Respiratory flora marker for assisting in predicting chlamydia psittaci infection and application of respiratory flora marker

Abstract

The invention relates to the technical field of pathogenic microorganism infection detection, and provides a respiratory flora marker for assisting in predicting chlamydia psittaci infection and application of the respiratory flora marker. The respiratory tract flora markers comprise Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus, and the respiratory tract flora markers comprise Candida glabrata, Lactobacillus paracasei, Lactobacillus rhamnosus, Lactobacillus paracasei, Lactobacillus rhamnosus, Lactobacillus paracasei, Lactobacillus rhamnosus, Lactobacillus paracasei, Lactobacillus paracasei, and Lactobacillus rhamnosus. The respiratory flora marker for aided prediction of chlamydia psittaci infection is applied to preparation of a kit for aided diagnosis of severe pneumonia caused by chlamydia psittaci infection, has high sensitivity and high specificity, reduces misdiagnosis and missed diagnosis, can effectively evaluate the risk of severe illness of a patient suffering from chlamydia psittaci infection, and has a good application prospect in the treatment of severe pneumonia caused by chlamydia psittaci infection. Therefore, medical intervention can be carried out as soon as possible, and great clinical application value is achieved.

Description

Technical Field

[0001] This invention belongs to the field of pathogenic microorganism infection detection technology, and in particular relates to respiratory flora markers for assisting in the prediction of Chlamydia psittaci infection and their applications. Background Technology

[0002] Chlamydia psittaci (Cps) is an obligate intracellular parasitic, multi-host zoonotic pathogen that can cause widespread infection in humans and animals. Studies have shown that animals infected with Chlamydia psittaci have a high mortality rate, causing severe economic losses to the livestock industry. Infection with Chlamydia psittaci can cause severe immunosuppression and spread through the bloodstream to the reticuloendothelial system. The lungs are the primary affected organ, but other parts of the body, such as the liver, spleen, and central nervous system, may also be affected to varying degrees. Severe cases of Chlamydia psittaci infection manifest as multiple organ failure and secondary infections by other pathogens. Currently, there are no reliable biomarkers to predict disease progression; clinical treatment relies on lagging indicators such as oxygenation index, inflammatory markers (e.g., IL-6, CRP), and radiographic changes, which often miss the early intervention window.

[0003] Recent studies have shown that the composition of the respiratory microbiota is closely related to the severity of lung infections, and changes in the respiratory microbiota can predict disease progression and prognosis. With the development of metagenomic sequencing technology, it is now possible not only to identify *Chlamydia psittaci* infection but also to analyze the differences in lower respiratory tract microbiota structure between severely and non-severely infected patients. Although non-invasive detection methods based on metagenomic sequencing technology show great application potential, clinically useful microbial biomarkers for assessing the risk of severe *Chlamydia psittaci* infection are still lacking. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides respiratory microbiota markers for assisting in the prediction of Chlamydia psittaci infection and their applications, with the aim of solving the problems mentioned in the background art.

[0005] In a first aspect, the present invention provides respiratory microbiota markers to aid in the prediction of Chlamydia psittaci infection, wherein the respiratory microbiota markers are Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus.

[0006] Furthermore, the application of respiratory flora markers that aid in predicting Chlamydia psittaci infection in the preparation of a kit to aid in the diagnosis of severe pneumonia caused by Chlamydia psittaci infection.

[0007] Furthermore, the kit detects the relative abundance of the respiratory flora markers in the sample of the examinee to assist in the diagnosis of whether the examinee is a patient with severe pneumonia caused by Chlamydia psittaci infection.

[0008] Furthermore, the kit includes reagents for detecting the relative abundance of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus.

[0009] Furthermore, the subjects were patients with severe pneumonia caused by Chlamydia psittaci infection and patients with mild pneumonia caused by Chlamydia psittaci infection.

[0010] Furthermore, the sample was bronchoalveolar lavage fluid.

[0011] Furthermore, the relative abundance of the respiratory microbiota markers was obtained by metagenomic sequencing.

[0012] Furthermore, the method for obtaining the relative abundance of the respiratory microbiota markers specifically involves: separating and extracting nucleic acids from the bronchoalveolar lavage fluid of the subject, constructing a library, performing sequencing, performing bioinformatics analysis on the sequencing data to obtain the sequence number of the respiratory microbiota markers, standardizing the sequence number, and obtaining the relative abundance of the respiratory microbiota markers.

[0013] Secondly, a computer program for diagnosing severe pneumonia caused by Chlamydia psittaci infection, wherein when the computer program is executed, the following specific steps are performed: The relative abundance values ​​of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus in the bronchoalveolar lavage fluid samples of the test subjects were obtained, and the logarithm y of the dominance of the test subjects was calculated according to the binary logistic regression equation. The probability Q of a subject being a patient with severe pneumonia caused by Chlamydia psittaci infection is calculated based on the logarithm y. The formula is: Q = exp(y) / {1 + exp(y)}. In the formula, exp(y) is the natural exponential function of y; Based on the comparison of probability Q with the reference value, the risk of the subject having severe pneumonia caused by Chlamydia psittaci infection can be diagnosed. When the probability Q is greater than 0.5, it indicates that the subject is highly likely to be a patient with severe pneumonia caused by Chlamydia psittaci infection; when the probability Q is less than 0.5, it indicates that the subject is lowly likely to be a patient with severe pneumonia caused by Chlamydia psittaci infection.

[0014] Furthermore, the logarithm y of the subject's dominance is calculated based on the binary logistic regression equation, expressed by the formula: y=-8.947+1226.328*x1+1557.206*x2+5293.928*x3; In the formula, x1, x2, and x3 are the relative abundance values ​​of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus, respectively.

[0015] The present invention has the following technical effects: the respiratory flora markers for assisting in the prediction of Chlamydia psittaci infection are Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus. These respiratory flora markers are used in the preparation of a kit for assisting in the diagnosis of severe pneumonia caused by Chlamydia psittaci infection, and have high sensitivity and high specificity (AUC of ROC curve = 0.990), reducing misdiagnosis and missed diagnosis. They can effectively assess the risk of severe disease in patients with Chlamydia psittaci infection, thereby enabling early medical intervention and have great clinical application value. Attached Figure Description

[0016] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1 This is a graph showing the α-diversity analysis results of the lower respiratory tract flora species composition in the patients with severe pneumonia caused by Chlamydia psittaci and the patients with mild pneumonia caused by Chlamydia psittaci in Example 2 of the present invention. ** indicates p < 0.01, *** indicates p < 0.001, and **** indicates p < 0.0001.

[0017] Figure 2 This is a graph showing the β-diversity analysis results of the lower respiratory tract flora species composition in the patients with severe pneumonia caused by Chlamydia psittaci and the patients with mild pneumonia caused by Chlamydia psittaci in Example 2 of the present invention.

[0018] Figure 3 This is a graph showing the ROC analysis results of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus, and combinations of at least two of these bacterial strains, as described in Example 2 of this invention. Detailed Implementation

[0019] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0021] This invention provides respiratory microbiota markers to aid in the prediction of Chlamydia psittaci infection, namely Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus.

[0022] In some embodiments, respiratory microbiota markers that aid in predicting Chlamydia psittaci infection are used in the preparation of a kit to aid in the diagnosis of severe pneumonia caused by Chlamydia psittaci infection.

[0023] In some embodiments, the kit detects the relative abundance of respiratory flora markers in the sample of the subject to aid in the diagnosis of whether the subject is a patient with severe pneumonia caused by Chlamydia psittaci infection.

[0024] In some embodiments, the kit includes reagents for detecting the relative abundance of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus.

[0025] In some embodiments, the subjects are patients with severe pneumonia caused by Chlamydia psittaci infection and patients with mild pneumonia caused by Chlamydia psittaci infection.

[0026] In some embodiments, the sample is bronchoalveolar lavage fluid.

[0027] In some embodiments, the relative abundance of respiratory microbiota markers is obtained by metagenomic sequencing.

[0028] In some embodiments, the method for obtaining the relative abundance of respiratory microbiota markers specifically includes: isolating and extracting nucleic acids from the sample of the subject, constructing a library, performing sequencing, performing bioinformatics analysis on the sequencing data, obtaining the sequence number of respiratory microbiota markers, standardizing the sequence number, and obtaining the relative abundance of respiratory microbiota markers.

[0029] In some embodiments, this invention provides a computer program for diagnosing severe pneumonia caused by Chlamydia psittaci infection. When the computer program is executed, it performs the following specific steps: The relative abundance values ​​of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus in the bronchoalveolar lavage fluid samples of the test subjects were obtained, and the logarithm y of the dominance of the test subjects was calculated according to the binary logistic regression equation. The probability Q of a subject being a patient with severe pneumonia caused by Chlamydia psittaci infection is calculated based on the logarithm y. The formula is: Q = exp(y) / {1 + exp(y)}. In the formula, exp(y) is the natural exponential function of y; Based on the comparison of probability Q with the reference value, the risk of the subject having severe pneumonia caused by Chlamydia psittaci infection can be diagnosed. When the probability Q is greater than 0.5, it indicates that the subject is highly likely to be a patient with severe pneumonia caused by Chlamydia psittaci infection; when the probability Q is less than 0.5, it indicates that the subject is lowly likely to be a patient with severe pneumonia caused by Chlamydia psittaci infection.

[0030] In some embodiments, the logarithm y of the strength of the test object is calculated based on the binary logistic regression equation, expressed by the formula: y=-8.947+1226.328*x1+1557.206*x2+5293.928*x3; In the formula, x1, x2, and x3 are the relative abundance values ​​of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus, respectively.

[0031] Example 1: 1. Inclusion and exclusion criteria for the study cohort population Inclusion criteria for patients with severe pneumonia caused by Chlamydia psittaci infection: (1) Meets the diagnostic criteria for community-acquired pneumonia in Chinese adults; (2) Specific Chlamydia psittaci gene fragments are detected by metagenomic sequencing; (3) Patients with pneumonia who present with one or more of the following signs can be diagnosed with severe pneumonia: ① altered mental status; ② respiratory rate > 30 breaths / min; ③ PaO2 < 60 mmHg; PaO2 / FiO2 < 30, requiring mechanical ventilation; ④ systolic blood pressure < 90 mmHg; ⑤ septic shock; ⑥ chest X-ray shows bilateral or multi-lobular involvement, or lesion expansion ≥ 50% within 48 hours of admission; ⑦ oliguria: urine output < 20 mL / h, or < 80 mL / 4h, or acute renal failure requiring dialysis.

[0032] The control group consisted of patients with mild pneumonia caused by Chlamydia psittaci infection.

[0033] 2. Sample Collection According to the above inclusion criteria, bronchoalveolar lavage fluid samples were collected from 33 patients with severe pneumonia caused by Chlamydia psittaci and 30 patients with mild pneumonia caused by Chlamydia psittaci. The study has been approved by the hospital ethics committee, with ethics number (2024)CDYFYYLK(11-007).

[0034] 3. Metagenomic sequencing Microbial genomic DNA was extracted from bronchoalveolar lavage fluid samples using magnetic bead extraction. The extracted genomic DNA underwent quality control by measuring nucleic acid concentration and purity. After meeting the testing requirements, the DNA was fragmented, amplified, end-repaired, ligated with A bases, and adapters and indexes were added to obtain a library. After purification and quality control, the library was sequenced using a BGI MGISEQ-2000 sequencer according to the manufacturer's manual to obtain sequencing data. Bioinformatics analysis was performed on the sequencing data. Raw sequencing reads were processed using quality control to remove short (<35bp) and low-quality reads. Host sequences were filtered out using Bowtie2 (V.2.3.5.1) software based on alignment with the NCBI human database. The remaining reads were aligned and classified against microbial databases using Kraken2 (2.1.2) and Bowtie2 software. Species annotation was performed using Kraken2's default parameters. Simultaneously, the species-level abundance of the metagenomic samples was estimated using Bayesian algorithms and Kraken classification results via Bracken.

[0035] Example 2: 1. Analyze the alpha diversity of species among groups. Alpha diversity analysis was used to analyze the microbial community diversity in bronchoalveolar lavage fluid samples from 33 patients with severe pneumonia caused by Chlamydia psittaci and 30 patients with mild pneumonia caused by Chlamydia psittaci. Single-sample diversity analysis (Alpha diversity) reflects the richness and diversity of the microbial community in the two groups. ACE, Chao1, Shannon, and Simpson indices were used to analyze whether there were differences in alpha diversity at the species level between the two groups.

[0036] Using Wilcoxon's method, the alpha diversity analysis results of the lower respiratory tract microbiota species composition in patients with severe pneumonia caused by Chlamydia psittaci infection and patients with mild pneumonia caused by Chlamydia psittaci infection were as follows: Figure 1 As shown in the figure, the results indicate that there is a statistically significant difference in the α-diversity of the two groups of lower respiratory tract microbiota.

[0037] 2. Analyze the β-diversity of species among groups. Beta diversity analysis was used to investigate the differences in microbial communities between bronchoalveolar lavage fluid samples from 33 patients with severe pneumonia caused by Chlamydia psittaci and 30 patients with mild pneumonia caused by Chlamydia psittaci. A non-linear model based on Bray-Curtis distance, using non-metric multidimensional scaling (NMDS), was employed to analyze the microbial communities. The species information contained in the samples was represented as points on a two-dimensional plane. NMDS analysis, by representing the species information in both groups of samples as points in a multidimensional space, allows the degree of difference between samples to be reflected by the distance between these points, thus reflecting the inter-group species differences.

[0038] The results of the β-diversity analysis of the lower respiratory tract flora species composition in patients with severe pneumonia caused by Chlamydia psittaci infection and patients with mild pneumonia caused by Chlamydia psittaci infection are as follows: Figure 2 As shown in the figure, the results indicate that there are significant differences in the β-diversity of the two groups of lower respiratory tract microbiota.

[0039] 3. Screening of bacterial strains related to Chlamydia psittaci infection Candidate marker strains were screened based on their relative abundance. The screening criteria were as follows: patients with mild psittacosis-infected pneumonia were used as the control group, and patients with severe psittacosis-infected pneumonia had a fold change > 2 for strain abundance, indicating upregulation. In other words, strains with a significant difference (P < 0.05) in strain abundance between the patients with severe psittacosis-infected pneumonia and the control group, and a fold change greater than 2, were considered differentially expressed strains. The results of the differentially expressed strain screening are shown in Table 1. A total of three differentially expressed strains were found: Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus.

[0040] Table 1 Results of differential bacterial strain screening

[0041] 4. ROC analysis of candidate bacterial biomarkers ROC analysis was used to validate the function of candidate biomarkers. The ROC curve is plotted with the true positive rate (sensitivity) on the ordinate and the false positive rate (1-specificity) on the x-axis. Each point corresponds to a cutoff point in the diagnostic test; connecting these possible points creates the ROC curve. The integral of the ROC curve is the AUC (Area Under the Curve). Table 2 shows the ROC diagnostic curve results for the three-bacterial combination and single-bacterial prediction scoring methods. The results show that combining *Candida glabrata*, *Lactobacillus paracasei*, and *Lactobacillus rhamnosus* resulted in different AUC values ​​for different combinations of bacteria; the more bacteria in the combination, the higher the AUC value.

[0042] ROC analysis results of *Candida glabrata*, *Lactobacillus paracasei*, and *Lactobacillus rhamnosus*, and combinations of at least two of these strains, are as follows: Figure 3 As shown in the figure. The results showed that Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus, as respiratory flora markers, have high reliability in assisting the diagnosis of severe pneumonia caused by Chlamydia psittaci infection.

[0043] Table 2 ROC diagnostic curve results

[0044] 5. Diagnostic / predictive models for candidate bacterial biomarkers A computer program is constructed for diagnosing or determining whether a patient has severe pneumonia caused by Chlamydia psittaci infection. The specific steps for executing the computer program are as follows: The relative abundance values ​​of *Candida glabrata*, *Lactobacillus paracasei*, and *Lactobacillus rhamnosus* were obtained in bronchoalveolar lavage fluid samples from subjects (including severe / mild pneumonia caused by *Chlamydia psittaci* infection). The logarithm y of the dominance of the subjects was calculated using a binary logistic regression equation, expressed by the following formula: y=-8.947+1226.328*x1+1557.206*x2+5293.928*x3; In the formula, x1, x2 and x3 are the relative abundance values ​​of Candida glabrata, Lactobacillus paracasei and Lactobacillus rhamnosus, respectively. The probability Q of a subject being a patient with severe pneumonia caused by Chlamydia psittaci infection is calculated based on the logarithm y. The formula is: Q = exp(y) / {1 + exp(y)}. In the formula, exp(y) is the natural exponential function of y; Based on the comparison of probability Q with the reference value, the risk of the subject having severe pneumonia caused by Chlamydia psittaci infection can be diagnosed or predicted. When the probability Q is greater than 0.5, it indicates that the subject is highly likely to be a patient with severe pneumonia caused by Chlamydia psittaci infection; when the probability Q is less than 0.5, it indicates that the subject is lowly likely to be a patient with severe pneumonia caused by Chlamydia psittaci infection.

[0045] In summary, the respiratory microbiota markers that can help predict Chlamydia psittaci infection are Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus. These markers, when used in the preparation of a diagnostic kit for severe pneumonia caused by Chlamydia psittaci infection, exhibit high sensitivity and specificity (AUC = 0.990), an optimal cutoff value of 0.7389173, a sensitivity of 0.939, and a specificity of 1.000. They reduce misdiagnosis and missed diagnosis, effectively assess the risk of severe illness in patients with Chlamydia psittaci infection, and enable early medical intervention, demonstrating significant clinical value.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A respiratory flora marker to aid in the prediction of Chlamydia psittaci infection, characterized in that: The respiratory flora markers are Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus.

2. The application of the respiratory flora markers for assisting in the prediction of Chlamydia psittaci infection as described in claim 1 in the preparation of a kit for assisting in the diagnosis of severe pneumonia caused by Chlamydia psittaci infection.

3. The application as described in claim 2, characterized in that: The kit detects the relative abundance of the respiratory flora markers in the sample of the subject, and helps to diagnose whether the subject is a patient with severe pneumonia caused by Chlamydia psittaci infection.

4. The application as described in claim 3, characterized in that: The kit includes reagents for detecting the relative abundance of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus.

5. The application as described in claim 4, characterized in that: The subjects were patients with severe pneumonia caused by Chlamydia psittaci infection and patients with mild pneumonia caused by Chlamydia psittaci infection.

6. The application as described in claim 5, characterized in that: The sample was bronchoalveolar lavage fluid.

7. The application as described in claim 6, characterized in that: The relative abundance of the respiratory microbiota markers was obtained by metagenomic sequencing.

8. The application as described in claim 7, characterized in that: The method for obtaining the relative abundance of the respiratory microbiota markers is as follows: nucleic acids are isolated and extracted from the bronchoalveolar lavage fluid of the subject, a library is constructed, sequencing is performed, bioinformatics analysis is conducted on the sequencing data to obtain the sequence number of the respiratory microbiota markers, the sequence number is standardized, and the relative abundance of the respiratory microbiota markers is obtained.

9. A computer program for diagnosing severe pneumonia caused by Chlamydia psittaci infection, characterized in that: When a computer program is executed, it performs the following specific steps: The relative abundance values ​​of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus in the bronchoalveolar lavage fluid samples of the test subjects were obtained, and the logarithm y of the dominance of the test subjects was calculated according to the binary logistic regression equation. The probability Q of a subject being a patient with severe pneumonia caused by Chlamydia psittaci infection is calculated based on the logarithm y. The formula is: Q = exp(y) / {1 + exp(y)}. In the formula, exp(y) is the natural exponential function of y; Based on the comparison of probability Q with the reference value, the risk of the subject having severe pneumonia caused by Chlamydia psittaci infection can be diagnosed. When the probability Q is greater than 0.5, it indicates that the subject of the test is highly likely to be a patient with severe pneumonia caused by Chlamydia psittaci infection. When the probability Q is less than 0.5, it indicates that the probability of the subject being tested being a patient with severe pneumonia caused by Chlamydia psittaci infection is low.

10. A computer program for diagnosing severe pneumonia caused by Chlamydia psittaci infection as described in claim 9, characterized in that: The logarithm y of the subject's dominance is calculated using the binary logistic regression equation, expressed by the following formula: y=-8.947+1226.328*x1+1557.206*x2+5293.928*x3; In the formula, x1, x2, and x3 are the relative abundance values ​​of Candida glabrata, Lactobacillus paracasei, and Lactobacillus rhamnosus, respectively.

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