Method for providing information on Collinsella species present in the intestinal flora, and method for predicting the severity of COVID-19 and cytokine storm using said information

Abstract

To provide methods for providing information on the specific types of bacteria among the bacteria contained in the intestinal flora to use them as materials for judging treatment policies and the like; and to provide methods for predicting COVID-19 exacerbation and methods for predicting cytokine storm exacerbation using the information.SOLUTION: Provided is a method for providing information on the genus Collinsella in the intestinal flora, comprising: an analysis step of analyzing the type and amount of the bacteria contained in a stool sample, and an information provision step of providing information on the ratio of the genus Collinsella in the intestinal flora, COVID-19 exacerbation being predicted based on the information provided.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The disclosure of the present application relates to a method for providing information on the genus Collinsella present in the intestinal microbiota, a method for predicting the aggravation of COVID-19, and a method for predicting the aggravation of cytokine storm using the information. [Background technology]

[0002] A wide variety of bacteria live in the human intestine. Because the diverse population of bacteria resembles a flower garden, the bacterial flora living in the intestine is called the "intestinal flora." There are more than 1,000 types of intestinal flora, totaling approximately 100 trillion. Bacteria are said to have various functions, including energy production; metabolism of substances such as short-chain fatty acids, vitamins, and serotonin; immune regulation; and obesity prevention.

[0003] A method is known in which the intestinal flora is detected from differences in the sequence of the 16S rRNA gene (16S rDNA), and the dominant type of bacteria is identified by creating a database of the intestinal flora and comparing the multiple databases created (see Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6863633 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the method described in Patent Document 1 is an invention that provides a method for extracting bacterial groups related to health conditions from a database of intestinal microbiota. In clinical settings, it is preferable to provide information on specific types of bacteria among the more than 1,000 types and approximately 100 trillion bacteria that are said to be contained in the intestinal microbiota, so that it can be used as information for determining treatment plans, etc.

[0006] The disclosure of this application is an invention made to solve the above-mentioned conventional problems, and after intensive research, it has been newly discovered that information on the proportion of Collinsella bacteria among the bacteria contained in the intestinal flora is useful information when treating a subject's disease.

[0007] That is, the purpose of the disclosure in this application is to provide a method for providing information on the Collinsella genus present in the intestinal bacterial flora, and a method for predicting the aggravation of COVID-19 and a method for predicting the aggravation of cytokine storm using said information. [Means for solving the problem]

[0008] The disclosure of the present application relates to a method for providing information on the genus Collinsella present in the intestinal microbiota, a method for predicting the aggravation of COVID-19, and a method for predicting the aggravation of cytokine storm, which are shown below.

[0009] (1) A method for providing information on the genus Collinsella present in the intestinal flora, comprising: The method comprises: an analysis step of analyzing the type and amount of bacteria contained in the fecal sample; An information providing step of providing information on the proportion of Collinsella genus in the intestinal flora; A method comprising: (2) A method for predicting the severity of COVID-19 in a subject based on information provided by the method described in (1) above. (3) A method for predicting the severity of COVID-19 described in (2) above, which predicts that the lower the proportion of Collinsella in the intestinal flora, the more severe the COVID-19 will be. (4) A method for predicting the aggravation of a cytokine storm, which predicts the aggravation of a cytokine storm in a subject based on information provided by the method described in (1) above. (5) The method for predicting the aggravation of a cytokine storm described in (4) above, which predicts that the cytokine storm will become more severe as the proportion of Collinsella in the intestinal flora decreases. [Effects of the Invention]

[0010] Information on the proportion of Collinsella obtained by analyzing a subject's fecal sample is useful in treating the subject's disease. DETAILED DESCRIPTION OF THE INVENTION

[0011] Below, we will explain in detail how to provide information on the Collinsella genus (hereinafter simply referred to as "Collinsella") that occupies the intestinal microbiota, and how to use this information to predict the severity of COVID-19 and the severity of cytokine storm.

[0012] (Embodiment of a method for providing information on Collinsella in the intestinal flora) An embodiment of a method for providing information on the proportion of Collinsella in the intestinal flora (hereinafter, sometimes simply referred to as an "information providing method") will be described. The information providing method includes an analysis step of analyzing the type and amount of bacteria contained in a fecal sample, and an information providing step of providing information on the proportion of Collinsella in the intestinal flora.

[0013] A fecal sample may be collected from a subject by a known method. The type and amount of bacteria contained in the intestinal microbiota may be analyzed by a known method, such as 16S rRNA gene (16S rDNA) analysis. Information on the proportion of Collinsella in the intestinal microbiota obtained from the analysis results in the analysis step can be used for the purposes described below.

[0014] (Prediction method for COVID-19 aggravation and cytokine storm aggravation) As described in the Examples below, the information provided by the information provision method correlates with the worsening of COVID-19. Therefore, information on the proportion of Collinsella in the intestinal microbiota can be used to predict whether a subject will develop severe symptoms when infected with COVID-19. In other words, the information provided by the information provision method can be used in a method for predicting the worsening of COVID-19 in a subject. When predicting the worsening of COVID-19, the lower the proportion of Collinsella in the intestinal microbiota, the more severe the COVID-19 will be. Based on statistical data, a threshold for the proportion of Collinsella in the intestinal microbiota can be determined, and if the proportion is lower than the threshold, the COVID-19 will be predicted to worsen, while if it is higher than the threshold, the COVID-19 will not be predicted to worsen. Predicting whether a subject will develop severe symptoms when infected with COVID-19, or whether a subject already infected with COVID-19 will develop severe symptoms, can help doctors plan treatment strategies, secure hospital beds, and so on.

[0015] The mechanism by which a lower proportion of Collinsella in the intestinal flora leads to more severe COVID-19 is unclear, but the following speculation can be made.

[0016] Collinsella converts primary bile acids that reach the large intestine without being returned to the liver from the terminal ileum into secondary bile acids according to the following scheme: This conversion to secondary bile acids produces ursodeoxycholic acid (UDCA). JPEG0007783588000001.jpg39144

[0017] Analysis of the anti-COVID-19 components of bindweed extracts has shown through docking simulation that ursodeoxycholic acid (UDCA) inhibits the binding of the SARS-CoV-2 spike region and angiotensin converting enzyme 2 (ACE2) (Poochi SP et al., “Employing bioactive compounds derived from Ipomoea obscura (L.) to evaluate potential inhibitor for SARS-CoV-2 main protease and ACE2 protein”, Food Frontiers. 2020;1:168-179.)

[0018] In addition to the above simulations, cell-free experiments using recombinant proteins have shown that UDCA inhibits the binding of the spike region to ACE2 in a concentration-dependent manner (Adriana Carino1 et al., “Hijacking SARS-CoV-2 / ACE2 Receptor Interaction by Natural and Semi-synthetic Steroidal Agents Acting on Functional Pockets on the Receptor Binding Domain”, Frontiers in Chemistry, October 2020, Volume 8, Article 572885).

[0019] Furthermore, in a rat spinal cord injury model, UDCA is known to suppress the production of inflammatory cytokines such as TNF-α, IL-1β, IL-2, IL-4, and IL-6 (Wan-Kyu Ko et al., "Ursodeoxycholic Acid Inhibits Inflammatory Responses and Promotes Functional Recovery After Spinal Cord Injury in Rats", Mol Neurobiol, 2019, 56:267-277).

[0020] UDCA acts as a potent radical scavenger, exerting antioxidant and anti-apoptotic effects. Therefore, it is speculated that UDCA suppresses the cytokine storm that causes multiple organ failure in COVID-19. Therefore, it is speculated that the lower the proportion of Collinsella in the gut microbiota, the lower the amount of UDCA produced, resulting in the worsening of COVID-19.

[0021] UDCA also increases alveolar fluid clearance in acute respiratory distress syndrome (ARDS) via the ALX / cAMP / PI3K pathway. UDCA is approved by the FDA in both the US and Japan for various liver diseases, including cirrhosis, and is widely used as a safe drug with no serious side effects. UDCA is expected to be clinically applicable to prevent SARS-CoV-2 infection and suppress the progression of COVID-19.

[0022] As mentioned above, UDCA is presumed to suppress cytokine storm. Incidentally, cytokine storms are not limited to COVID-19, but can also occur due to viral infections, bacterial infections, hemophagocytic lymphohistiocytosis, graft-versus-host disease, and iatrogenic diseases (CAR-T therapy, blinatumomab) (Fajgenbaum DC et al., "Cytokine Storm," N. Engl J Med 2020, 383:2255-2273). When cytokine storms occur, excessive fever, fatigue, and coagulation abnormalities occur, leading to deterioration of overall condition and thrombus formation. Therefore, the information provision method disclosed in this application is not limited to predicting the severity of COVID-19, but can also be used for predicting the severity of cytokine storms. Similar to predicting the severity of COVID-19, it can be predicted that the lower the proportion of Collinsella in the intestinal microbiota, the more severe the cytokine storm will be.

[0023] The following examples are provided to specifically explain the embodiments disclosed in the present application, but these examples are merely for the purpose of explaining the embodiments and are not intended to limit or restrict the scope of the invention disclosed in the present application. [Example]

[0024] Example 1 By analyzing the intestinal microbiota using the following procedure, we identified bacteria that could be used as indicators for predicting the severity of COVID-19.

[0025] 〔sample〕 We used 16SrRNAV3-V5 sequence data from 953 healthy individuals from the 10 countries shown in Table 1 below. The data were obtained from publicly available information (https: / / www.ncbi.nlm.nih.gov / geo / ). The 10 countries were selected from the Organization for Economic Cooperation and Development (OECD) to reduce the influence of geopolitical factors. Table 1 also shows the COVID-19 mortality rate per million people, average age, and gender ratio. Note that the COVID-19 mortality rate per million people is from Our World in Data (https: / / ourworldindata.org / ) as of February 9, 2021, before widespread vaccination in these 10 countries. Because the average gut microbiota is similar across countries between the ages of 20 and 70, we did not filter the dataset by age or gender. The data from 137 healthy Japanese individuals was obtained and analyzed by the inventors (Nishiwaki H., et al., "Short-Chain Fatty Acid-Producing Gut Microbiota Is Decreased in Parkinson's Disease but Not in Rapid-Eye-Movement Sleep Behavior Disorder", mSystems 2020, 5:e00797-20).

[0026] [Table 1]

[0027] [Data Analysis] Raw 16S rRNA sequencing datasets from 953 healthy individuals from 10 countries were labeled with COVID-19 mortality rates for each country and pooled into a single dataset. The datasets were analyzed according to the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines.

[0028] [Generalized Linear Model (GLM) analysis] First, we analyzed the relative abundance of each gut bacteria at the genus level using 16SrRNAV3-V5 sequence data from 953 healthy individuals from 10 countries. We then used GLM to examine the correlation between COVID-19 mortality rates and genus-level bacteria in the 10 countries. In the GLM analysis, we compared Gaussian, gamma, and inverse Gaussian distributions and found that the gamma distribution yielded the lowest Akaike's Information Criterion (AIC) (Gaussian, 14599; gamma, 14573; and inverse Gaussian, 15318). The results of the GLM analysis using the gamma distribution are shown in Table 2. Collinsella confirmed that a lower proportion of gut microbiota was associated with a higher COVID-19 mortality rate. Furthermore, the p-value for Collinsella was significantly low at 1.58E-15. Regarding the "Positive or negative effect" in Table 2, the fewer "-"s there are, the higher the COVID-19 mortality rate, and the more "+"s there are, the higher the COVID-19 mortality rate.

[0029] [Table 2]

[0030] Factors known to contribute to the severity of COVID-19 include old age, obesity, smoking history, history of respiratory infections, and diabetes. Although the prevalence of these factors does not vary significantly between countries, COVID-19 mortality rates vary significantly. Therefore, the existence of Factor X, which determines mortality rates, has been proposed, but its exact nature remains unclear. As shown in Table 2, the proportion of Collinsella bacteria in the gut microbiota of healthy individuals is less than 1% on average across 10 countries, but in many countries, they are among the top ten bacteria in the gut microbiota. The significantly low p-value for Collinsella suggests that it may be Factor X.

[0031] These results confirm that information on the proportion of Collinsella in a subject's gut microbiota is extremely useful for predicting the severity of COVID-19. Being able to predict whether a COVID-19 patient will develop severe symptoms in the future will enable appropriate allocation of medical resources and is expected to reduce the number of cases where patients end up in a tragic state while recovering at home due to unexpected worsening of symptoms. [Industrial Applicability]

[0032] The disclosure of this application provides information for predicting the progression of COVID-19 and cytokine storm, which is useful for testing and researching COVID-19 patients in medical institutions, university medical schools, and other research institutions.

Claims

1. A method for assisting in prediction of the severity of COVID-19, for predicting the severity of COVID-19 in a subject, based on information on the proportion of Collinsella in the intestinal flora of the subject provided by a method for providing information on the proportion of Collinsella in the intestinal flora, comprising: The method for providing information on the proportion of Collinsella genus in the intestinal microbiota includes: an analysis step of analyzing the type and amount of bacteria contained in the fecal sample; An information providing step of providing information on the proportion of Collinsella genus in the intestinal flora; Including, A method to assist in predicting the severity of COVID-19.

2. The method for assisting in prediction of the severity of COVID-19 according to claim 1, wherein the lower the proportion of Collinsella genus in the intestinal flora, the more severe COVID-19 is predicted to become.

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