Marker for identifying pathogenic microorganism type based on host reaction

Abstract

The invention relates to the technical field of biomedicine, in particular to a marker for identifying pathogenic microorganism types based on host reaction, which comprises a combination of coding RNA (Ribonucleic Acid) or proteins coded by the combination of the coding RNA: SLC37A3 and UBALD2. The biomarker can be used for diagnosing the types of pathogenic microorganisms and distinguishing bacterial or virus infection. Moreover, diagnosis can be carried out through the marker in the early stage of infection, and the marker has the advantages of being short in consumed time, high in specificity, high in sensitivity and the like.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a marker for identifying the type of pathogenic microorganisms based on host response. Background Art

[0002] At present, for patients suspected of infection, the clinician's prior knowledge and the patient's clinical manifestations are usually required to decide which pathogen type detection project to conduct. The conventional detection methods at the current clinical stage are mainly culture, microscopy, serology, molecular biology (such as PCR) and mass spectrometry. These methods have a narrow detection range and are time-consuming. In addition, some of these methods need to be based on culture methods, and some methods have low sensitivity in the early stages of infection. Therefore, the current methods have limitations in the early identification of pathogens, and it is impossible to distinguish pathogen types at the same time in one test.

[0003] Early and rapid identification of the type of pathogen that infects the patient is crucial for guiding clinical treatment and the rational use of anti-infective drugs. For example, patients with bloodstream infections and lower respiratory tract infections usually progress rapidly, seriously threatening the patient's life and health. For immunosuppressed patients, the diagnosis of infection is more complicated and difficult, and the mortality rate is also significantly increased. However, existing pathogen detection methods still have many shortcomings, such as low sensitivity, long experimental cycle, and narrow detection range, which make it difficult to effectively respond to the prevention and treatment needs of patients with complex and rapidly progressive bloodstream infections and severe lower respiratory tract infections. Summary of the invention

[0004] The purpose of the present invention is to provide a marker for identifying the type of pathogenic microorganisms based on host response. The biomarker can be used to diagnose the type of infectious pathogens and distinguish between bacterial and viral infections. In addition, the marker can be used for diagnosis in the early stages of infection, with the advantages of shorter time consumption, higher specificity, and higher sensitivity.

[0005] To this end, in a first aspect, the present invention provides a marker for diagnosing the type of pathogenic microorganisms, which comprises the following combination of encoding RNAs or proteins encoded by them: SLC37A3, UBALD2.

[0006] In some embodiments, the pathogens include bacteria and viruses.

[0007] The second aspect of the present invention provides the use of the marker in preparing a product for diagnosing the type of pathogenic microorganisms.

[0008] In some embodiments, the pathogens include bacteria and viruses; that is, by using the product, it is possible to diagnose or assist in diagnosing which of the following pathogens a subject is infected with: bacteria and viruses.

[0009] In some embodiments, the product includes a detection reagent for detecting the expression level of the marker; the expression level refers to the nucleic acid expression level and / or protein expression level of the marker.

[0010] In some embodiments, the detection reagent includes one or a combination of two or more selected from the following group: a mass spectrometry identification reagent, an antibody, a probe, or a primer that specifically binds to the marker.

[0011] In some embodiments, the antibody is a full-length antibody, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a double-chain Fv fragment, or a single-chain Fv fragment.

[0012] In some embodiments, the sample detected by the detection reagent is from a body fluid of a subject; for example, urine, blood, serum, plasma, saliva, lymph, cerebrospinal fluid, ascites, feces, bile, tissue fluid, etc.

[0013] In some embodiments, the sample is from the subject's blood.

[0014] In some embodiments, the subject is a mammal; eg, a human.

[0015] In some embodiments, the product is selected from the following group: reagents, test kits, test strips, gene chips, high-throughput sequencing platforms, antibody chips, and instrument platforms.

[0016] In some embodiments, the instrument platform comprises a measurement module for measuring the expression level of the marker in the sample to be tested.

[0017] The third aspect of the present invention provides a product for diagnosing the type of pathogenic microorganisms, the product comprising a detection reagent, the detection reagent detects the expression level of the marker described in the first aspect of the present invention.

[0018] In some embodiments, the detection reagent includes one or a combination of two or more selected from the following group: a mass spectrometry identification reagent, an antibody, a probe, or a primer that specifically binds to the marker.

[0019] In some embodiments, the antibody is a full-length antibody, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a double-chain Fv fragment, or a single-chain Fv fragment.

[0020] In some embodiments, the product is selected from the following group: reagents, test kits, test strips, gene chips, high-throughput sequencing platforms, antibody chips, and instrument platforms.

[0021] In some embodiments, the instrument platform comprises a measurement module for measuring the expression level of the marker in the sample to be tested.

[0022] A fourth aspect of the present invention provides a method for training a model for diagnosing the type of pathogenic microorganisms, comprising the following steps:

[0023] Step S1: obtaining expression level data of a marker in a sample; the marker is the marker described in the first aspect of the present invention;

[0024] Step S2: Using the expression level data of the marker obtained in step S1, the machine learning model is trained to obtain a model for diagnosing the type of pathogenic microorganism.

[0025] In some embodiments, the machine learning model is a binary logistic regression model.

[0026] In some embodiments, the step S2 further includes: validating the model for diagnosing the type of pathogenic microorganism.

[0027] In some embodiments, the model for diagnosing the type of pathogenic microorganism is validated by the Bootstrap method and the cross-validation method.

[0028] In some embodiments, the marker is screened by the following steps: extracting total RNA from the sample, constructing an lncRNA chain-specific transcriptome library and sequencing it to obtain transcriptome sequencing data; analyzing the transcriptome sequencing data and calculating the number of gene sequences (reads); then screening the differentially expressed genes (DEGs) of the bacterial infection group vs. the viral infection group through the R program package, and the screening criteria are P<0.05, |log2FoldChange|>1 (difference multiple); performing random forest analysis on the DEGs of this comparison group and calculating the feature importance (Importance); screening the top 100 features ranked by Importance, and then performing least absolute shrinkage and selection operator regression (LASSO) analysis to complete further screening; using the variable stepwise regression method, screening the minimum feature combination to complete the screening of marker combinations; screening to obtain the marker described in the first aspect of the present invention.

[0029] In some embodiments, the sample includes samples from the following subjects: patients with bacterial infections and patients with viral infections.

[0030] In some embodiments, the number of samples is at least 40, at least 60, or at least 80.

[0031] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0032] The present invention screens a group of gene markers by performing transcriptome sequencing analysis on clinical samples, and further constructs a disease diagnosis model based on this group of markers, which can be used for diagnosis or auxiliary diagnosis of pathogenic microorganism types (bacteria, viruses). The marker provided by the present invention is a combination of two genes, which has a more stable diagnostic performance than a single indicator. Moreover, the marker can be used for early diagnosis of infection, and also has the advantages of shorter time consumption, higher specificity and sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only used for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. In the accompanying drawings:

[0034] Figure 1 : In the discovery cohort, the ROC curve and classification performance of the model provided by the present invention for identifying bacterial or viral infections;

[0035] Figure 2 : In the discovery cohort, the Bootstrap method was used to verify the AUC distribution results of the model classification;

[0036] Figure 3 : In the validation cohort, the ROC curve and classification performance of the model provided by the present invention for identifying bacterial or viral infections. DETAILED DESCRIPTION

[0037] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0038] Unless otherwise specified, the terms used herein have the meanings commonly understood by those skilled in the art.

[0039] The relevant information of the markers involved in this article is as follows:

[0040] Table 1

[0041] Serial number Gene name NCBI database gene number 1 SLC37A3 ENSG00000157800 2 UBALD2 ENSG00000185262

[0042] Example 1 Case Collection

[0043] 1 mL of whole blood samples were collected from patients with suspected bloodstream infection at Peking University People's Hospital, and RNA preservation solution (ZYMO DNA / RNA shield) was added in a 1:1 ratio and then frozen at -80°C. The basic information, medical history, and medical records of the patients were reviewed and collected through the hospital's laboratory report and case reporting system. All information was desensitized, and the protocol was approved by the Ethics Committee of Peking University People's Hospital.

[0044] Case inclusion and exclusion criteria: follow-up of suspected infection patients with fever (body temperature ≥ 37°C), previous history of infection, endotracheal intubation or mechanical ventilation, abnormal imaging or elevated inflammatory indicators. Inclusion criteria: positive results of pathogen detection and exclusion of contamination; whole blood samples within three days before and after the pathogen detection; successful completion of RNA-seq and available data. Exclusion criteria: negative results of pathogen detection; positive pathogen detection but suspected contamination; no qualified whole blood samples; unsuccessful completion of RNA-seq and no available data. According to the type of pathogen infection, 81 patients with bacterial infection and 83 patients with viral infection were finally screened out.

[0045] Example 2

[0046] The clinical samples of Example 1 were divided into a discovery cohort (training set) and a validation cohort (test set) using a stratified sampling method, wherein the ratio of cases in the training set to the test set was 5: 5. The training set was used for feature screening and model construction, and the test set was used for model validation.

[0047] 1. Extraction of total RNA from whole blood samples: Take 2 mL of whole blood sample containing preservation solution from Example 1 and use ZYMO Quick-DNA / RNA TM Total RNA was extracted using Microprep Plus Kit. Nucleic acid quantification was performed using Vazyme Equalbit RNA HS Assay Kit and Thermo Fisher Scientific Qubit4.0. All RNA samples were frozen at -80°C until the construction of lncRNA strand-specific transcriptome libraries.

[0048] 2. Construction and sequencing of lncRNA strand-specific transcriptome library: (1) Use Vazyme UltraClean Ribo-off rRNA Depletion Kit (Human / Mouse / Rat) to remove ribosomal RNA (rRNA) from total RNA; (2) Use Vazyme UltraClean Universal V8 RNA-seq Library Prep Kit for Illumina to complete the construction of RNA sequencing library after rRNA removal; (3) Use Vazyme Equalbit DNA HS Assay Kit and Thermo Fisher Scientific Qubit4.0 to complete library quantification; (4) Use Illumina Hiseq platform for sequencing, paired-end sequencing, sequencing read length of 150bp, and obtain 12G data volume for each sample, which is the original transcriptome data.

[0049] 3. Bioinformatics analysis: The raw transcriptome data was subjected to linker removal, quality control, and filtering of low-quality sequences (reads) to obtain RNA-seq clean data. Bowtie2 software was used to align the GRCh38 human reference genome, and FeatureCounts was used to calculate the number of sequences for each gene. The measured gene reads were standardized and normalized. The R package EdgeR was used to screen the differentially expressed genes (DEGs) between the bacterial infection group and the viral infection group, with the screening criteria of P < 0.05 and |log2FoldChange|> 1 (difference fold).

[0050] 4. Machine learning feature screening and model construction: (1) Screen the DEGs of the bacterial infection group vs. the viral infection group, then perform random forest analysis on this comparison group and calculate the feature importance; (2) Screen the top 100 features ranked by importance, and perform LASSO analysis for further screening; (3) Use the stepwise regression method to screen the key features and complete the final screening by screening the minimum feature combination. The final number of combined features is 2, namely SLC37A3 and UBALD2; (4) Use the final feature combination and binary logistic regression model algorithm to build a diagnostic model, and verify the model performance by Bootstrap method and cross-validation method. The model function verification results are shown in the figure below. Figures 1 to 3 shown.

[0051] According to the validation results, the model provided by the present invention has good specificity and sensitivity.

[0052] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A marker for diagnosing the type of pathogenic microorganism, characterized in that: Includes the following group of coding RNA combinations or proteins encoded by them: SLC37A3, UBALD2.

2. Use of the marker according to claim 1 in the preparation of products for diagnosing the types of pathogenic microorganisms.

3. The use according to claim 2, characterized in that Types of pathogenic microorganisms include bacteria and viruses.

4. The use according to claim 2 or 3, characterized in that The product includes a detection reagent for detecting the expression level of the marker; the expression level refers to the nucleic acid expression level and / or protein expression level of the marker; Preferably, the detection reagent comprises one or a combination of two or more selected from the following groups: a mass spectrometry identification reagent, an antibody, a probe, or a primer that specifically binds to the marker; Preferably, the antibody is a full-length antibody, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a double-chain Fv fragment or a single-chain Fv fragment; Preferably, the sample detected by the detection reagent is from the body fluid of the subject; Preferably, the body fluid is selected from urine, blood, serum, plasma, saliva, lymph, cerebrospinal fluid, ascites, feces, bile, tissue fluid.

5. The use according to claim 2 or 3, characterized in that: The product is selected from the group consisting of reagents, test kits, test strips, gene chips, high-throughput sequencing platforms, antibody chips, and instrument platforms; Preferably, the instrument platform comprises a measurement module for measuring the expression level of the marker in the sample to be tested.

6. A product for diagnosing the type of pathogenic microorganisms, characterized in that: The product comprises a detection reagent, which detects the expression level of the marker of claim 1.

7. The product according to claim 6, characterized in that The detection reagent comprises one or a combination of two or more selected from the following groups: a mass spectrometry identification reagent, an antibody, a probe, or a primer that specifically binds to the marker; Preferably, the antibody is a full-length antibody, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a double-chain Fv fragment or a single-chain Fv fragment; Preferably, the product is selected from the group consisting of reagents, test kits, test strips, gene chips, high-throughput sequencing platforms, antibody chips, and instrument platforms; Preferably, the instrument platform comprises a measurement module for measuring the expression level of the marker in the sample to be tested.

8. A training method for a model for diagnosing pathogenic microorganism types, characterized in that: The following steps are involved: Step S1: obtaining expression level data of a marker in a sample; the marker is the marker described in the first aspect of the present invention; Step S2: Using the expression level data of the marker obtained in step S1, the machine learning model is trained to obtain a model for diagnosing the type of pathogenic microorganism.

9. The training method according to claim 8, characterized in that: The machine learning model is a binary logistic regression model; Preferably, after step S2, the method further includes: verifying the model for diagnosing the type of pathogenic microorganisms; Preferably, the model for diagnosing the type of pathogenic microorganisms is validated by the Bootstrap method and the cross-validation method.

10. The training method according to claim 8 or 9, characterized in that: The samples include samples from the following subjects: patients with bacterial infections and patients with viral infections; Preferably, the number of samples is at least 40, at least 60 or at least 80.

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