtaining a set of data on the base sequence of the 16S ribosomal RNA gene contained in a sample derived from the vagina of the subject; Detecting the presence of bacteria selected from the group of bacteria shown in G2 below based on the obtained data group; correlating the sample with a likelihood of developing chorioamnionitis if the number of bacterial species detected is three or more; A method for predicting the onset of chorioamnionitis, comprising: Bacteria group G2 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Streptococcus agalactiae; Anaerococcus tetradius.

2. Obtaining a set of data on the base sequences of 16S ribosomal RNA genes contained in a sample derived from the vagina of a subject; Detecting the presence of bacteria selected from the bacterial group indicated as either G3 or G4 below based on the obtained data group; If the number of bacterial species detected is two or more, correlating the sample with the likelihood of developing chorioamnionitis; A method for predicting the onset of chorioamnionitis, comprising: Bacteria group G3 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis Bacteria group G4 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Anaerococcus tetradius.

3. Obtaining a set of data on the base sequence of the 16S ribosomal RNA gene contained in a sample derived from the vagina of the subject; Detecting the presence of bacteria selected from the group of bacteria shown in G2 below based on the obtained data group; associating the sample with the presence of chorioamnionitis if the number of bacterial species detected is three or more; A method for detecting chorioamnionitis, comprising: Bacteria group G2 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Streptococcus agalactiae; Anaerococcus tetradius.

4. Obtaining a set of data on the base sequences of 16S ribosomal RNA genes contained in a sample derived from the subject's vagina; Detecting the presence of bacteria selected from the bacterial group indicated as either G3 or G4 below based on the obtained data group; correlating the sample with the presence of chorioamnionitis if the number of bacterial species detected is two or more; A method for detecting chorioamnionitis, comprising: Bacteria group G3 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Lactobacillus jensenii; Ureaplasma parvum; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis. Bacteria group G4 Finegoldia magna; Streptococcus anginosus; Aerococcus christensenii; Prevotella disiens; Lactobacillus vaginalis; Prevotella buccalis; Dialister micraerophilus; Atopobium vaginae; Prevotella bivia; Prevotella amnii; Anaerococcus lactolytcus; Anaerococcus tetradius.

5. obtaining an amplicon of a 16S ribosomal RNA gene contained in said sample; The method according to any one of claims 1 to 4, comprising randomly determining the base sequences of the 16S ribosomal RNA gene contained in the amplicon to obtain the data group.

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