Method for detecting bacterium-derived low-molecular-weight RNA, correlation analysis method, inhibitory nucleic acid molecule, and therapeutic or prophylactic agent for cancer or liver disease

Abstract

The present invention addresses the problem of providing a method for detecting a bacterium-derived molecule contained in blood, which can be a diagnostic marker or a subject for basic research. The problem is solved by analyzing a sample prepared from a blood specimen and detecting a bacterium-derived low-molecular-weight RNA.

Description

Method for detecting bacterial small RNA, correlation analysis method, inhibitory nucleic acid molecule, and therapeutic or preventive agent for cancer or liver disease

[0001] The present invention relates to a technique for detecting and analyzing nucleic acids derived from bacteria.

[0002] As the close relationship between the human gut microbiota and disease has become clear, metagenomic analysis techniques have been used to actively study the human gut microbiota. For example, in addition to colon-related diseases, it has been reported that the human gut microbiota is associated with lifestyle- and diet-related conditions such as obesity, diabetes, various autoimmune diseases, colon cancer, liver cancer, renal failure, heart failure, neurological disorders, and mental and brain functions such as autism. Recent research has revealed that the structure of the gut microbiota is involved in the function of the entire body, regardless of the organ. Focusing on this relationship between the gut microbiota and disease is expected to lead to new, unconventional treatments and preventions for various diseases.

[0003] In this context, it has become clear that bacteria secrete extracellular vesicles (EVs) composed of lipid bilayer membranes, which can be detected in the blood and urine of their human hosts. The EVs secreted by bacteria are called membrane vesicles (MVs) or bacterial extracellular vesicles (bEVs) (collectively referred to as "bEVs" in this specification). In recent years, techniques have been proposed for diagnosing human health and disease using MVs as indicators.

[0004] For example, Patent Document 1 discloses a technology for diagnosing gastric cancer, colon cancer, pancreatic cancer, bile duct cancer, breast cancer, ovarian cancer, bladder cancer, prostate cancer, head and neck cancer, lymphoma, cardiomyopathy, atrial fibrillation, variant angina, chronic obstructive pulmonary disease, stroke, diabetes, renal failure, dementia, Parkinson's disease, or depression by extracting DNA from bEV isolated from a subject sample and performing 16S rDNA analysis.

[0005] Japanese Patent Application Laid-Open No. 2021-168683

[0006] JNCI Cancer Spectrum, Volume 7, Issue 1, February 2023, pkac080, https: / / doi. org / 10.1093 / jncics / pkac080

[0007] Tests for gut bacteria are usually performed based on fecal samples, so if a test for gut bacteria is performed as an option during a health checkup that also includes a blood test, it is necessary to obtain a fecal sample in addition to the blood sample, which is cumbersome.

[0008] Furthermore, recent research suggests that bEVs are involved not only in bacterial interactions such as quorum sensing and horizontal gene transfer, but also in bacterial-host interactions such as the delivery of toxins to host cells and immune regulation. However, much remains unknown about the functions of the biomolecules encapsulated in bEVs and their relationship to disease.

[0009] In view of the above circumstances, an objective of the present invention is to provide a method for detecting bacteria-derived molecules contained in blood that can be used as diagnostic markers or as targets for basic research.

[0010] The present inventors analyzed RNA extracted from human blood samples using a next-generation sequencer and noticed that the reads contained a significant amount of small RNA that was not of human origin. Analysis of this small RNA revealed that it was derived from bacteria. Through extensive research efforts, the present inventors discovered that this bacterial-derived small RNA exhibits a correlation with disease. It was also revealed that bacterial-derived small RNA itself acts as a functional molecule and can become pathogenic. Based on these findings, the present invention was completed. The present invention, which solves the above problems, is as shown in [1] to

[15] below.

[0011] [1] A method for detecting small RNA derived from bacteria by analyzing a sample prepared from a blood specimen.

[0012] [2] The method according to [1], wherein the small RNA to be detected is an RNA fragment that is shorter than 200 nt and has a sequence that completely matches a bacterial genome as a reference sequence, but does not completely match a genome of the animal species to which the test animal from which the blood sample was obtained belongs as a reference sequence.

[0013] [3] The method according to [1] or [2], wherein the small RNA is a fragment of one or more types of RNA selected from tRNA, rRNA, RNase P RNA, tmRNA, SRP RNA, 6s RNA, RprA RNA, glmS ribozyme, group I intron, and RtT RNA.

[0014] [4] The method according to any one of [1] to [3], wherein the small RNA to be detected has a base sequence that completely matches a part of the base sequence represented by any one of SEQ ID NOs: 1 to 33.

[0015] [5] The method according to any one of [1] to [4], wherein the small RNA to be detected is one or more species selected from RNA fragments 1 to 53, and each of the RNA fragments 1 to 53 is an RNA having a base sequence that completely matches a portion of the base sequence of the molecular species shown on the same line in Table 1, which is possessed by the bacterial species shown on the same line, and which has an overlapping region of 10% or more with the reference sequence specified by any of SEQ ID NOs: 34 to 86 shown on the same line.

[0016] [6] The method according to any one of [1] to [5], wherein the small RNA is encapsulated in a vesicle derived from a bacterium.

[0017] [7] A correlation analysis method comprising: detecting small RNAs derived from the bacteria by the method according to any one of [1] to [6]; and analyzing the correlation between the small RNAs derived from the bacteria detected from the sample and one or more conditions selected from a specific disease, a disease risk, and a health condition.

[0018] [8] The method according to any one of [1] to [6], wherein a sample prepared from a blood specimen obtained from an animal to be tested is analyzed to detect small RNAs derived from bacteria in order to test for one or more conditions selected from a specific disease, a disease risk, and a health condition in the animal to be tested.

[0019] [9] The method according to [8], wherein a correlation between the condition to be tested and the small RNA to be detected has been confirmed by the method according to [7].

[0020]

[10] The method according to [8], wherein the condition to be examined is cancer or liver fibrosis.

[0021]

[11] An inhibitory nucleic acid molecule having a base sequence complementary to a small RNA derived from a bacterium.

[0022]

[12] The inhibitory nucleic acid molecule according to

[11] , wherein the small RNA is an RNA fragment having a length of less than 200 nt, a sequence that perfectly matches the bacterial genome as a reference sequence, and a sequence that does not match the genome of the animal species to which the test animal from which the blood sample was obtained belongs as a reference sequence.

[0023]

[13] The inhibitory nucleic acid molecule according to any one of

[11] to

[12] , wherein the small RNA is a fragment of one or more types of RNA selected from tRNA, rRNA, RNase P RNA, tmRNA, SRP RNA, 6s RNA, RprA RNA, glmS ribozyme, group I intron, and RtT RNA.

[0024]

[14] The inhibitory nucleic acid molecule according to any one of

[11] to

[13] , wherein the small RNA consists of a nucleotide sequence that completely matches a part of the nucleotide sequence represented by any one of SEQ ID NOs: 1 to 33.

[0025]

[15] The inhibitory nucleic acid molecule according to any one of

[11] to

[14] , wherein the small RNA is one or more species selected from RNA fragments 1 to 53, and each of the RNA fragments 1 to 53 is an RNA having a base sequence that completely matches a portion of the base sequence of the molecular species shown in the same row in Table 1, which is possessed by the bacterial species shown in the same row, and which has an overlapping region of 10% or more with the reference sequence specified by any of SEQ ID NOs: 34 to 86 shown in the same row.

[0026]

[16] The inhibitory nucleic acid molecule according to any one of

[11] to

[15] , wherein the small RNA is encapsulated in a vesicle derived from a bacterium.

[0027]

[17] A therapeutic or preventive agent for cancer or liver disease, comprising the inhibitory nucleic acid molecule according to any one of

[11] to

[16] as an active ingredient.

[0028]

[18] A method for designing an inhibitory nucleic acid molecule according to any one of

[11] to

[16] , comprising identifying a base sequence complementary to the base sequence of the small RNA derived from the bacterium.

[0029] By applying the detection method of the present invention to bacterial RNAs that have been identified as markers for health conditions and diseases, information for diagnosing health conditions and diseases can be obtained. Furthermore, bacterial small RNAs have the potential for use as markers for health conditions and diseases. Therefore, by detecting bacterial small RNAs using the detection method of the present invention, basic research data can be collected to explore the functions of bacterial small RNAs, verify their usefulness as diagnostic markers, and verify their usefulness as drug discovery targets. In other words, the detection method of the present invention allows data collection for diagnosis and basic research.

[0030] According to the correlation analysis method of the present invention, it is possible to analyze the applicability of bacterial small RNAs as diagnostic markers for health conditions and diseases or as drug discovery targets.

[0031] The inhibitory nucleic acid molecules of the present invention can be used as tools for functional analysis of bacterial small RNAs. Furthermore, inhibitory nucleic acid molecules against bacterial small RNAs identified as disease-causing factors can be used as agents for preventing or treating the diseases.

[0032] According to the design method of the present invention, it is possible to design useful inhibitory nucleic acid molecules such as those described above.

[0033] 1 is a graph plotting the AUROC for each detected small RNA by cancer type. The symbols on the X-axis mean the following: LU: lung cancer, HC: hepatocellular carcinoma, BL: bladder cancer, PR: prostate cancer, CR: colorectal cancer, OV: ovarian cancer, BT: biliary tract cancer, PA: pancreatic cancer, GA: gastric cancer, BR: breast cancer, GL: brain tumor, ES: esophageal squamous cell carcinoma, SA: bone and soft tissue sarcoma. This figure shows the partial sequence of tRNA-Val of Klebsiella pneumoniae, which matches the base sequences of tsRNA1 and tsRNA3. This figure shows the results of RT-qPCR performed on RNA extracted from bEV secreted by Klebsiella pneumoniae using primers for tsRNA1 and tsRNA3. A sample containing no tsRNA, a negative control, and a sample containing chemically synthesized tsRNA at a predetermined concentration were used as positive controls. This figure shows an overview of the CRISPR sensor system. The left panel shows reporter gene expression in Coco-2 and THP-1 cell lines co-cultured with Klebsiella pneumoniae transformed to express sgRNA or wild-type Klebsiella pneumoniae. The right panel shows reporter gene expression in Coco-2 and THP-1 cell lines cultured in the medium supplemented with bEV secreted by Klebsiella pneumoniae transformed to express sgRNA or wild-type Klebsiella pneumoniae. The upper panel shows a graph of αSMA expression measured by RT-qPCR when LX-2 cell lines were transfected with tsRNA1 or tsRNA3 under TGF-β(-) or TGF-β(+) conditions, and the lower panel shows a photograph of the results of Western blotting. The expression level of αSMA in RT-qPCR was calculated relative to β-actin.

[0023] Figure 1 shows a graph (top) of RT-qPCR measurement of αSMA expression when LX-2 cell line was transfected with tsRNA3 alone or a combination of tsRNA3 and its antisense oligo under TGF-β(-) or TGF-β(+) conditions, and a photograph (bottom) showing the results of Western blotting. In RT-qPCR, the expression level of αSMA was calculated as the relative amount to β-actin. Figure 1 shows the results of Test Example 5.Two types of tsRNA were introduced into a hepatic stellate cell line (LX-2), and TGFβ was added to the TGFβ(+) group and cultured, followed by RNA extraction. IL-β and IL-6 expression levels were measured by RT-qPCR. This is a heat map showing the results of Test Example 6. Two types of tsRNA were introduced into a hepatic stellate cell line (LX-2), and TGFβ was added to the TGFβ(+) group and cultured, followed by RNA extraction. RNA-seq analysis was performed on this RNA sample. This is a graph showing the results of Test Example 6. Proteins were extracted from hepatic stellate cells introduced with tsRNA1 and tsRNA3, and Western blotting was performed to measure the expression level of CCND3 relative to β-actin. This is a graph showing the results of Test Example 6. Livers were collected from STAM model mice with liver cancer, and the maximum tumor diameter and Ccnd3 expression level were analyzed, confirming a positive correlation between them. This is a graph showing the results of Test Example 6. A hepatic stellate cell line (LX-2) was transfected with siRNA having a sequence complementary to CCND3 mRNA and cultured. RNA was then extracted using Quick-RNA MiniPrep Plus (ZYMO RESEARCH), and the expression levels of CCND3, IL-1β, and IL-6 were measured by RT-qPCR. This graph shows the results of Test Example 6. tsRNA1 or tsRNA3, or each antisense oligonucleotide together with tsRNA1 or tsRNA3, was transfected into hepatic stellate cells and cultured. Protein was extracted and Western blotting was performed. The relative intensity of the CCND3 band relative to the intensity of the β-actin band was graphed. This is a schematic diagram showing the flow of a method for detecting small RNAs derived from bacteria. This is a schematic diagram showing the flow of a correlation analysis method. This is a diagram showing an example of calculating the percentage (%) of regions where small RNAs perfectly match a reference sequence (overlapping regions).

[0034] The present invention will be described in detail below. In the embodiments of the present invention, A (numerical value) to B (numerical value) means A or more and B or less. The preferred and more preferred embodiments exemplified below can be used in appropriate combinations with each other, regardless of expressions such as "for example," "one," "preferable," and "more preferred." Numerical ranges are merely examples, and ranges obtained by appropriately combining the upper and lower limits of each range and the numerical values ​​of the examples can also be used (for example, when A to B or C to D is stated, A to D or C to B can be used). Furthermore, terms such as "contain" or "comprise" may be interpreted as "essentially consisting of" or "consisting only of."

[0035] 1. Detection Method Section 1 describes an embodiment of the detection method of the present invention. The present invention is a method for analyzing a sample prepared from a blood specimen and detecting small RNA derived from bacteria. The present invention will be described with appropriate reference to the simple flowchart shown in Figure 14.

[0036] In one embodiment, the present invention includes a sample preparation step of processing a blood sample obtained from a subject to prepare a sample, and an analysis step of analyzing the sample. The blood sample may be obtained from any animal, such as a human, dog, cat, cow, pig, mouse, or rat. From the viewpoint of applicability to diagnostic techniques and drug discovery, it is preferable to use a blood sample collected from a human.

[0037] The specific embodiment of the sample preparation step can be appropriately set depending on the analysis method in the analysis step described below. Examples of samples prepared in the sample preparation step include whole blood, serum, and plasma, as well as samples prepared by treating these samples by any treatment method.

[0038] In one embodiment, the sample preparation step includes removing cellular components by centrifugation and / or filtration. When performing filtration, it is preferable to use, for example, a 0.22 μm filter. Samples subjected to such treatment are substantially free of cellular components, such as host blood cell components and bacterial cells. Therefore, errors in data interpretation due to the detection of RNA molecules encapsulated in cells can be avoided, and small RNAs encapsulated in extracellular vesicles can be accurately detected.

[0039] In one embodiment, the sample preparation step includes fractionation and purification of small RNAs, and analysis of such a sample allows for efficient detection of small RNAs.

[0040] Small RNA fractionation and purification can be easily performed using commercially available kits, such as ISOSPIN Liquid Sample miRNA (manufactured by Nippon Gene Co., Ltd.) and PureLink. ＴＭ Examples include miRNA isolation kit (manufactured by Thermo Fisher Scientific).

[0041] In one embodiment, the sample prepared by the sample preparation step contains bacterial-derived small RNA and host-derived small RNA. As described in Non-Patent Document 1, analyzing miRNA, a small RNA of the host, can distinguish various types of cancer with high accuracy. By analyzing a sample containing bacterial-derived small RNA and host-derived small RNA, two different targets, host-derived and bacterial-derived small RNA, can be simultaneously detected, enabling multifaceted data collection.

[0042] Next, the analysis step will be described. The method for analyzing the sample is not particularly limited as long as it can detect small RNAs derived from bacteria. Examples include RT-PCR (which may be qualitative, quantitative, or determinative), microarrays, and Northern blotting, but it is preferable to use a next-generation sequencer because it can simultaneously detect multiple small RNAs.

[0043] Examples of sequencing technologies include sequencing technologies that are based on sequencing principles other than the Sanger method and can obtain a large number of reads per run, such as ion semiconductor sequencing, pyrosequencing, sequencing-by-synthesis using reversible dye terminators, sequencing-by-ligation, and sequencing by oligonucleotide probe ligation.

[0044] When RT-PCR is used to detect bacterial small RNAs, primers capable of specifically detecting the small RNAs can be used. When a microarray is used to detect bacterial small RNAs, a chip on which a nucleic acid having a sequence complementary to the small RNAs is immobilized can be used. When Northern blotting is used to detect bacterial small RNAs, a probe capable of specifically detecting the small RNAs can be used. When a next-generation sequencer is used to detect bacterial small RNAs, an embodiment in which base-called reads are mapped using the bacterial genome as a reference may be used.

[0045] The bacterial species used as a reference for mapping is not particularly limited, and one or more species may be selected. Preferably, bacterial species that have been reported to be associated with diseases, more specifically, cancer, can be used.

[0046] The bacteria used as references for mapping include the genus Klebsiella, Bacteroides, Gemella, Fusobacterium, Leptotrichia, Parvimonas, and Peptostreptococcus. tococcus, Prevotella, Selenomonas, Solobacterium and Streptococcus, Candidatus, Hathewayia, Alliococcus, Pelobacter One or more species selected from bacteria belonging to the genera Pelobacter, Mycoplasma, Ruminococcus, Porphyromonas, Neisseria, and Haemophilus can be selected.

[0047] The bacterial species used as references for mapping include Klebsiella pneumoniae, Bacteroides fragilis, Gemella morbillorum, Fusobacterium necrophorum, Fusobacterium nucleatum, Fusobacterium periodonticum, Leptotrichia trevisanii, Parvimonas micra, and the like. micra), Peptostreptococcus stomatis, Prevotella tannerae, Selenomonas sputigena, Solobacterium moorei, Streptococcus mutans, Streptococcus oralis, Candidatus symbiosum, Streptococcus wadsworthensis, wadsworthensis, Hathewayia hathewayi, Alliococcus colihominis, Pelobacter pallens, Mycoplasma micronuciformis, Ruminococcus aeria, Porphyromonas gingivalis, Streptococcus mitis, Neisseria elongata elongata), Haemophilus effluvii,One or more species selected from Haemophilus hominis can be selected.

[0048] As used herein, the term "small RNA" refers to RNA of less than 200 nt. Specific examples of small RNA derived from bacteria that are the target of detection include, without limitation, bacterial RNA of less than 200 nt.

[0049] As described above, the small RNA to be detected is less than 200 nt, preferably less than 180 nt, more preferably less than 150 nt, even more preferably less than 120 nt, even more preferably less than 100 nt, and even more preferably less than 80 nt.

[0050] Furthermore, from the viewpoint of improving detection accuracy, the small RNA to be detected is preferably 5 nt or more, more preferably 7 nt or more, even more preferably 10 nt or more, even more preferably 12 nt or more, and even more preferably 15 nt or more.

[0051] It is preferable that the small RNA to be detected satisfies both of the following conditions 1 and 2: (Condition 1) It has a sequence that perfectly matches the bacterial genome as a reference sequence; (Condition 2) It has a sequence that does not perfectly match the genome of the animal species to which the test animal from which the blood sample was obtained belongs as a reference sequence. By using small RNA that satisfies conditions 1 and 2 as the detection target, the risk of erroneously determining that RNA derived from the test animal is of bacterial origin can be eliminated.

[0052] Examples of small RNAs to be detected include one or more fragments selected from bacterial-derived tRNA (transfer RNA), rRNA (ribosomal RNA), RNase P RNA, tmRNA (transfer-messenger RNA), SRP RNA (signal recognition particle RNA), 6s RNA, RprA RNA, glmS ribozyme (glmS glucosamine-6-phosphate activated ribozyme), group I intron, and RtT RNA (RtT RNA (repeat structure of the tyrT operon)). These RNA fragments have been shown to correlate with diseases, more specifically cancer, in test examples described below, and can therefore be used as diagnostic markers or drug discovery targets.

[0053] In one embodiment, the small RNA to be detected satisfies conditions 1 and 2 and consists of a base sequence that completely matches a portion of the base sequence represented by any one of SEQ ID NOs: 1 to 33. The base sequences represented by SEQ ID NOs: 1 to 33 correspond to the full-length sequences of the following molecular species:

[0054] <SEQ ID NO: 1> tRNA-Val of Klebsiella pneumoniae <SEQ ID NO: 2> tRNA-Val of Klebsiella pneumoniae <SEQ ID NO: 3> tRNA-Asp of Fusobacterium necrophorum <SEQ ID NO: 4> tRNA-Lys of Parvimonas micra <SEQ ID NO: 5> tRNA-Ser of Parvimonas micra <SEQ ID NO: 6> tRNA-Gly of Parvimonas micra <SEQ ID NO: 7> tRNA-Gly of Parvimonas micra <SEQ ID NO: 10> 23S rRNA of Streptococcus mutans <SEQ ID NO: 11> tRNA-Arg of Streptococcus oralis <SEQ ID NO: 12> tRNA-Ala of Candidatus symbiosum <SEQ ID NO: 13> tRNA-Ala of Streptococcus wadsworthensis <SEQ ID NO: 14> tRNA-Ile from Streptococcus wadsworthensis <SEQ ID NO: 15> tRNA-Lys from Streptococcus wadsworthensis <SEQ ID NO: 16> tRNA-Ala from Hathewayia hathewayi <SEQ ID NO: 17> tRNA-Ala from Hathewayia hathewayi <SEQ ID NO: 18> tRNA-Ala from Alliococcus colihominis<SEQ ID NO: 19> tRNA-Tyr of Alliococcus colihominis <SEQ ID NO: 20> tRNA-Ala of Pelobacter pallens <SEQ ID NO: 21> tRNA-Met of Pelobacter pallens <SEQ ID NO: 22> tRNA-Ala of Mycoplasma micronuciformis <SEQ ID NO: 23> tRNA-Gly of Ruminococcus aeria <SEQ ID NO: 24> tRNA-Pro of Porphyromonas gingivalis <SEQ ID NO: 25> tRNA-Pro of Porphyromonas gingivalis <SEQ ID NO: 26> tRNA-Thr of Porphyromonas gingivalis <SEQ ID NO: 27> tRNA-Ala of Porphyromonas gingivalis <SEQ ID NO: 28> tRNA-Glu of Streptococcus mitis <SEQ ID NO: 29> tRNA-Arg of Streptococcus mitis <SEQ ID NO: 30> tRNA-Ala of Neisseria elongata <SEQ ID NO: 31> tRNA-Gly from Neisseria elongata <SEQ ID NO: 32> tRNA-Ala from Haemophilus effluvii <SEQ ID NO: 33> tRNA-Ala from Haemophilus hominis

[0055] In one embodiment, one or more species selected from RNA fragments 1 to 53 are targeted for detection. Each of RNA fragments 1 to 53 is identified as satisfying the above-mentioned conditions 1 and 2, as well as the following conditions 3 and 4. (Condition 3) The RNA fragments consist of a base sequence that completely matches a portion of the base sequence of the molecular species shown in the same row and possessed by the bacterial species shown in the same row in Table 1. (Condition 4) The RNA has an overlapping region of 10% or more with the reference sequence identified by any of SEQ ID NOs: 34 to 86 shown in the same row in Table 1.

[0056]

[0057] In condition 4, the percentage (%) of the overlapping region between the target small RNA and the reference sequence is calculated using the following formula: Number of bases in the base sequence of the target small RNA that match the reference sequence × 100 / Number of bases in the reference sequence

[0058] For example, in case 1 of Figure 16, the target small RNA, consisting of eight bases, perfectly matches the eight bases within the reference sequence. In this case, the target small RNA is said to have an 80% overlap region with the reference sequence. In case 2 of Figure 16, a 10-base sequence within a 15-base target small RNA perfectly matches the 10-base reference sequence. In this case, the target small RNA is said to have a 100% overlap region with the reference sequence. In case 3 of Figure 16, the eight bases at the 5' end of the 12-base target small RNA perfectly match the eight bases at the 3' end of the reference sequence. In this case, the target small RNA is said to have an 80% overlap region with the reference sequence.

[0059] Under condition 4, the ratio of the overlapping region between the reference sequence and the target small RNA is 10% or more, preferably 15% or more, more preferably 20% or more, even more preferably 30% or more, even more preferably 40% or more, and even more preferably 50% or more.

[0060] Furthermore, the ratio of the overlapping region between the reference sequence and the target small RNA may be 60% or more, 70% or more, 80% or more, 90% or more, or even 100%.

[0061] Bacterial RNA fragments useful as diagnostic markers for diseases have been discovered. However, it has been found that there is variation in the cleavage sites of full-length RNA during the production process of these RNA fragments. Condition 4 reflects this variation in the cleavage sites.

[0062] In one embodiment, the target of detection is a small RNA that has a base sequence that completely matches a portion of the base sequence of any of the molecular species identified in Tables 2 to 14 described below, but has a sequence that does not completely match when the genome of the animal species to which the test animal from which the blood sample was obtained belongs is used as a reference sequence.

[0063] In one embodiment, it is preferable to detect small RNAs encapsulated in bacterial vesicles (bEVs). In this case, it is preferable to analyze a sample containing extracellular vesicles from which cellular components have been removed by centrifugation and / or filtration, as described above.

[0064] In one embodiment, a sample containing bacterial-derived small RNAs and host-derived small RNAs is analyzed to detect both bacterial-derived small RNAs and host-derived small RNAs. In a more preferred embodiment, a sample containing bacterial-derived small RNAs and host-derived small RNAs is analyzed using a next-generation sequencer, and mapping is performed using the genome of the host animal species and one or more bacterial genomes as references, respectively. This embodiment allows for simultaneous detection of two different targets, host-derived and bacterial-derived small RNAs, enabling multifaceted data collection.

[0065] The detection results may be qualitative or quantitative data, and are determined appropriately depending on the analytical method. When quantitative data is obtained, the data may be absolute values ​​or relative values.

[0066] When analyzing with a next-generation sequencer, the detection results of bacterial small RNAs may be obtained as the number of reads or as a mapping rate calculated according to the following formula: Mapping rate = number of mapped reads / total number of reads in the sample.

[0067] As described in the test examples below, most of the bacterial small RNAs detected in blood were not invalid as indicators for use in disease diagnostic models. Therefore, the detection method of the present invention can be applied to data collection for the diagnosis or detection of specific diseases, disease risks, and health conditions.

[0068] The detection method of the present invention can also be applied to data collection to analyze correlations between specific diseases, disease risks, and health conditions and small RNA molecules derived from bacteria.

[0069] Furthermore, as shown in the test examples described below, bacterial small RNAs can be targets for drug discovery. Therefore, the detection method of the present invention can be applied to data collection for the search for novel drug discovery targets.

[0070] 2. Correlation Analysis Method In Section 2, the correlation analysis method will be explained with reference to Figure 15 as appropriate. The correlation analysis method of the present invention comprises detecting bacterial small RNAs using the method described above. The correlation analysis method of the present invention then includes a correlation analysis step of analyzing the correlation between the bacterial small RNAs detected from the sample and one or more conditions selected from a specific disease, disease risk, and health condition.

[0071] The explanation in Section 1 is applicable to an embodiment of the step of detecting small RNAs derived from bacteria in the correlation analysis method of the present invention.

[0072] In one embodiment, samples prepared from blood specimens obtained from a group of patients with a specific disease or a group of subjects in a specific health condition (a group of subjects with a specific disease, etc.) and a group of healthy subjects not suffering from the above-mentioned specific disease, etc. (a group of healthy subjects) are analyzed to detect small RNAs derived from bacteria (Figure 15).

[0073] In one embodiment, in the correlation analysis process, based on the detection results for a group of subjects with a specific disease, etc. and a group of healthy subjects, small bacterial RNAs that are significantly detected, or that are detected in significantly higher or lower amounts, in the group of subjects with a specific disease, etc. are identified.

[0074] The specific method of correlation analysis is not limited, and known statistical methods can be used without limitation. For example, the AUC (under the curve) of a receiver operating characteristic curve (ROC curve) created based on the detection results of a group of subjects with a specific disease or the like and a group of healthy subjects can be calculated. In this case, if the AUC is not 0.5, it can be said that the diagnostic model using the detection value of bacterial small RNA as an index is not invalid and can be used to determine whether or not a subject has the specific disease, the risk of developing the specific disease, or the specific health condition.

[0075] The diseases and health conditions to be analyzed are not limited, and examples include diseases or health conditions that have been suggested to be associated with the intestinal microbiota. Examples of such diseases or health conditions include cancer, digestive disorders, mental disorders, metabolic disorders, immune system disorders, and cardiovascular diseases. Specific examples include inflammatory bowel diseases such as ulcerative colitis and Crohn's disease, intestinal diseases such as irritable bowel syndrome, non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, cirrhosis, and other liver diseases accompanied by liver fibrosis, pancreatic diseases such as acute pancreatitis and chronic pancreatitis, diabetes, bile acid metabolism disorders, short-chain fatty acid metabolism disorders, lipid metabolism disorders, insulin resistance, hypertension, glomerulonephritis, chronic kidney disease, angina pectoris, myocardial infarction, cardiovascular disease, bone metabolism disorders, anemia, fatigue, sarcopenia, major depressive disorder, bipolar disorder, autism, and anxiety disorders. Examples of cancer include lung cancer, hepatocellular carcinoma, bladder cancer, prostate cancer, colon cancer, ovarian cancer, biliary tract cancer, pancreatic cancer, stomach cancer, breast cancer, brain tumor, esophageal squamous cell carcinoma, and bone and soft tissue sarcoma.

[0076] 3. Detection Method for Testing Section 3 describes an embodiment in which the detection method described in Section 1 is applied for testing. This embodiment involves analyzing a sample prepared from a blood specimen obtained from a test subject to detect bacterial small RNAs in order to test for one or more conditions selected from a specific disease, disease risk, and health condition in the test subject animal.

[0077] The explanation in Section 1 is applicable to an embodiment of the step of detecting small RNAs derived from bacteria in the correlation analysis method of the present invention.

[0078] There are no particular limitations on the animals to be tested, and any animals such as humans, dogs, cats, cows, pigs, mice, and rats may be used.

[0079] The condition to be examined is not limited. For example, the examination can be performed on one or more conditions selected from cancer, digestive disorders, psychiatric disorders, metabolic disorders, immune system disorders, and cardiovascular disorders, which are listed as analysis targets in Section 2.

[0080] In one embodiment, the test target can be one or more conditions selected from diseases, disease risks, and health conditions that have been confirmed to be correlated with the small RNA derived from the bacteria being detected by the correlation analysis method described in Section 2.

[0081] In one embodiment, the condition to be examined is a cancer selected from lung cancer, hepatocellular carcinoma, bladder cancer, prostate cancer, colorectal cancer, ovarian cancer, biliary tract cancer, pancreatic cancer, gastric cancer, breast cancer, brain tumor, esophageal squamous cell carcinoma, and bone and soft tissue sarcoma, or liver fibrosis.

[0082] In one embodiment, the correlation analysis method includes a correlation analysis step of carrying out the correlation analysis method described in Section 2, and includes a detection step of analyzing a sample prepared from a blood specimen obtained from the test subject and detecting bacterial-derived small RNA in order to examine one or more conditions selected from diseases, disease risks, and health conditions that have been confirmed to be correlated with the bacterial-derived small RNA in the correlation analysis step.

[0083] In one embodiment, the bacterial small RNA to be detected consists of a base sequence that completely matches a portion of the base sequence represented by any one of SEQ ID NOs: 1 to 33, which satisfies the above-mentioned conditions 1 and 2, and the condition to be tested is cancer selected from lung cancer, hepatocellular carcinoma, bladder cancer, prostate cancer, colorectal cancer, ovarian cancer, biliary tract cancer, pancreatic cancer, gastric cancer, breast cancer, brain tumor, esophageal squamous cell carcinoma, and bone and soft tissue sarcoma, or liver fibrosis.

[0084] In one embodiment, the bacterial small RNA to be detected is one or more types selected from RNA fragments 1 to 53 that satisfy the above-mentioned conditions 1 and 2, and the condition to be examined is cancer selected from lung cancer, hepatocellular carcinoma, bladder cancer, prostate cancer, colorectal cancer, ovarian cancer, biliary tract cancer, pancreatic cancer, gastric cancer, breast cancer, brain tumor, esophageal squamous cell carcinoma, and bone and soft tissue sarcoma, or liver fibrosis.

[0085] In one embodiment, a threshold is set in advance for the absolute or relative amount of small RNA derived from the bacterium to be detected, and if an amount of the small RNA detected is equal to or greater than the threshold, the animal to be tested is determined to be in the condition desired for the test.

[0086] In one embodiment, the method comprises comparing the absolute or relative amount of the small RNA detected to the threshold value.

[0087] There are no particular limitations on the method for setting a threshold for the absolute or relative amount of small RNA derived from the bacterium to be detected, which is used to determine the state of interest in the test. The threshold can be set by adjusting the balance between the true positive rate (TPR) and the false positive rate (FPR) so as to match the accuracy and purpose required for the test.

[0088] 4. Inhibitory Nucleic Acid Molecules Section 4 describes the inhibitory nucleic acid molecules of the present invention, which have a base sequence complementary to a small RNA derived from a bacterium.

[0089] For specific embodiments of small RNAs derived from bacteria that are targets of the inhibitory nucleic acid molecules of the present invention, the explanation for the small RNAs derived from bacteria that were the detection targets in Section 1 applies as is.

[0090] The "inhibitory nucleic acid molecule" is not limited as long as it has a base sequence complementary to a bacterial small RNA and induces functional inhibition by hybridization or subsequent RNA degradation, and examples thereof include siRNA (small interfering RNA), shRNA (short hairpin RNA), and antisense nucleic acid. The "inhibitory nucleic acid molecule" may be RNA, DNA, PNA, or a complex thereof.

[0091] siRNA is a 21-25 nt dsRNA with a dinucleotide 3' overhang, and is formed by cleaving a long dsRNA by Dicer in the RNA interference pathway. RNAi is induced by introducing synthetic siRNA into mammalian cells.

[0092] shRNAs are hairpin-shaped RNA molecules used for gene silencing by RNA interference: shRNAs are cleaved by Dicer, and either strand of the double-stranded molecule is loaded into RISC, where it degrades the complementary RNA target.

[0093] Antisense nucleic acids are nucleic acids that form hybrids with target RNA. DNA or chemically modified versions thereof can also be used as antisense nucleic acids. In this case, RNase H recognizes the hybrid (DNA-RNA) between the target RNA and the antisense nucleic acid, and the target RNA is degraded. Various chemical modifications (phosphorothioate, etc.) are performed to confer resistance to nucleases. RNA can also be used as antisense nucleic acids. In this case, the function of the target RNA can be inhibited by forming a hybrid (RNA-RNA) between the nucleotide-resistant antisense RNA and the target RNA.

[0094] As described in the test examples below, bacterial small RNAs can be used not only as diagnostic markers for diseases and health conditions, but also as functional molecules themselves, which can affect the host. The inhibitory nucleic acid molecules of the present invention can be used as tools for functional analysis of bacterial small RNAs. Furthermore, inhibitory nucleic acid molecules against bacterial small RNAs identified as disease-causing factors can be used as agents for preventing or treating the disease.

[0095] In one embodiment, the inhibitory nucleic acid molecule has a base sequence complementary to a bacterial small RNA encapsulated in a bacterial vesicle (bEV). As described below, the bacterial small RNA may leak into the host's bloodstream while contained in the bEV, circulate throughout the body, and affect the host. Inhibitory nucleic acids targeting the bacterial small RNA encapsulated in bEV are useful as tools for analyzing the effects of the small RNA on the host, and as agents for preventing or treating diseases in the host.

[0096] In one embodiment, the inhibitory nucleic acid molecule has a base sequence complementary to a fragment of one or more types of RNA selected from bacterial tRNA, rRNA, RNase P RNA, tmRNA, SRP RNA, 6s RNA, RprA RNA, glmS ribozyme, group I intron, and RtT RNA. As described in the test examples below, these RNA fragments have been suggested to be deeply involved in host diseases and health conditions. Therefore, the inhibitory nucleic acid molecule of this embodiment is highly useful as a tool for functional analysis or as an agent for preventing or treating host diseases.

[0097] The inhibitory nucleic acid molecule of the present invention can be used as an active ingredient in a preventive or therapeutic agent for cancer, digestive system disease, psychiatric disease, metabolic disease, immune system disease, or cardiovascular disease. More specifically, the inhibitory nucleic acid molecule of the present invention can be used to treat inflammatory bowel diseases such as ulcerative colitis and Crohn's disease, intestinal diseases such as irritable bowel syndrome, non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, cirrhosis, and other liver diseases accompanied by liver fibrosis, pancreatic diseases such as acute pancreatitis and chronic pancreatitis, diabetes, bile acid metabolism disorders, short-chain fatty acid metabolism disorders, lipid metabolism disorders, insulin resistance, hypertension, glomerulonephritis, chronic kidney disease, angina pectoris, myocardial infarction, cardiovascular disease, bone metabolism disorders, anemia, fatigue, sarcopenia, major depressive disorder, bipolar disorder, autism, anxiety, and the like. The compound can be used as an active ingredient in a preventive or therapeutic agent for one or more cancers selected from lung cancer, hepatocellular carcinoma, bladder cancer, prostate cancer, colon cancer, ovarian cancer, biliary tract cancer, pancreatic cancer, stomach cancer, breast cancer, brain tumor, esophageal squamous cell carcinoma, and bone and soft tissue sarcoma.

[0098] In one embodiment, the inhibitory nucleic acid molecule satisfies the above conditions 1 and 2 and has a sequence complementary to a nucleotide sequence that completely matches a portion of the nucleotide sequence represented by any one of SEQ ID NOs: 1 to 33. These inhibitory nucleic acid molecules against bacterial-derived small RNAs are effective as agents for preventing or treating cancer selected from lung cancer, hepatocellular carcinoma, bladder cancer, prostate cancer, colon cancer, ovarian cancer, biliary tract cancer, pancreatic cancer, gastric cancer, breast cancer, brain tumor, esophageal squamous cell carcinoma, and bone and soft tissue sarcoma, or liver fibrosis.

[0099] In one embodiment, the inhibitory nucleic acid molecule has a sequence complementary to one or more base sequences selected from RNA fragments 1 to 53, which satisfy the above-mentioned conditions 1 and 2. These inhibitory nucleic acid molecules against bacterial-derived small RNAs are effective as agents for preventing or treating cancer selected from lung cancer, hepatocellular carcinoma, bladder cancer, prostate cancer, colon cancer, ovarian cancer, biliary tract cancer, pancreatic cancer, gastric cancer, breast cancer, brain tumor, esophageal squamous cell carcinoma, and bone and soft tissue sarcoma, or liver fibrosis.

[0100] 5. Design Method The design method of the present invention is a method for designing an inhibitory nucleic acid molecule against a small RNA, which comprises identifying a base sequence complementary to the base sequence of the small RNA derived from a bacterium.

[0101] Regarding specific embodiments of inhibitory nucleic acid molecules to be designed by the design method of the present invention, the explanation in Section 4 is applicable as is.

[0102] As described above, inhibitory nucleic acid molecules include siRNA (small interfering RNA), shRNA (short hairpin RNA), and antisense nucleic acids, and software for designing these molecules is generally available. In the present invention, these software programs can be used to identify base sequences complementary to the base sequences of bacterial small RNAs and design inhibitory nucleic acid molecules.

[0103] Test Example 1: Mapping of small RNAs in human serum to bacterial genomes. Blood samples collected from patients diagnosed with malignant tumors (cancer group) and healthy adults (non-cancer group) were centrifuged to obtain serum. The cancers studied were lung cancer, hepatocellular carcinoma, bladder cancer, prostate cancer, colon cancer, ovarian cancer, biliary tract cancer, pancreatic cancer, gastric cancer, breast cancer, brain tumor, esophageal squamous cell carcinoma, and bone and soft tissue sarcoma.

[0104] RNA was extracted from serum, and the small RNA fraction was subjected to next-generation sequencing. The obtained small RNA sequence information was mapped to 46 bacterial genomes reported to be associated with cancer. The mapping rate calculated using the following formula was compared between the cancer and non-cancer groups, and AUROC (the area under the receiver operating characteristic curve) was calculated. Mapping rate = number of mapped reads / total number of reads in the sample.

[0105] A graph plotting the AUROC for each detected small RNA by cancer type is shown in Figure 1. An AUROC of 0.5 indicates that the model attempting to discriminate cancer using that small RNA is invalid, while the closer it is to 1, the more excellent the model can be used. An AUROC of less than 0.5 suggests that the model can be used as a useful model by reversing the relationship between the threshold and positive / negative results.

[0106] As shown in Figure 1, almost all of the detected bacterial-derived small RNAs were found to be valid indicators of cancer diagnostic models (AUROC not 0.5). Many bacterial-derived small RNAs, such as tRNA fragments (tsRNA) and rRNA fragments, were detected, and these were found to correlate with cancer.

[0107] The small RNAs detected and mapped to the reference sequence of each bacterial genome are listed below (Tables 2 to 14), which were determined to be valid as indicators of cancer diagnostic models (AUROC not 0.5). Each table shows the gene ID of the sequence to which the detected small RNA was aligned, the accession number (Chr) of the reference genome sequence information, the start and end of the aligned gene, and the molecular species.

[0108] Klebsiella pneumoniae (Table 2)

[0109] Bacteroides fragilis (Table 3)

[0110] Gemella morbilorum (Table 4)

[0111] Fusobacterium necroforum (Table 5)

[0112] Fusobacterium nucleatum (Table 6)

[0113] Fusobacterium periodonticum (Table 7)

[0114] Leptotrichia trevisanii (Table 8)

[0115] Parvimonas micra (Table 9)

[0116] Peptostreptococcus stomatis (Table 10)

[0117] Prevotella tanerae (Table 11)

[0118] Selenomonas sputigena (Table 12)

[0119] Solobacterium moulei (Table 13)

[0120] Streptococcus mutans (Table 14)

[0121] The tables listed above are assigned branch numbers. When a table is referred to by its trunk number without the branch number, it refers to a group of multiple tables identified by the branch numbers that are connected to the trunk number.

[0122] As shown in Tables 2 to 14, fragments of one or more types of RNA selected from bacterial tRNA, rRNA, RNase P RNA, tmRNA, SRP RNA, 6s RNA, RprA RNA, glmS ribozyme, group I intron, and RtT RNA can be used as indicators for cancer diagnostic models.

[0123] In particular, small RNAs mapped to parts of the sequences of the molecular species represented by SEQ ID NOs: 1 to 33 exhibited an AUROC of 0.75 or more, and were shown to be useful.

[0124] More specifically, small RNAs having reference sequences represented by SEQ ID NOs: 34 to 86 exhibit an AUROC of 0.75 or more and have sequences that do not match the genomic sequence of the human subject, and are therefore particularly useful as diagnostic markers.

[0125] More specifically, small RNAs having the reference sequences represented by SEQ ID NOs: 34 to 86 were significantly detected in hepatocellular carcinoma patients compared to non-cancer patients. Therefore, small RNAs having the reference sequences represented by SEQ ID NOs: 34 to 86 are particularly useful as diagnostic markers for hepatocellular carcinoma.

[0126] It is reasonably assumed that these small RNAs are produced by cleavage and fragmentation of full-length RNAs expressed by bacteria. However, the mapping results in Test Example 1 revealed that there are various variations in the cleavage sites. Therefore, it is preferable to detect the above-mentioned RNA fragments 1 to 53, which have sequences that overlap with the reference sequences represented by SEQ ID NOs: 34 to 86 within a certain range.

[0127] The results of this test example will be supplemented below. Among the small RNAs determined to be usable as indicators for the diagnostic model in this test example, two sequences identified as matching the partial sequence of tRNA-Val of Klebsiella pneumoniae will be described as examples (referred to as tsRNA1 and tsRNA3, respectively). tsRNA1 is a small RNA represented by SEQ ID NO: 34, and has a sequence that does not completely match the human genome sequence.

[0128] The base sequences of these small RNAs are also found in the base sequences of tRNAs of bacterial species other than Klebsiella pneumoniae. In this test example, the base sequences of small RNAs detected by a next-generation sequencer were mapped using the genome sequences of 46 bacterial species, including Klebsiella pneumoniae, as a reference. As a result, tsRNA1 and tsRNA3 were identified as fragments of tRNA-Val (tsRNA) of Klebsiella pneumoniae, but this does not confirm that the detected tsRNA1 and tsRNA3 are derived from Klebsiella pneumoniae.

[0129] In other words, the results of this test indicate that health conditions and diseases can be diagnosed using the presence or absence of detection of specific small RNAs, or their absolute or relative amounts, as indicators, without specifying the type of bacteria.

[0130] The technology described in Patent Document 1 identifies the type of bacteria (family, phylum, or genus) by 16S rDNA analysis and attempts to diagnose based on the correlation between the amount of bEV derived from that bacteria and the disease. On the other hand, as mentioned above, the present invention is crucially different in that it does not require identification of the type of bacteria.

[0131] Test Example 2: Detection of tsRNA from Klebsiella pneumoniae-derived bEVs As shown in the results of Test Example 1, bacterial small RNAs were detected in human blood, and this was found to correlate with disease. There have been cases where the barrier function of the intestinal wall has been disrupted, resulting in the bacterial cells themselves leaking into the blood. However, given that bacterial small RNAs were also detected in the blood of healthy individuals, it is highly likely that the bacterial cells themselves are not leaking into the blood, but rather that the bacteria secrete bEVs encapsulating small RNAs, which then circulate through the human host via the blood. To verify this possibility, we decided to verify that small RNAs are encapsulated in the bEVs secreted by the bacteria. In Test Example 2, we focused on tsRNA1 and tsRNA3 detected in Test Example 1, and analyzed the bEVs secreted by Klebsiella pneumoniae, which are likely their origin, using the following procedure.

[0132] Klebsiella pneumoniae was cultured in suspension in a liquid medium, and the culture medium was centrifuged to precipitate the bacterial cells, yielding a culture supernatant containing bEV. RNA was extracted from the culture supernatant using an RNA extraction kit. RT-qPCR was performed on the extracted RNA using primers designed to target tsRNA1 and tsRNA3. RT-qPCR was similarly performed on a sample containing predetermined concentrations of chemically synthesized tsRNA1 and tsRNA3 as a positive control, and a sample containing no tsRNA as a negative control.

[0133] As a result, tsRNA1 and tsRNA3 were detected from bEV secreted by Klebsiella pneumoniae ( FIG. 3 ). Combined with the results of Test Example 1, the results of Test Example 2 support the hypothesis of the present inventors that bacteria secrete bEV encapsulating small RNAs, which then circulate in the bloodstream within the body of the human host.

[0134] Test Example 3: Verification of the transfer of small RNA encapsulated in bEV into host cells Test Examples 1 and 2 revealed that bacteria secrete bEV encapsulating small RNA, which then circulates in the bloodstream within the host human body. Test Example 3 verified whether the small RNA encapsulated in bEV can be taken up by host cells and function.

[0135] In Test Example 3, the CRISPR sensor system outlined in Figure 4 was used. Specifically, bacteria transformed to express sgRNA and host cells expressing Cas9 and into which a reporter sequence having the structure shown in Figure 4 was introduced were used. A stop cassette is inserted upstream of a tricistronic reporter gene encoding RFP, LacZ, and Luc juxtaposed via a 2A peptide, so the reporter gene is not normally expressed. However, when the complex of CAS9 and sgRNA acts on the target sequence of the sgRNA, a mutation is introduced into the stop cassette, and the reporter gene is expressed. In other words, when the sgRNA expressed by the bacteria reaches the host cell via bEV and is transferred into the nucleus, the reporter gene is expressed.

[0136] In Test Example 3, Klebsiella pneumoniae transformed to express sgRNA was used as the bacterium, and a human colon cancer-derived cell line (Caco-2) and a human monocyte-like cell line (THP-1) were used as the host cells. Two types of test systems were also performed: one in which Klebsiella pneumoniae and host cells were co-cultured using a culture insert, and the other in which bEV isolated from the culture supernatant of Klebsiella pneumoniae was added to the culture medium of the host cells. Wild-type Klebsiella pneumoniae that did not express sgRNA was used as a negative control.

[0137] As a result, reporter gene expression was observed in host cells co-cultured with transformed Klebsiella pneumoniae, as well as in host cells cultured with the addition of bEV derived from transformed Klebsiella pneumoniae (Figure 5). This result indicates that the small RNAs carried by the bEV secreted by the bacteria are transported into the nucleus of the host cell and function there. Combining the results of Test Examples 1 to 3, it was revealed that the small RNAs derived from the bacteria leak into the blood loaded on bEV, circulate within the host body, and are taken up into the nucleus of the host cell, thereby exerting their function.

[0138] <Test Example 4> Functional analysis of bacterial-derived small RNAs (1) In Test Example 4, the effects of tsRNA1 and tsRNA3 on human cells were examined. As shown in the results of Test Example 1, tsRNA1 and tsRNA3 are strongly associated with liver cancer. Therefore, in Test Example 4, the examination was focused on liver disease.

[0139] Hepatic fibrosis is caused by transformation of hepatic stellate cells, the responsible cells, through activation by humoral factors such as TGF-β, resulting in the production of fibers. It is known that the progression of fibrosis increases the risk of developing liver cancer. In Test Example 4, the activation of hepatic stellate cells by tsRNA1 and tsRNA3 was examined.

[0140] Twenty-four hours after seeding the hepatic stellate cell line (LX-2), mirVana miRNA Mimic, Negative Control #1 (NC) (Thermo Fisher) and two types of tsRNA were transfected using jetPRIME. Furthermore, for the TGFβ(+) group, TGFβ was added to the growth medium at a concentration of 5 ng / mL. The cells were incubated for 24 hours in CO . ２ The cells were cultured in an incubator. 24 hours after the RNA transfection, the growth medium was replaced to remove the reagent, and TGFβ was added again to the TGFβ(+) group.

[0141] Forty-eight hours after RNA transfection, RNA was extracted using Quick-RNA MiniPrep Plus (ZYMO RESEARCH), and the expression level of αSMA relative to β-actin was measured by RT-qPCR. αSMA is an activation marker for hepatic stellate cells. Protein was also extracted and subjected to Western blotting. The results are shown in Figure 6.

[0142] Furthermore, an experiment was conducted in which an antisense oligo of tsRNA3 was introduced together with tsRNA3 using a similar procedure. The expression levels of αSMA relative to β-actin measured by RT-qPCR in this experiment are shown in Figure 7.

[0143] As shown in Figures 6 and 7, it was confirmed that the expression level of αSMA was increased in hepatic stellate cells transfected with tsRNA1 and tsRNA3. This result indicates that tsRNA1 and tsRNA3 have the function of activating hepatic stellate cells.

[0144] Test Example 5: Functional analysis of bacterial-derived small RNAs (2) Using the same procedure as in Test Example 4, two types of tsRNA were introduced into a hepatic stellate cell line (LX-2), and TGFβ was added to the TGFβ(+) group for culture, followed by RNA extraction. The expression levels of IL-β and IL-6, known as SASP factors, were measured by RT-qPCR. The results are shown in Figure 8.

[0145] As shown in Figure 8, increased expression of IL-1β and IL-6 was observed in hepatic stellate cells transfected with tsRNA1 and tsRNA3. These results suggest that tsRNA1 and tsRNA3 induce increased expression of SASP factors, thereby damaging surrounding cells and promoting carcinogenesis.

[0146] Test Example 6: Functional analysis of bacterial-derived small RNAs (2) Using the same procedures as in Test Examples 4 and 5, two types of tsRNA were introduced into a hepatic stellate cell line (LX-2), and TGFβ was added to the TGFβ(+) group for culture, followed by RNA extraction. RNA-seq analysis was performed on this RNA sample. The results are shown in Figure 9.

[0147] As shown in Figure 9, enhanced CCND3 gene expression was observed in hepatic stellate cells transfected with tsRNA1 and tsRNA3. Proteins were extracted from the cells and subjected to Western blotting to measure the expression level of CCND3 relative to β-actin, and similar results were obtained (Figure 10).

[0148] CCND3 was verified using the STAM model mouse. This model shows a pathological progression similar to that of human MASH / NASH-HCC. This model has a background of late-stage type 2 diabetes, and the pathology progresses to fatty liver, NASH, fibrosis, and liver cancer. Livers were collected from STAM model mice that had developed liver cancer, and the maximum tumor diameter and Ccnd3 expression level were analyzed. A positive correlation was confirmed between these two (Figure 11).

[0149] In addition, a hepatic stellate cell line (LX-2) was transfected with siRNA having a sequence complementary to CCND3 mRNA and cultured. RNA was then extracted using Quick-RNA MiniPrep Plus (ZYMO RESEARCH), and the expression levels of CCND3, IL-1β, and IL-6 were measured by RT-qPCR. The results are shown in Figure 12.

[0150] As shown in Figure 12, it was confirmed that the expression levels of IL-1β and IL-6 were reduced in the cell line in which CCND3 was knocked down. This result suggests that CCND3 may induce SASP in hepatic stellate cells.

[0151] Similarly, hepatic stellate cells were transfected with tsRNA1 or tsRNA3, or with tsRNA1 or tsRNA3 and the respective antisense oligos, and then cultured. Proteins were extracted and subjected to Western blotting. The relative intensity of the CCND3 band to that of β-actin was plotted (Figure 13).

[0152] As shown in Figure 13, it was confirmed that the expression level of CCND3 was reduced in the group into which tsRNA1 and antisense oligo were introduced. This result suggests that tsRNA1 activates CCND3 in a sequence-specific manner.

[0153] The results of Test Examples 4 to 6 revealed that tsRNA1 and tsRNA3, when introduced into hepatic stellate cells, activate intracellular signaling pathways and are involved in the onset or progression of liver cancer.

[0154] Specifically, both tsRNAs promoted the expression of αSMA, an activation marker of hepatic stellate cells, as well as the expression of the inflammatory cytokines IL-1β and IL-6. Furthermore, gene expression analysis and knockdown experiments identified CCND3 as a molecule located downstream of tsRNA1 and tsRNA3, suggesting that CCND3 regulates the expression of these SASP factors upstream.

[0155] In other words, tsRNA1 and tsRNA3 regulate the expression of SASP factors via CCND3, creating a chronic inflammatory environment that may promote the onset or progression of liver cancer through adverse effects on surrounding cells and deterioration of the tumor microenvironment.

[0156] These results indicate that tsRNA1 and tsRNA3 are involved in a novel molecular mechanism that contributes to the development and progression of liver cancer via the CCND3-SASP factor pathway.

[0157] <Summary> The results of Test Examples 1 to 6 revealed that bacterial-derived small RNAs, loaded onto bEVs, leak into the bloodstream, circulate within the host's body, and are taken up by host cells. Furthermore, it was revealed that bacterial-derived small RNAs are not simply metabolites excreted by bacteria, but are functional molecules that bring about changes in the host cells that take them up.

[0158] These results indicate that bacterial small RNAs in the blood can be used as indicators to diagnose the host's health and disease. Furthermore, bacterial small RNAs are not simply disease markers; they themselves act as functional molecules and can be pathogenic, making them promising targets for drug discovery.

[0159] The results of this test example demonstrate that by applying the detection method of the present invention to bacterial RNA identified as a marker for health or disease, an indicator for diagnosing a subject's health or disease can be obtained.

[0160] Bacterial small RNAs have the potential to be used as markers for health and disease or as targets for drug discovery. Therefore, the results of this test example support the idea that detecting bacterial small RNAs of unknown function using the detection method of the present invention makes it possible to collect data for the development of diagnostic techniques and drug discovery research.

[0161] The results of this test example demonstrate that the effectiveness of bacterial-derived small RNAs as markers for health conditions and diseases can be analyzed using the correlation analysis method of the present invention.

[0162] The results of this test example also demonstrate that the inhibitory nucleic acid molecules of the present invention can be used as tools for functional analysis of bacterial small RNAs, and that inhibitory nucleic acid molecules directed against bacterial small RNAs identified as causative agents of diseases can be used as agents for preventing or treating those diseases.

[0163] Furthermore, the results of this test example demonstrate that the design method of the present invention makes it possible to design useful inhibitory nucleic acid molecules such as those described above.

[0164] The present invention can be used as a diagnostic technique, or as a tool for research to develop diagnostic techniques or drug discovery research.

Claims

A method for analyzing samples prepared from blood specimens to detect small RNA molecules derived from bacteria. 　 The method according to claim 1, wherein the small RNA to be detected is an RNA fragment having a length of less than 200 nt, a sequence that completely matches a bacterial genome as a reference sequence, and a sequence that does not match a genome of the animal species to which the test animal from which the blood sample was obtained belongs as a reference sequence. 　 The method of claim 2, wherein the small RNA is a fragment of one or more types of RNA selected from tRNA, rRNA, RNase P RNA, tmRNA, SRP RNA, 6s RNA, RprA RNA, glmS ribozyme, group I intron, and RtT RNA. 　 The method according to claim 2, wherein the small RNA to be detected consists of a base sequence that completely matches a part of the base sequence represented by any one of SEQ ID NOs: 1 to 33. 　 the small RNA to be detected is one or more species selected from RNA fragments 1 to 53; The method according to claim 2, wherein each of the RNA fragments 1 to 53 is an RNA that consists of a base sequence that completely matches a portion of the base sequence of the molecular species shown on the same line in Table 1, which is possessed by the bacterial species shown on the same line, and has an overlapping region of 10% or more with the reference sequence specified by any of SEQ ID NOs: 34 to 86 shown on the same line. The method of claim 1, wherein the small RNA is encapsulated in a vesicle derived from a bacterium. 　 Detecting small RNAs derived from the bacteria by the method according to any one of claims 1 to 6, A small RNA derived from the bacterium detected from the sample; one or more conditions selected from specific diseases, disease risks, and health conditions; and analyzing the correlation between the 　 The method according to any one of claims 1 to 6, wherein a sample prepared from a blood specimen obtained from an animal to be tested is analyzed to detect small RNAs derived from bacteria in order to test for one or more conditions selected from a specific disease, a disease risk, and a health condition in the animal to be tested. 　 The method according to claim 8, wherein the state to be inspected and the small RNA to be detected have been confirmed to be correlated by the method according to claim 7. 　 The method of claim 8, wherein the condition to be examined is cancer or liver fibrosis. 　 An inhibitory nucleic acid molecule having a base sequence complementary to a small RNA derived from bacteria. 　 The inhibitory nucleic acid molecule described in claim 11, wherein the small RNA is an RNA fragment that is less than 200 nt and has a sequence that perfectly matches when the bacterial genome is used as a reference sequence, but does not match when the genome of the animal species to which the test animal from which the blood sample was obtained belongs is used as a reference sequence. 　 The inhibitory nucleic acid molecule of claim 12, wherein the small RNA is a fragment of one or more RNAs selected from tRNA, rRNA, Rnase P RNA, tmRNA, SRP RNA, 6s RNA, RprA RNA, glmS ribozyme, group I intron, and RtT RNA. 　 The inhibitory nucleic acid molecule of claim 12, wherein the small RNA comprises a base sequence that completely matches a portion of the base sequence represented by any one of SEQ ID NOs: 1 to 33. 　 the small RNA is one or more selected from RNA fragments 1 to 53; The inhibitory nucleic acid molecule of claim 12, wherein each of the RNA fragments 1 to 53 is an RNA having a base sequence that completely matches a portion of the base sequence of the molecular species shown in the same row in Table 1, which is possessed by the bacterial species shown in the same row, and which has an overlapping region of 10% or more with the reference sequence identified by any of SEQ ID NOs: 34 to 86 shown in the same row. The inhibitory nucleic acid molecule of claim 11 , wherein the small RNA is encapsulated in a vesicle derived from a bacterium. 　 A therapeutic or preventive agent for cancer or liver disease, comprising the inhibitory nucleic acid molecule according to any one of claims 11 to 16 as an active ingredient. 　 A method for designing an inhibitory nucleic acid molecule according to any one of claims 11 to 16, comprising identifying a base sequence complementary to the base sequence of the small RNA derived from the bacterium.

Images