KR101872457B1 - Prediction of a risk of preterm delivery using differential microbial community in a blood sample - Google Patents

Abstract

The present invention relates to prediction of premature birth risk using a change of a microbiome in blood, and more specifically, provides a composition for diagnosis of the premature birth risk by detecting strains of Weissella, Thermacetogenium, Faecalibacterium, Clostridium, or Lactobacillus in the blood of a pregnant woman.

Description

Prediction of the risk of premature birth using microbial community changes in the blood (Prediction of a preterm delivery using differential microbial community in a blood sample)

The present invention relates to a composition, a diagnostic kit, and a diagnostic method for diagnosing premature birth by detecting microbio-moth from blood of a mother. More specifically, the present invention relates to a method for detecting a preterm birth by detecting a strain of the genus Weissella, Thermacetogenium, Lactobacillus, or Clostridium, A diagnostic kit, and a diagnostic method for diagnosing a risk.

Preterm birth (PTB) generally refers to delivery between 20 and 37 weeks of pregnancy. Newborn babies born at premature births account for about 60% of all infant deaths and are at higher risk of death. Neonatal developmental disability, respiratory complications, postnatal growth retardation require neonatal intensive care and, moreover, serious long-term or short-term morbidity. About 70% of preterm births are caused by preterm labor (PTL) and premature rupture of membranes (PPROM) due to various pathological processes such as intrauterine infection and inflammation. However, the mechanism associated with preterm delivery is still unclear. Common risk factors for natural labor include genital tract infections, multiple pregnancies, a few second trimester bleeding, and previous preterm delivery history.

The minimum gestational age is 27 weeks and the birth weight is 0.9 kg to improve the survival rate of preterm infants. It is known that the morbidity and mortality of the newborn are influenced primarily by gestational age, maturity, rather than birth weight. Thus, delaying the delivery of newborns by appropriate treatment to raise maturity when early signs of premature birth come, is a crucial issue for the health and quality of life and costs of mothers and newborns.

Until now, for the treatment of premature labor, maternal use of ant contractions, antibiotics, steroids and progesterone have been used to suppress labor and delay delivery. However, in cases of preterm labor and premature rupture of membranes, the treatment effect of antibiotics on intrauterine infection and inflammation is very limited and it is known that preterm delivery can not be prevented. In addition, women with cervical incompetence have been given cervical cerclage as a treatment and prophylaxis because of the great risk of infection and miscarriage, but this can not be treated as a fundamental treatment, It is known.

Therefore, it is important to select the maternal risk pregnancies by predicting the risk of premature birth rather than to perform the treatment after symptoms of premature rupture of membranes or premature birth, and to prevent premature birth or to use effective treatment methods It is necessary to develop. In addition, the earlier the preterm delivery period, the more likely it is to leave a sequel to the neonate and the degree of sequelae is so severe that it is expected that the incidence of preterm infants and children with disabilities can be significantly reduced if the preterm delivery can be predicted.

A common approach to predicting preterm birth was to identify female populations that should be given special attention in terms of obstetric, demographic and various syndromes, but this approach is not as sensitive or specific as the approach there is a problem. To overcome these problems, many studies have been conducted to find biochemical markers for predicting sudden preterm birth or rupture of membranes. Plasma estradiol-17 beta, progesterone, C Substances such as C-reactive protein have been nominated, but this has also been shown to be less accurate. Therefore, it is necessary to develop a marker that is easier to detect and has high sensitivity and specificity.

Recent advances in gene sequencing technology have enabled the investigation of human microbiomes in various parts of the human body. However, until now, the relationship between the microbial community (microbiome) in blood and the risk of preterm birth has been identified, There is almost no research aiming to utilize it. Furthermore, no comparative studies of microbial characterization in blood have been performed in Korean mothers who have experienced preterm labor and premature rupture of membranes.

Accordingly, the present inventors completed the present invention by characterizing microorganisms in the blood of a mother who has developed preterm labor or premature rupture of membranes, which are major causes of premature birth, and identifying microbial communities in the blood that predict premature labor using gene sequence analysis.

The present invention aims to contribute to prevention of premature birth by analyzing microbiomes in the blood of a mother and identifying predictive markers of prematurity suggesting premature birth.

To this end, one example provides a composition for the diagnosis of maternal risk of premature birth comprising an agent capable of detecting microorganisms of the genus Weissella in the blood.

Another example provides a kit for the diagnosis of maternal risk of premature birth, comprising the composition.

Another example provides a method of providing information necessary for the diagnosis of premature rupture, including the step of detecting the B. subtilis microorganism from the blood of the mother.

In one aspect, the present invention provides a method of treating a disease selected from the group consisting of Weissella spp., Thermacetogenium spp ., Faecalibacterium spp ., And Clostridium spp. And a composition capable of detecting at least one microorganism.

Preferably, the diagnostic composition of the present invention may further comprise an agent capable of detecting microorganisms of a strain of the genus Lactobacillus.

Preferably, the agent capable of detecting the microorganism in the present invention may be a microbe-specific primer, a probe, an antisense oligonucleotide, an aptamer or an antibody.

More preferably, the primer can be a primer capable of amplifying 16S rRNA (16S rDNA) of microorganism. For example, the primer capable of amplifying the 16S rRNA may be a primer consisting of the sequence of SEQ ID NO: 1 or SEQ ID NO: 2.

In another aspect, the present invention relates to a kit for the diagnosis of maternal risk of premature birth comprising the composition.

In another aspect, the present invention provides a method for diagnosing preterm birth, comprising the steps of extracting from the blood of a mother a strain of Weissella, a strain of Thermacetogenium, a strain of Faecalibacterium , , Clostridium sp., And Clostridium sp. Strain. The present invention provides a method for diagnosing a risk of preterm birth, comprising the steps of: detecting a microorganism selected from the group consisting of Clostridium sp.

Preferably, the method for providing information necessary for diagnosing the risk of premature birth of the present invention may further comprise the step of detecting microorganisms of the Lactobacillus sp. Strain.

Also preferably, the method comprises

(a) extracting genomic DNA from the blood of a mother,

(b) reacting the extracted genomic DNA with a primer specific for a strain of the genus Bacillus, a genus Sarcoma genus, a genus of the genus Pecalibacterium, or a genus Clostridium; and

(c) amplifying the reactant.

Also preferably, step (c) may be carried out through a polymerase reaction.

Also preferably, step (c) may further comprise comparing the amount of amplification product to an amplification product or cut-off of normal maternal blood.

Also preferably, the amount of amplification product of the DNA of the B. subtilis strain, the S. martha toxin strain, the Pecalibacterium sp. Strain, or the Clostridium sp. Strain detected from the blood of the mother is higher than that of the normal mother , It may be a method of evaluating that the risk of premature birth is high.

Also preferably, the amount of the amplification product of the genomic DNA of the strain comprising the strains of the genus Bacillus, Thalassemethium, Clostridium, Pecalibacterium, and Lactobacillus, detected from the blood of the mother, Is higher than that of normal pregnant women, it can be considered that the risk of premature birth is high.

Hereinafter, the present invention will be described in more detail.

In the present invention, the influence of the composition of the microbial community on the premature labor in the blood blood of the mother hospitalized with premature labor and premature rupture of membranes was analyzed. As a result, an increase in the genus Weissella, an increase in the Lactobacillus, An increase in the genus Faecalibacterium, an increase in the genus Sermassonium, and / or an increase in the genus Clostridium affect preterm birth.

Therefore, in the present invention, the risk of premature birth of a mother can be diagnosed by detecting microorganisms of the genus Baicela, Pecalibacterium, Sarcassetodium, or Clostridium from the blood of the mother, Kit, and method for detecting microorganisms of the genus Pseudomonas spp., Pseudomonas spp., Pecalibacterium spp., S. spasmodium spp., Or Clostridium spp. Preferably, the present invention further provides a composition, kit and method for diagnosing maternal risk of premature birth by further detecting Lactobacillus sp. Microorganisms.

As used herein, the term " microbiome " refers to the entire genetic information of microorganisms that coexist in the human body. In the present invention, microbiome in blood means the entire genetic information of microorganisms present in the blood.

The term "risk assessment" or "risk prediction" is used herein to refer to the possibility of preterm delivery at less than 37 weeks gestation for a mother, the relatively high probability of premature delivery at less than 37 weeks, It is to determine whether there is a possibility to show signs. The present invention may be used to delay or prevent premature delivery of maternal mothers who are at high risk of premature birth for less than 37 weeks through special and appropriate care. In addition, the invention can be used clinically to make treatment decisions by early diagnosis of preterm birth of less than 37 weeks and by selecting the most appropriate treatment regimen.

As used herein, the term " bicelle "refers to a microorganism or a cluster thereof consisting of Species belonging to the genus Baicela. In the present invention, in the present invention, in comparison with the strains reported in the prior art, not only the strains reported in the prior art but also the 16S rRNA sequence of Baicela reported above are preferably at least 70%, more preferably at least 80%, even more preferably at least 90% , Most preferably at least 95%, of all microorganisms are included within the scope of the present invention.

As used herein, the term " Sarmaase toxinum " refers to a microorganism or a group of species consisting of Species belonging to the genus Taxonomic. In the present invention, Sirmaseinium is preferably not less than 70%, more preferably not less than 80%, even more preferably not more than 70%, more preferably not more than 80% , More preferably 90% or more, and most preferably 95% or more, of all microorganisms are included in the scope of the present invention.

As used herein, the term " lactobacillus " refers to a microorganism or a community thereof consisting of Species belonging to the genus Lactobacillus taxonomically. In the present invention, Lactobacillus is preferably not less than 70%, more preferably not less than 80%, more preferably not less than 90%, more preferably not less than 90% , Most preferably at least 95%, of all microorganisms are included within the scope of the present invention.

The term " clostridium "as used herein refers to a microorganism or a group thereof consisting of Species belonging to the genus Clostridium taxonomically. In the present invention, Clostridium is preferably not less than 70%, more preferably not less than 80%, more preferably not less than 90%, more preferably not less than 90%, more preferably not less than 90% Or more, and most preferably 95% or more, of all microorganisms are included in the scope of the present invention.

As used herein, the term " pecalibacterium "refers to a microorganism or a group of species consisting of Species belonging to the genus Pecalibacterium. In the present invention, peculiar bacterium is preferably 70% or more, more preferably 80% or more, more preferably 70% or more, more preferably 80% or more, , More preferably 90% or more, and most preferably 95% or more, of all microorganisms are included in the scope of the present invention.

As used herein, the term "detectable agent" refers to a substance that can be used to detect the presence of a preterm birth risk marker, Bacela, Lactobacillus, Clostridium and / or Pecalibacterium in the blood . For example, an organic substance such as proteins, nucleic acids, lipids, glycolipids, glycoproteins or saccharides (monosaccharides, disaccharides, oligosaccharides, etc.) specifically present in Baicela, Lactobacillus, Clostridia and / or Pecalibacterium A primer, a probe, an antisense oligonucleotide, an aptamer, or an antibody capable of specifically detecting a biomolecule.

Preferably, the agent which can be detected in the present invention may be a primer capable of detecting Baicela, Lactobacillus, Sarcometitanium, Clostridia and / or Pecalibacterium. It is preferred that the primers specifically detect the genomic sequence of Bacela, Lactobacillus, Sarcomassinium, Clostridium and / or Pecalibacterium, and do not specifically bind to the genomic sequence of other microorganisms. More preferably, it may be a primer capable of amplifying 16S rRNA of at least one microorganism selected from the group consisting of Baicela, Lactobacillus, Sarcomassinium, Clostridium and Pecalibacterium.

As used herein, the term "primer " refers to 7 to 50 nucleic acid sequences that can form complementary base pairs in a template strand and function as a starting point for template strand replication. Primers are usually synthesized but can also be used in naturally occurring nucleic acids. The sequence of the primer does not necessarily have to be exactly the same as the sequence of the template, but is sufficiently complementary that it can hybridize with the template. Additional features that do not alter the primer properties of the primer can be incorporated. Examples of additional features that may be incorporated include, but are not limited to, methylation, capping, substitution of one or more nucleic acids with homologues, and modification between nucleic acids. Preferably, the primer of the present invention may be a primer capable of amplifying 16S rRNA of Bacela, Lactobacillus, Sarcomassinium, Clostridium and / or Pecalibacteria microorganisms.

As used herein, the term "16S rRNA" refers to an rRNA that constitutes the 30S subunit of a prokaryotic ribosome. It includes a conserved region common to all species and a hypervariable region capable of classifying a particular species. The microorganism can be identified through sequencing. In particular, there is little diversity among homologous species, but diversity among different species appears, so that the sequence of 16S rRNA can be compared to identify prokaryotes effectively. Since 16S rDNA is a gene encoding 16S rRNA, 16S rDNA can be used to identify microorganisms.

In a preferred embodiment, the primer according to the present invention amplifies a 16S rRNA (or 16S rDNA) sequence specific for genus Bacillus, Lactobacillus, Thalassemethianum, Clostridium and / or Pecalibacterium , And the presence of the genus Baicela, Lactobacillus, Thalassemethodinium, Clostridium and / or Pecalibacterium can be detected through the generation of the desired product as a result of sequence amplification. The sequence amplification method using the primer can use various methods known in the art. For example, polymerase chain reaction (PCR), reverse-transcription polymerase chain reaction (RT-PCR), multiplex PCR, touchdown PCR, hot start PCR, nested PCR, PCR, real-time PCR, differential display PCR (DD-PCR), rapid amplification of cDNA ends (RACE), inverse polymerase chain reaction , Vectorette PCR, thermal asymmetric interlaced PCR (TAIL-PCR), ligase chain reaction, repair chain reaction, transcription-mediated amplification, autologous retention nucleotide sequence replication, or selective amplification of the target sequence , The scope of the present invention is not limited thereto.

In the present invention, the microorganism detecting agent may be an antibody, and the microorganism may be detected using an immunological method based on an antigen-antibody reaction. Analysis methods include Western blotting, enzyme linked immunosorbent as (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis But are not limited to, immunoprecipitation assay, complement fixation assay, fluorescence activated cell sorter (FACS), or protein chip, and the like.

In addition, molecular and immunological methods widely used in the art can be used to detect the microorganism of the present invention.

The diagnostic composition comprising the microorganism detection agent of the present invention may be provided in the form of a diagnostic kit. The kit of the present invention not only includes a detection agent such as a primer, a probe, an antisense oligonucleotide, an umbrella, or an antibody for detecting the microorganism, but also includes one or more other component compositions, solutions, or devices May be included.

For example, in the present invention, a kit comprising a primer specific for genus Baicela, Lactobacillus genus, Sarcometodium genus, Clostridial genus and / or Pecalibacterium may be used for carrying out an amplification reaction such as PCR And a kit containing the necessary elements for the operation. For example, the kit for PCR can be used in a test tube or other suitable container, reaction buffer, deoxynucleotides (dNTPs), enzymes such as Taq polymerase and reverse transcriptase, DNase, RNAse inhibitor, DEPC-water ), Sterile water, and the like.

In addition, the present invention provides a method for diagnosing maternal risk of premature birth by detecting microorganisms of the genus Baicela, Sermassenium, Clostridium and / or Pecalibacterium from the blood of the mother.

In other words, in order to provide information necessary for diagnosis of the risk of premature birth of a mother, the present invention provides a method for detecting a microorganism belonging to the genus Baicela, Sermassenitium, Clostridium and / or Pecalibacterium from the blood of a mother ≪ / RTI > Alternatively, there is provided a method for providing information necessary for diagnosis of premature rheumatic fever, comprising the step of detecting microorganisms of the genus Bacillus, Thalassemethodinium, Clostridium and / or Pecalibacterium from the blood of the mother .

Preferably, the method may further comprise detecting the Lactobacillus sp. Microorganism.

As a preferred example, the method may be implemented including the following steps:

(a) extracting genomic DNA from the blood of a mother,

(b) reacting the extracted genomic DNA with a primer specific for a microorganism belonging to the genus Bacillus, Lactobacillus, Pecalibacterium and / or Clostridium; and

(c) amplifying the reactant.

The extraction of the genomic DNA from the blood of the mother in the step (b) can be performed by applying a general technique known in the art, and the microorganism of the genus Baicela, Lactobacillus, Pecalibacterium and / Specific primers are as described above.

The method of amplifying the reaction product in the step (c) may be performed by a conventional amplification technique known in the art, for example, a polymerase chain reaction, a reverse-transcription polymerase chain reaction, multiplex PCR, touchdown PCR, hot start PCR, PCR, booster PCR, real-time PCR, fractional display PCR, Rapid amplification of cDNA ends, Inverse PCR, VECTORET PCR, TAIL-PCR, Ligase chain reaction, Restriction chain reaction, Transcription-mediated amplification, The selective amplification reaction of the base sequence can be used, but the scope of the present invention is not limited thereto.

In step (c), the step of comparing the amount of amplification product of the reactant with the amplification product or cut-off of normal maternal blood may be further performed, If the maternal blood is judged to be significantly increased compared with the amplification product or cut-off, the mother may be judged to have a high risk of premature birth.

In addition, the method may further comprise detecting the Lactobacillus sp. Microorganism. The method may further comprise detecting the Lactobacillus amplification product in the maternal blood by comparing with the amplification product or cut-off of the normal maternal blood It is considered that the mother is highly at risk of premature birth.

In the present invention, the ratio of microorganisms means that 16s rRNA sequence amplification amount of the corresponding microorganism is occupied by the 16s rRNA sequence amplification product of the entire microbiota in the sample. For example, the weight% w of the amplification product showing the 16s rRNA sequence amplification amount / w%), or the copy number of the amplification product.

The present invention provides a microbiological biomarker for the diagnosis of premature death, and it is possible to quickly detect the risk of premature death by simple blood collection and to develop a rapid diagnostic kit for this.

Figure 1a shows the microbial diversity of non-pregnant women, normal-born women, and PTB women by principal-coordinate analysis.
FIG. 1B shows the two-dimensional scatter plot of the microbial diversity of non-pregnant women, normal-born women and PTB women on the axis of the first and second components.
Figure 1c is a hitmap of the microbial diversity of non-pregnant women, normal-born women, and PTB women.
Figure 2a shows the number of 16s metagenomes for non-pregnant women, normal women, and PTB women.
FIG. 2B shows the Shannon index of non-pregnant women, normal-born women, and PTB women.
FIG. 3A shows the difference in microbial composition in the blood samples of non-pregnant women, normal pregnant women, and PTB women at the phyla level.
FIG. 3B shows differences in microbial composition in blood samples of non-pregnant women, normal pregnant women, and PTB women at the class level.
FIG. 4A shows the proportion of B. subtilis strains in blood samples of non-pregnant women, normal pregnant women, and PTB women.
4B shows the proportion of Lactobacillus sp. Strains in blood samples of non-pregnant women, normal pregnant women, and PTB women.
FIG. 4c shows the proportion of Clostridium spp. In blood samples of non-pregnant women, normal pregnant women, and PTB women.
FIG. 4d shows the percentage of S. metapneumia in blood samples of non-pregnant women, normal pregnant women and PTB women.
Figure 4e shows the percentage of WD2101 strains in blood samples of non-pregnant women, normal-born women and PTB women.
Figure 4f shows the ratios of TM7 strains in blood samples of non-pregnant women, normal pregnant women and PTB women.

Hereinafter, the present invention will be described in more detail with reference to the following examples. However, these examples are only for illustrating the present invention, and the scope of the present invention is not limited by these examples.

Example 1: Selection of experimental materials

For non-pregnant women, normal pregnant women, and pregnant mothers, 41 non-pregnant women admitted to Ewha Women's University Hospital, 20 normal pregnant women, 21 mothers were selected. Maternal groups with normal births were selected as mothers at 37 weeks of gestation. When pregnant women had symptoms of labor or amniotic membrane wounds, maternal blood was placed in a tube containing EDTA and the plasma was separated and stored at -70 ° C.

Data were used for 25-42 week births only, except for multiple births, stillbirths, birth defects, chronic hypertension, placenta previa, and placental abruption.

Non-pregnant women were selected as healthy women who did not suffer from pregnancy-related diseases according to the normal health examination process.

This study was approved by the Clinical Trial Ethics Committee of Ewha Womans University Mokdong Hospital (Certificate No. ECT 06-127-7) for all the procedures and procedures of the experiment and conducted the experiments according to the Guidelines of the Clinical Trial Ethics Committee. All participants were given prior consent.

Example 2 DNA Extraction and 16S rRNA Sequencing

Bacterial DNA was extracted from the blood sample of Example 1 according to the manufacturer's protocol using the PowerMax Soil DNA Isolation Kit (MOBIO, Carlsbad, Calif., USA).

The 16S rRNA gene V3-V4 hypervariable region (519F-816R) in bacterial genomic DNA was amplified according to the Illumina 16S metagenome sequencing library protocol (Illumina, San Diego, CA, USA). The bar code fusion primer sequences used for amplification are as follows

16S_V3_F primer (SEQ ID NO: 1)

5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3 ''

16S_V4_RPr (SEQ ID NO: 2)

5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3.

The PCR products were used to prepare libraries according to the MiSeq System guide (Illumina) and quantified using QIAxpert (QIAGEN, Hilden, Germany). After PCR products were extracted and quantified, each sample was irradiated at the same molar ratio according to the protocol of the manufacturer using MiSeq (Illumina) and sequenced.

Example 3: Microbial composition analysis

The sequence information obtained in Example 2 was processed using MiSeq (Illumina) according to the bar code algorithm and the primer sequence. Taxonomic analysis was performed using profiling program MDx-Pro ver.1 (MD Healthcare, Seoul, Korea).

After confirming the lead length (= 300 bp) and the quality score (average Phred score = 20), high quality read was selected. The operational taxonomy units were clustered using the sequence clustering algorithm CD-HIT. Subsequently, taxonomic analysis was performed using UCLST and QIIME according to the 16SRNA sequence database of GreenGenes 8.15.13. Based on sequence similarity, all 16 sRNA sequences were taxonomically classified. Figures 4a to 4f show the cumulative histograms of six bacterial populations in the blood of non-pregnant women, normal birth fetuses and PTB women. If the database is not in the database or the case clusters are not assigned to the rapid level due to duplicate sequences, the group is assigned to the higher level indicated in parentheses. The data were normalized by linear normalization with an average of 0 and a standard deviation of 1. Principal coordinate analysis using Matlab and two-dimensional scatter plot around the first and second components were calculated and shown. In addition, hierarchical clustering of R packages was applied to identify sample groups with similar bacterial composition.

Example 4: Statistical analysis

The experimental results are shown as mean ± standard deviation. The baseline characteristics such as age, maternal characteristics, and birth results of PTB women and normal women were compared using Student t- test.

Using the Kruskal-Wallis test, clustering characteristics of PTB, normal, and non-pregnant women were analyzed according to the significant difference of the Shannon index. Mann Whitney test was used to compare women with normal birth and women with PTB. Statistical analysis was performed using SAS software (Version 9.3; SAS Institute, Cary, NC, USA). A p-value of less than 0.05 was considered statistically significant.

[Experiment result]

1. General characteristics of the subject

Non-pregnant women were healthy 36-year-old women. Table 1 shows the general characteristics of pregnant women.

The average age of women with normal births was 31.6 years, and the average age of premature women was 30.9 years. Gestational age, neonatal weight, and Apgar score (p <0.05) were significantly lower in the prematurity group at birth (p <0.05).

2. Microbial community diversity

The taxonomic diversity and profile of bacterial DNA in blood were analyzed using high-throughput metagenomic sequencing. After clustering of operational taxonomic units and taxonomic assignments, blood clots of non-pregnant women (n = 41), women with normal birth (n = 20), and PTB women (n = 21) The characteristics of microbial composition were compared.

Principal coordinate analysis graphs and hierarchical cluster analysis showing similar bacterial composition showed differences between pregnant and nonpregnant women including normal and PTB women, It was difficult to distinguish between microbial community differences in the blood of women and PTB women (Figs. 1a, 1b, 1c).

However, when the mean of the metagenomic sequence readings was analyzed, PTB women were significantly higher (p <0.05) than non-pregnant women and normal women (Fig. 2a). In addition, the lowest Shannon index was found for the normal birth women, indicating that the diversity of the microbial community was the highest (p <0.05) (Fig. 2b).

3. Microbial community composition in blood of PTB women is different from normal women

To characterize the microbial composition among the three groups, the phylum of 1% or more was represented by a pie graph (Fig. 3A). Firmicutes (phylum), Bacteoidetes , Proteobacteria , and Actinobacteria were abundant in all groups. The Archaea Crenarchaeota and Euryarchaeota gates were more abundant in pregnant women than non - pregnant women (p <0.001). Firmicutes and Bacteoidetes were less frequent in preterm and nonpregnant women (p <0.001) (p <0.001), and Proteobacteria were fewer in preterm and preterm women than in nonpregnant women (p <0.001). As can be seen in Figure 3b, there was a large difference between the three groups at the class level. The Bacilli River was more abundant in pregnant women than non-pregnant women (p <0.001). PTB women were more abundant in PTB women than normal women (p <0.001) in the case of Alphaproteobacteria , although the level of Proteobacteria was lower. However, Betaproteobacteria And Gammaproteobacteria were lower in PTB women (p <0.001).

As shown in Table 2, 17 genera including Fusobacterium and Propionibacterium were more abundant in pregnant women than in non-pregnant women, and 25 genera including Bifidobacterium And less in pregnant women (p <0.05).

As seen in Table 3, Pseudomona s and Delftia were more common in pregnant women.

As shown in Table 4, six strains including Bacteroides and Ruminococcus were more abundant in non-pregnant and PTB women (p <0.01). In addition, three strains, including Rothia and Lautropia, were less common in non-pregnant and PTB women.

As shown in Table 5, Lactobacillus spp., Weissella spp., Clostridium spp., Thermacetogenium spp., WD2101 spp. And TM7-1 spp. Showed a large increase in PTB females (p <0.01). Of these, four genera belonging to Lactobacillus, Weissella, Clostridium, and Thermacetogenium belonged to Firmicutes phylum and Lactobacillus genus And the Weissella genus is the Bacilli class, and the Clostridium genus And the Thermacetogenium genus is Clostridia class (p <0.001). Potential pathogenic TM7-1 also increased in PTB women (p <0.001).

Phylotypes Non-pregnant women Preterm delivered women Term delivered women p value Mean (%) SD Mean (%) SD Mean (%) SD An Enrichement of phyllotypes in blood samples of pregnant women k__Archaea; p__Crenarchaeota; c__MCG 0.0010 0.0062 0.3704 0.4282 0.2044 0.2386 0.0000 k__Archaea; p__Crenarchaeota; c__MCG; o__pGrfC26 0.0011 0.0070 1.2940 0.7923 1.6515 1.4430 0.0000 k__Archaea; p__Crenarchaeota; c__Thaumarchaeota; o__Cenarchaeales; f__Cenarchaeaceae; g__Nitrosopumilus 0.0005 0.0034 0.6386 0.5444 0.9204 1.1164 0.0000 k__Archaea; p__Crenarchaeota; c__Thaumarchaeota; o__Cenarchaeales; f__SAGMA-X 0.0000 0.0003 0.8260 0.7653 0.7512 0.8821 0.0000 mastanobacteria; 0.0424 0.0766 0.3281 0.3313 0.3174 0.3720 0.0000 k__Archaea; p__Euryarchaeota; c__Methanomicrobia; o__Methanosarcinales; f__ANME-2a-2b 0.0000 0.0000 0.9386 0.6009 1.0087 1.4770 0.0000 k__Archaea; p__Euryarchaeota; c__Methanomicrobia; o__Methanosarcinales; f__ANME-2D 0.0000 0.0000 0.4137 0.3097 0.5512 0.6327 0.0000 k__Archaea; p__Euryarchaeota; c__Methanomicrobia; o__Methanosarcinales; f__Methanosaetaceae; g__Methanosaeta 0.0911 0.1616 0.5391 0.4198 0.3157 0.2954 0.0000 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Corynebacteriaceae; g__Corynebacterium 1.7318 6.7509 2.9806 1.2914 2.5090 1.8558 0.0000 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Propionibacteriaceae; g__Propionibacterium 1.3194 2.5347 5.1811 1.5418 4.1784 2.2459 0.0000 k__Bacteria; p__Cyanobacteria; c__Chloroplast; o__ stramenopiles; f __; g__ 0.0461 0.0745 0.2501 0.3237 0.2205 0.4134 0.0124 k__Bacteria; p__Cyanobacteria; c__Chloroplast; o__Streptophyta 0.3818 0.8386 3.0669 1.4653 1.9649 1.8149 0.0000 k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; f__Staphylococcaceae; g__Staphylococcus 0.1887 0.2802 1.6973 0.8316 2.2843 1.4521 0.0000 k__Bacteria; p__Fusobacteria; c__Fusobacteria; o__Fusobacteriales; f__Fusobacteriaceae; g__Fusobacterium 0.0038 0.0160 0.2431 0.2530 0.3789 0.5883 0.0000 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__Rickettsiales; f__mitochondria; 0.0521 0.1336 0.7450 0.5283 0.4030 0.5153 0.0000 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Oxalobacteraceae 0.2457 0.2165 0.6050 0.3560 1.0894 1.1506 0.0000 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__Neisseriales; f__Neisseriaceae; g__Neisseria 0.0133 0.0383 0.0730 0.1125 0.5443 2.2490 0.0012 Unassigned 4.0873 1.2345 24.6875 11.5106 36.8536 20.8460 0.0000 Low abundance of phyllotypes in blood samples of pregnant women k__Bacteria; p__Acidobacteria; c__Acidobacteria; o__Acidobacteriales; f__Koribacteraceae; g__ 0.3573 0.2538 0.0459 0.0884 0.0249 0.0823 0.0000 k__Bacteria; p__Acidobacteria; c__DA052; o__Ellin6513; f __; g__ 0.2186 0.1833 0.0300 0.0732 0.0221 0.0950 0.0000 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__C111; g__ 0.2277 0.1814 0.0792 0.1236 0.0436 0.1326 0.0000 k__Bacteria; p__Actinobacteria; c__Acidimicrobiia; o__Acidimicrobiales; f__EB1017; g__ 0.2462 0.2136 0.0430 0.0870 0.1281 0.3196 0.0000 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f __; g__ 0.4081 0.2991 0.1778 0.1901 0.1708 0.5354 0.0000 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium 3.1407 2.3639 0.5443 0.4067 0.6145 0.9518 0.0000 k__Bacteria; p__Actinobacteria; c__Coriobacteriia; o__Coriobacteriales; f__Coriobacteriaceae; g__ 0.5639 0.3363 0.0475 0.0854 0.0102 0.0336 0.0000 k__Bacteria; p__Actinobacteria; c__Coriobacteriia; o__Coriobacteriales; f__Coriobacteriaceae; g__Collinsella 2.0090 1.6569 0.0048 0.0218 0.0000 0.0000 0.0000 k__Bacteria; p__Bacteroidetes; c __ [Saprospirae]; __ [Saprospirales]; f__Chitinophagaceae; g__ 0.5011 0.3170 0.2628 0.2869 0.2615 0.4521 0.0040 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__S24-7; g__ 5.0790 3.4588 0.0529 0.0682 0.1141 0.2902 0.0000 k__Bacteria; p__Chloroflexi; c__Anaerolineae; o__Anaerolineales; f__Anaerolinaceae; g__T78 0.3883 0.6213 0.0386 0.0933 0.0148 0.0442 0.0000 k__Bacteria; p__Chloroflexi; c__Ellin6529; o __; f __; g__ 0.4352 0.2960 0.1373 0.1184 0.0951 0.1439 0.0000 k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; f__Bacillaceae; g__Bacillus 0.4561 0.4775 0.2071 0.1996 0.2300 0.2797 0.0033 k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; f__Planococcaceae; g__Lysinibacillus 0.2843 0.3263 0.0896 0.1092 0.1823 0.3990 0.0003 k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Enterococcaceae; g__Eterococcus 0.8784 0.7591 0.0540 0.0826 0.2357 0.5498 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium 0.7946 0.9045 0.1065 0.1175 0.0717 0.1974 0.0000 k__Bacteria; p__Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__Allobaculum 1.8151 1.4577 0.0000 0.0000 0.0000 0.0000 0.0000 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__Ellin329; f __; g__ 0.2252 0.1782 0.1092 0.1784 0.0168 0.0411 0.0000 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__Rhizobiales; f__Hyphomicrobiaceae; g__Rhodoplanes 0.6332 0.3548 0.2697 0.3348 0.1923 0.3555 0.0000 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__Rhizobiales; f__Rhizobiaceae; g__Rhizobium 0.8360 0.7578 0.0234 0.0688 0.0003 0.0007 0.0000 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__Rhodospirillales; f__Rhodospirillaceae; g__ 0.4318 0.3492 0.0532 0.1077 0.0900 0.3633 0.0000 bacterium; 0.4346 0.2803 0.1810 0.1818 0.4484 0.7039 0.0007 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__Desulfuromonadales; f__Geobacteraceae; g__Geobacter 0.3151 0.5503 0.0046 0.0198 0.0036 0.0121 0.0000 k__Bacteria; p__Tenericutes; c__Mollicutes; o__Entomoplasmatales; f __; g__ 0.3772 0.3804 0.0000 0.0000 0.0000 0.0000 0.0000 k__Bacteria; p__ Verrucomicrobia; c __ [Methylacidipilene]; o__Methylacidiphilales; f__Methylacidiphilaceae; g__Candidatus Methylacidiphilum 0.4062 0.3515 0.0000 0.0000 0.0000 0.0000 0.0000

Phylotypes Non-pregnant women Preterm delivered women Term delivered women p value Mean (%) SD Mean (%) SD Mean (%) SD An Enrichement of phyllotypes in blood samples of term delivered women k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Pseudomonadales; f__Pseudomonadaceae; g__Pseudomonas 0.4142 0.2593 1.0945 0.5858 4.3279 3.3250 0.0000 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Comamonadaceae; g__Delftia 0.5752 0.6053 0.8264 0.3741 3.0679 2.3863 0.0000 Low abundance of phyllotypes in blood samples of term delivered women k__Bacteria; p__Acidobacteria; c__Acidobacteria-6; o__iii1-15 0.4912 0.2900 0.1816 0.1585 0.0824 0.1947 0.0000 k__Bacteria; p__Acidobacteria; c__Solibacteres; o__Solibacterales; f__Solibacteraceae; g__Candidatus Solibacter 0.3557 0.2264 0.1988 0.1991 0.0484 0.1701 0.0000 k__Bacteria; p__Actinobacteria; c__Termoleophilia; o__Gaiellales; f__Gaiellaceae 0.5513 0.2899 0.0905 0.0887 0.0420 0.1209 0.0000 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales 0.7320 0.5199 0.4142 0.5777 0.2507 0.4504 0.0001 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Porphyromonadaceae; g__Parabacteroides 0.5481 0.4746 0.1509 0.1758 0.0495 0.1556 0.0000 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Prevotellaceae; g__Prevotella 0.8695 0.4109 0.4197 0.2892 0.3897 0.6860 0.0000 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae 0.9178 0.9229 0.2460 0.1610 0.1100 0.2355 0.0000 k__Bacteria; p__Bacteroidetes; c__Flavobacteria; o__Flavobacteriales; f__Flavobacteriaceae; g__Flavobacterium 0.2488 0.2476 0.0933 0.1185 0.0441 0.1319 0.0000 k__Bacteria; p__Chloroflexi; c__Kttedonobacteria; o__JG30-KF-AS9 0.2570 0.1936 0.0838 0.1310 0.0000 0.0000 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales 7.8066 3.6059 2.6156 0.9292 1.4241 1.2970 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__ 3.3558 1.3433 0.6128 0.4883 0.3641 0.5321 0.0000 fructose, fructose, fructose, fructose, fructose, fructose, 0.4195 0.3136 0.2678 0.3830 0.0807 0.2309 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia 0.4189 0.3470 0.0708 0.1157 0.0190 0.0838 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus 0.5752 0.3781 0.1295 0.1483 0.0639 0.1865 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Lachnospira 0.2295 0.2505 0.0327 0.0698 0.0072 0.0321 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; 0.2650 0.2400 0.0841 0.0936 0.0032 0.0041 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__ 3.4054 1.7340 2.1825 0.6248 1.1830 1.0543 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira 2.0946 1.2227 0.9134 0.3301 0.2916 0.5739 0.0000 k__Bacteria; p__Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae 0.3296 0.2979 0.1298 0.1261 0.0046 0.0200 0.0000 k__Bacteria; p__Planctomycetes; c__Planctomycetia; o__Gemmatales; f__Gemmataceae 0.3596 0.2773 0.1468 0.1752 0.0296 0.0791 0.0000 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__Myxococcales 0.4190 0.2449 0.0641 0.0852 0.0173 0.0553 0.0000 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Eterobacteriales; f__Eterobacteriaceae 2.7008 1.7836 0.2924 0.1894 0.1787 0.4539 0.0000 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Pseudomonadales; f__Pseudomonadaceae 0.3904 0.2961 0.1958 0.1737 0.0333 0.0507 0.0000 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Sinobacteraceae 0.2034 0.1906 0.1074 0.1212 0.0868 0.2180 0.0011 k__Bacteria; p__ Verrucomicrobia; c __ [Spartobacteria]; __ [Chthoniobacterales]; f __ [Chthoniobacteraceae]; g__DA101 0.5567 0.3660 0.3102 0.1895 0.1411 0.2104 0.0000 k__Bacteria; p__Verrucomicrobia; c__Verrucomicrobiae; o__Verrucomicrobiales; f__Verrucomicrobiaceae; g__Akkermansia 0.2839 0.3117 0.0793 0.1583 0.0074 0.0255 0.0000

Phylotypes Non-pregnant women Preterm delivered women Term delivered women p value Mean (%) SD Mean (%) SD Mean (%) SD An Enrichement of phyllotypes in non-pregnant women and preterm deliver pregnant women k__Bacteria; p__Acidobacteria; c__Solibacteres; o__Solibacterales 0.2657 0.2177 0.2008 0.3153 0.0457 0.1651 0.0000 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides 9.6044 5.9431 9.8477 2.2891 4.1241 2.8917 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus 0.9052 0.4308 0.9408 0.3488 0.4985 0.6471 0.0024 k__Bacteria; p__OD1; c__ZB2 0.1457 0.1724 0.2298 0.3004 0.0460 0.1170 0.0017 k__Bacteria; p__Planctomycetes; c__Planctomycetia; o__Gemmatales; f__Isosphaeraceae 0.1904 0.1574 0.1768 0.2378 0.0731 0.1689 0.0015 k__Bacteria; p__Planctomycetes; c__Planctomycetia; o__Pirellulales; f__Pirellulaceae 0.2384 0.2062 0.1980 0.2390 0.1357 0.3837 0.0034 Low abundance of phyllotypes in non-pregnant women and preterm delivered pregnant women k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Micrococcaceae; g__Rothia 0.0051 0.0139 0.0295 0.0797 0.5515 2.1427 0.0002 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Burkholderiaceae; g__Lautropia 0.0015 0.0052 0.0021 0.0044 1.0982 4.8506 0.0000 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Xanthomonadales; f__Xanthomonadaceae 0.2053 0.1992 0.2495 0.2096 1.3036 1.1821 0.0000

Phylotypes Non-pregnant women Preterm delivered women Term delivered women p value Mean (%) SD Mean (%) SD Mean (%) SD k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactobacillus 1.6006 2.6890 3.4836 1.3976 1.6499 1.1057 0.0000 k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Leuconostocaceae; g__Weissella 0.0004 0.0015 0.3377 0.2606 0.1507 0.2974 0.0000 g_Clostridia; 0.1148 0.1488 0.6855 0.4927 0.3015 0.3833 0.0000 k__Bacteria; p__Firmicutes; c__Clostridia; o__Thermoanaerobacterales; f__Thermoanaerobacteraceae; g__Thermacetogenium 0.0000 0.0000 0.3886 0.3090 0.1163 0.2317 0.0000 k__Bacteria; p__Planctomycetes; c__Phycisphaerae; o__WD2101; f __; g__ 0.5722 0.3452 0.7884 0.4805 0.3282 0.5364 0.0007 k__Bacteria; p__TM7; c__TM7-1; o __; f __; g__ 0.1351 0.1225 0.3784 0.2754 0.0686 0.1685 0.0000

4. Experimental Conclusion

The present invention has identified microbial characteristics in the blood of PTB female, non-pregnant female, and pregnant female. In addition, the present invention firstly identifies microbial composition and system in the blood during pregnancy. Although the composition of individual microbial communities between women of term and PTB was similar, the composition of the microbial community of PTB female blood was different from that of late-pregnant women and non-pregnant women. Firmicutes et al. , Bacteoidetes et al. , Proteobacteria et al. , And Actinobacteria et al . Were found in all groups. However, Crenarchaeota and Euryarchaeota moth strains belonging to the Archaea strains were more abundant in non - pregnant women than non - pregnant women. Especially, in PTB female, Proteobacteria appeared very small, and Lactobacillus , Weissella , and potential pathogenic TM7 showed high contents.

FIG. 3A is a graph showing the microbial content in the blood samples of non-pregnant women, normal pregnant women, and PTB women at the phyla level. FIG. FIG. 3B is a graph showing the microbial content in blood samples of non-pregnant women, normal pregnant women, and PTB women at a class level.

For healthy pregnancy, microbial populations throughout the body undergo major changes in response to metabolic changes and immunological adaptations. However, bacterial infections can affect pregnant tissues such as decidua, placenta, and fetal membranes and can threaten pregnant women and fetuses. In addition, 80% of premature births occurring before 30 weeks of gestation are caused by bacterial infections. The main routes of infection are as follows: (1) infection of the uterus through the lower urinary tract, (2) infection of the abdominal cavity into the uterus, and (3) infection of the maternal circulation.

The blood of a healthy person is thought to be a sterile state in which microorganisms can not inhabit. However, the presence of blood microorganisms is associated with a variety of infectious and non-infectious diseases. According to the study, most bacterial DNA present in the blood of healthy males and females is present in the buffy coat (mean 93.74%) and erythrocytes (6.23%). Microorganisms in the blood can be transferred mainly from the gastrointestinal tract or oral cavity, and the change of pathogenic microorganisms is called 'dysbiosis'. Microbial imbalances in the blood are associated with diabetes and cardiovascular disease. Also, the transfer of oral bacteria into the blood is associated with endocarditis and myocardial infarction and / or cerebral infarction, especially early-born, from periodontal disease.

According to the present invention, the diversity of the microbial community in the blood was decreased in the normal birth and PTB women compared to the non-pregnant women, but the meta genome sequence readings showed the highest level in PTB women. This implies that the microbibi in the blood of PTB women is different from that of maturing women (normal birth women) and non-pregnant women, even though the inter-individual variation of blood between the three groups is similar. In the blood samples of normal women, Firmicutes and Bacteoidetes were lowest and Proteobacteria were increased.

Proteobacteria and Actinobacteria were found to increase in the third trimester in most cases. In particular, the increase of Proteobacteria in late pregnancy was repeatedly observed under inflammation-associated dysbioses. This suggests that pregnancy is not a disease but that it migrates into the blood microbiota of intestinal microorganisms during pregnancy.

According to the present invention, Clostridia &lt; RTI ID = 0.0 &gt; And Bacteroidia strain were significantly increased in the blood of PTB and nonpregnant women than normal women. In addition, Faecalibacterium genera strain belonging to the Clostridia River And Clostridium spp. Predominated in non-pregnant women and PTB women, but these microbes were fewer in normal-born women.

Figures 4A-4F compare the ratios of each microorganism in blood samples of non-pregnant women, normal pregnant women, and PTB women.

Clostridium spp. Is an important member of the non-anaerobic intestine and cervical follicle of humans. Human clostridia infections can occur from endogenous or exogenous infections and can cause diseases such as clostridial histiotoxic syndrome. The Bacteroidia class contains only one order, and Bacteroides spp. Excludes potential pathogens and provides benefits to the host. However, strains of Bacteroides such as B. fragilis and B. thetaiotaomicron are pathogenic agents causing opportunistic infection, which can inactivate peritoneal infections, beta-lactam antibiotics, and are associated with premature birth. In addition, TM7 (phylum) increased in the blood of prematurity (PTB) women than in women. TM7 is often associated with inflammatory mucosal diseases in humans, such as inflammatory bowel disease and periodontal disease.

The present invention relates to a strain of Lactobacillus sp. And Weissella spp. Were found to be more in the blood of PTB women than normal women. However, in a previous study of the present inventors, a reduction in Lactobacillus and Weissella in the vaginal environment in the premature group relative to the normal birth group was associated with premature birth.

The present invention has analyzed the characteristics of microbiome in blood of PTB female and normal female. As a result, microbiomes in the blood of pregnant women show disease-related microbial imbalances, and moreover, the microbiomics in the blood of women who are pregnant contain microbiomics associated with a healthy condition, while PTB women The microbiome in the blood confirmed that various pathogenic microbiomes exist in an increased amount.

Table 6 shows the blood microbial content of non-pregnant women, PTB women and normal women at the genus level.

Non-pregnant woman PTB Women A normal woman p value The genus Mean (%) SD Mean (%) SD Mean (%) SD Nitrosopneumilus 0.0005 0.0034 0.6386 0.5444 0.9204 1.1164 0.0000 Methanobacterium 0.0424 0.0766 0.3281 0.3313 0.3174 0.3720 0.0000 Methanosaeta 0.0911 0.1616 0.5391 0.4198 0.3157 0.2954 0.0000 Corynebacterium 1.7318 6.7509 2.9806 1.2914 2.5090 1.8558 0.0000 Propionibacterium 1.3194 2.5347 5.1811 1.5418 4.1784 2.2459 0.0000 Staphylococcus 0.1887 0.2802 1.6973 0.8316 2.2843 1.4521 0.0000 Fusobacterium 0.0038 0.0160 0.2431 0.2530 0.3789 0.5883 0.0000 Neisseria 0.0133 0.0383 0.0730 0.1125 0.5443 2.2490 0.0012 Bifidobacterium 3.1407 2.3639 0.5443 0.4067 0.6145 0.9518 0.0000 Collinsella 2.0090 1.6569 0.0048 0.0218 0.0000 0.0000 0.0000 Bacillus 0.4561 0.4775 0.2071 0.1996 0.2300 0.2797 0.0033 Lysinibacillus 0.2843 0.3263 0.0896 0.1092 0.1823 0.3990 0.0003 Enterococcus 0.8784 0.7591 0.0540 0.0826 0.2357 0.5498 0.0000 Faecalibacterium 0.7946 0.9045 0.1065 0.1175 0.0717 0.1974 0.0000 Allobaculum 1.8151 1.4577 0.0000 0.0000 0.0000 0.0000 0.0000 Rhodoplanes 0.6332 0.3548 0.2697 0.3348 0.1923 0.3555 0.0000 Rhizobium 0.8360 0.7578 0.0234 0.0688 0.0003 0.0007 0.0000 Geobacter 0.3151 0.5503 0.0046 0.0198 0.0036 0.0121 0.0000 Candidatus Methylacidiphilum 0.4062 0.3515 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudomonas 0.4142 0.2593 1.0945 0.5858 4.3279 3.3250 0.0000 Delftia 0.5752 0.6053 0.8264 0.3741 3.0679 2.3863 0.0000 Candidatus Solibacter 0.3557 0.2264 0.1988 0.1991 0.0484 0.1701 0.0000 Parabacteroides 0.5481 0.4746 0.1509 0.1758 0.0495 0.1556 0.0000 Prevotella 0.8695 0.4109 0.4197 0.2892 0.3897 0.6860 0.0000 Flavobacterium 0.2488 0.2476 0.0933 0.1185 0.0441 0.1319 0.0000 Blautia 0.4189 0.3470 0.0708 0.1157 0.0190 0.0838 0.0000 Coprococcus 0.5752 0.3781 0.1295 0.1483 0.0639 0.1865 0.0000 Lachnospira 0.2295 0.2505 0.0327 0.0698 0.0072 0.0321 0.0000 Oscillospira 2.0946 1.2227 0.9134 0.3301 0.2916 0.5739 0.0000 Bacteroides 9.6044 5.9431 9.8477 2.2891 4.1241 2.8917 0.0000 Ruminococcus 0.9052 0.4308 0.9408 0.3488 0.4985 0.6471 0.0024 Rothia 0.0051 0.0139 0.0295 0.0797 0.5515 2.1427 0.0002 Lautropia 0.0015 0.0052 0.0021 0.0044 1.0982 4.8506 0.0000 Lactobacillus 1.6006 2.6890 3.4836 1.3976 1.6499 1.1057 0.0000 Weissella 0.0004 0.0015 0.3377 0.2606 0.1507 0.2974 0.0000 Clostridium 0.1148 0.1488 0.6855 0.4927 0.3015 0.3833 0.0000 Thermacetogenium 0.0000 0.0000 0.3886 0.3090 0.1163 0.2317 0.0000

<110> Ewha University - Industry Collaboration Foundation <120> Prediction of a risk of preterm delivery using differential          microbial community in a blood sample <130> DPP20180166KR <160> 2 <170> KoPatentin 3.0 <210> 1 <211> 50 <212> DNA <213> Artificial Sequence <220> <223> 16S_V3_F primer <400> 1 tcgtcggcag cgtcagatgt gtataagaga cagcctacgg gnggcwgcag 50 <210> 2 <211> 55 <212> DNA <213> Artificial Sequence <220> <223> 16S_V4_R primer <400> 2 gtctcgtggg ctcggagatg tgtataagag acaggactac hvgggtatct aatcc 55

Claims (12)

A blood sample of a mother is selected from the group consisting of Weissella spp., Thermacetogenium spp., Faecalibacterium spp., And Clostridium spp. A composition for the diagnosis of maternal risk of premature birth, comprising a preparation capable of detecting the above microorganisms.
The composition of claim 1, further comprising an agent capable of detecting a microorganism of a strain of the genus Lactobacillus.
The composition of claim 1, wherein the agent capable of detecting the strain is a microbe-specific primer, a probe, an antisense oligonucleotide, an aptamer, or an antibody.
4. The composition of claim 3, wherein the primer is a primer capable of amplifying the 16S rRNA of the strain.
A kit for the diagnosis of premature rheumatism comprising the composition of any one of claims 1 to 4.
The blood of the mother is selected from the group consisting of Weissella spp., Thermacetogenium spp., Faecalibacterium spp., And Clostridium spp. Comprising the step of detecting microorganisms.
7. The method of claim 6, further comprising the step of detecting a microorganism of a Lactobacillus sp. Strain.
The method according to claim 6,
(a) extracting genomic DNA from the blood of a mother,
(b) reacting the extracted genomic DNA with a primer specific for a strain of the genus Bacillus, a genus Sarcoma genus, a genus of the genus Pecalibacterium, or a genus Clostridium; and
(c) amplifying the reactant.
9. The method of claim 8, wherein step (c) is performed through a polymerase reaction.
9. The method of claim 8, further comprising comparing the amount of the amplification product of step (c) with an amplification product of genomic DNA obtained from blood of a normal birth mother.
7. The method according to claim 6, wherein the amount of amplification product of the DNA of the B. subtilis strain, the S. marcesitiium strain, the Pecalibacterium sp. Strain, or the Clostridial spp. The risk of preterm birth is high.
8. The method according to claim 7, wherein the amplification of the DNA of at least one strain selected from the group consisting of the strains of the genus Baicela, Sarcometiumum, Clostridium and Pseudomonas spp. Wherein the amount of the product is further increased compared to the normal mother and the amount of the amplified product of the genomic DNA of the Lactobacillus spp. Strain is further increased compared with the normal mother, whereby the risk of premature birth is high.

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