Application of gene marker for risk assessment of active tuberculosis of HIV infected person

Abstract

The invention relates to the technical field of biomedicine, in particular to application of a gene marker for risk assessment of active tuberculosis of an HIV infected person. According to the application of the provided quantitative detection reagent of the gene marker in preparation of a product for evaluating the risk of the HIV infected person suffering from the active tuberculosis, the gene marker shown as Seq.No1-6 comprises one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2, and the gene marker shown as Seq.No1-6 comprises one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2. According to the application, the problem of identifying active tuberculosis high-risk groups in HIV infected persons is solved from the gene level, the disease progress trend can be captured earlier and more accurately from the gene level, and a foundation is laid for targeted implementation of tuberculosis preventive treatment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biomedical technology, and particularly relates to the application of gene markers for risk assessment of active tuberculosis in HIV infected persons. BACKGROUND

[0002] Tuberculosis (TB) is a chronic infectious disease caused by infection of Mycobacterium tuberculosis (Mtb), which is still one of the diseases with the highest mortality rate caused by a single pathogen worldwide. Especially in human immunodeficiency virus (HIV) infected population, due to the significant impairment of their immune function, the risk of developing active tuberculosis in this population is about 20 to 30 times higher than that in healthy population without HIV infection. Active tuberculosis has become the most common opportunistic infection type in HIV infected persons, and is also one of the main causes of death in HIV infected persons. Co-infection of HIV and TB not only aggravates the severity of the disease in individual patients, but also further increases the risk of disease transmission, and effective intervention measures are urgently needed to reduce the risk of tuberculosis in HIV infected persons.

[0003] To address the above problems, the World Health Organization (WHO) has clearly recommended that all HIV infected persons receive tuberculosis preventive treatment (TPT) in order to reduce the occurrence of active tuberculosis through early intervention. However, the existing TPT scheme faces many insurmountable challenges in actual clinical application, which seriously restricts the prevention effect of the scheme. On the one hand, the drugs used in the existing TPT scheme generally have strong side effects, and the treatment course is long, resulting in poor compliance of patients during treatment. Many patients cannot complete the entire treatment cycle as required, which makes the treatment effect unsatisfactory and difficult to maintain the preventive effect of tuberculosis for a long time. On the other hand, in areas with limited medical resources and insufficient diagnostic conditions, due to the lack of precise screening methods, universal TPT is often implemented for HIV infected persons who have not been bacteriologically diagnosed. This treatment mode not only cannot achieve precise prevention and control, but also may expose some patients to unnecessary treatment drugs, increase the probability of drug-related adverse events, and even increase the risk of death in extreme cases. The limitations of the existing TPT scheme have become a key factor hindering the progress of tuberculosis prevention and control work in HIV infected persons.

[0004] In current clinical practice, the core bottleneck hindering the precise implementation of tuberculosis preventive treatment (TPT) lies in the lack of a reliable prospective predictive tool to accurately identify high-risk individuals among HIV-infected individuals who may progress to active tuberculosis. This technological gap directly prevents TPT from achieving "targeted intervention," forcing medical resources to be evenly distributed among all HIV-infected individuals, resulting in inefficient resource allocation. This not only fails to ensure that high-risk groups receive timely and effective preventive treatment but may also lead to unnecessary medical interventions for low-risk individuals. This situation makes it difficult to effectively reduce the morbidity and mortality rates of HIV-TB co-infection populations, and global efforts to control HIV-TB co-infection have reached a bottleneck. Therefore, solving this problem urgently requires breakthrough technological innovation, namely, developing a predictive biomarker combination with both high sensitivity and specificity to construct an ideal risk prediction tool. Summary of the Invention

[0005] The purpose of this application is to provide an application of a genetic marker for assessing the risk of active tuberculosis in HIV-infected individuals, aiming to address the lack of existing technologies for assessing the risk of active tuberculosis in HIV-infected individuals.

[0006] To achieve the above-mentioned objectives, the technical solution adopted in this application is as follows:

[0007] In a first aspect, this application provides a quantitative detection reagent for genetic markers for use in the preparation of products assessing the risk of active tuberculosis in HIV-infected individuals. The genetic markers include one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4, and TSEN2. The mRNA sequence of BEX5 is shown in Seq. No. 1, the mRNA sequence of CEL is shown in Seq. No. 2, the mRNA sequence of DUSP14 is shown in Seq. No. 3, the mRNA sequence of EIF3I is shown in Seq. No. 4, the mRNA sequence of PPFIA4 is shown in Seq. No. 5, and the mRNA sequence of TSEN2 is shown in Seq. No. 6.

[0008] In some embodiments, the product is a kit for assessing the risk of active tuberculosis in HIV-infected individuals, wherein the kit includes a primer set for detecting genetic markers.

[0009] In some embodiments, the primer set includes at least one pair of specific primer sets:

[0010] (a) A first specific primer set for detecting BEX5, wherein the first specific primer set includes a first upstream primer sequence as shown in Seq. No. 7 and a first downstream primer sequence as shown in Seq. No. 8;

[0011] (b) a second specific primer set for detecting CEL, wherein the second specific primer set comprises a second upstream primer sequence as set forth in Seq. No. 9, and a second downstream primer sequence as set forth in Seq. No. 10;

[0012] (c) a third specific primer set for detecting DUSP14, wherein the third specific primer set comprises a third upstream primer sequence as set forth in Seq. No. 11, and a third downstream primer sequence as set forth in Seq. No. 12;

[0013] (d) a fourth specific primer set for detecting EIF3I, wherein the fourth specific primer set comprises a fourth upstream primer sequence as set forth in Seq. No. 13, and a fourth downstream primer sequence as set forth in Seq. No. 14;

[0014] (e) a fifth specific primer set for detecting PPFIA4, wherein the fifth specific primer set comprises a fifth upstream primer sequence as set forth in Seq. No. 15, and a fifth downstream primer sequence as set forth in Seq. No. 16;

[0015] (f) a sixth specific primer set for detecting TSEN2, wherein the sixth specific primer set comprises a sixth upstream primer sequence as set forth in Seq. No. 17, and a sixth downstream primer sequence as set forth in Seq. No. 18.

[0016] In some embodiments, the kit further comprises a seventh specific primer set for amplifying the internal reference gene β-ACTIN, wherein the seventh specific primer set comprises a seventh upstream primer sequence as set forth in Seq. No. 19, and a seventh downstream primer sequence as set forth in Seq. No. 20.

[0017] In some embodiments, the kit further comprises reagents for real-time fluorescence quantitative reaction.

[0018] In some embodiments, the kit further comprises a logistic regression model for evaluating the risk of active tuberculosis in HIV infected persons.

[0019] In some embodiments, the product is a system for evaluating anti-tuberculosis efficacy, wherein the system comprises:

[0020] a data acquisition unit for acquiring expression data Ct values of the above-mentioned gene markers in the sample after gene detection of the sample;

[0021] a data analysis unit for standardizing the Ct values to obtain ΔCt values, processing the ΔCt values by using the above-mentioned logistic regression model, and analyzing the prediction probability value of the sample;

[0022] The data prediction unit is configured to compare the prediction probability value with a threshold value to obtain a result of predicting the risk of the HIV infected person developing active tuberculosis.

[0023] In some embodiments, the threshold value is 0.45, and when the output value of the result of predicting the risk of the HIV infected person developing active tuberculosis is lower than the threshold value, it is determined as high risk; and when the output value of the result of predicting the risk of the HIV infected person developing active tuberculosis is higher than the threshold value, it is determined as high risk.

[0024] In some embodiments, the active tuberculosis includes at least one of pulmonary tuberculosis, extrapulmonary tuberculosis, or hematogenous disseminated pulmonary tuberculosis.

[0025] In a second aspect, the present application provides a kit for evaluating the risk of HIV infected person developing active tuberculosis, the kit comprising a quantitative detection reagent for a gene marker, and the gene marker comprises one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4, and TSEN2; wherein the mRNA sequence of BEX5 is shown as Seq. No. 1, the mRNA sequence of CEL is shown as Seq. No. 2, the mRNA sequence of DUSP14 is shown as Seq. No. 3, the mRNA sequence of EIF3I is shown as Seq. No. 4, the mRNA sequence of PPFIA4 is shown as Seq. No. 5, and the mRNA sequence of TSEN2 is shown as Seq. No. 6.

[0026] The application of the quantitative detection reagent for a gene marker in the preparation of a product for evaluating the risk of HIV infected person developing active tuberculosis, in which one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4, and TSEN2 are applied to prepare a product for evaluating the risk of HIV infected person developing active tuberculosis, fills the technical gap, realizes accurate risk identification, and solves the problem in the prior art that there is a lack of reliable tools to identify high-risk groups of active tuberculosis in HIV infected persons. Compared with traditional clinical symptoms or bacteriological detection, it can capture the disease progression trend earlier and more accurately from the genetic level, laying a foundation for targeted implementation of tuberculosis preventive treatment. In terms of application flexibility, flexibility and effectiveness are taken into account, supporting the use of single or multiple gene markers in combination, which can meet the rapid screening needs in resource-limited scenarios, and can improve prediction accuracy through multiple markers, avoiding missed detection and false detection problems that may occur with a single marker. In terms of standardization, the application lays a foundation for standardization, the clear mRNA sequence ensures the uniqueness and repeatability of the gene marker, provides a unified technical basis for the standardized production and clinical promotion of subsequent detection reagents and kits, and ensures the comparability of detection results in different laboratories and regions.

[0027] The kit for evaluating the risk of active tuberculosis of the HIV infected person provided in the second aspect of the application is quantitatively detected by means of one or more gene markers containing BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2 (the mRNA sequences are shown as Seq. No. 1-6, respectively); the active tuberculosis diagnosis dilemma of the HIV infected person is accurately solved, the problems of low sensitivity, specificity and sample acquisition in the traditional detection in the population are broken through by the specific gene markers, the peripheral blood sample can be detected, the sensitivity is expected to be more than 85%, and the specificity is expected to be more than 90%; the treatment effect optimization scheme can also be dynamically monitored; and the markers can be combined to synergistically reduce the limitations of single markers, the results can be obtained within 2-4 hours based on the RT-qPCR technology, the equipment is popular, and it is suitable for the clinical rapid diagnosis demand; meanwhile, the prognosis of the HIV infected person can be improved, the tuberculosis related mortality can be reduced, and the tuberculosis transmission can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0029] Figure 1 FIG. 6 is a ROC curve diagram of the RT-qPCR quantitative detection results of the six gene markers in the baseline PBMC of the training queue provided in the embodiments of the present application to predict the HIV infected person complicated with active tuberculosis.

[0030] Figure 2 FIG. 7 is a ROC curve diagram of the RT-qPCR quantitative detection results of the six gene markers in the baseline PBMC of the verification queue provided in the embodiments of the present application to predict the HIV infected person complicated with active tuberculosis.

[0031] Figure 3 FIG. 8 is the difference between the LR model values of the six gene markers in the training queue and the test queue provided in the embodiments of the present application in the HIV infected person complicated with tuberculosis and the group without tuberculosis.

[0032] Figure 4 FIG. 9 is a ROC curve diagram of the LR model values of the six gene markers in the training queue and the verification queue provided in the embodiments of the present application to predict the HIV infected person complicated with active tuberculosis. DETAILED DESCRIPTION

[0033] In order to make the technical problems, technical solutions and beneficial effects of the present application clearer, the present application will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application and not to limit the present application.

[0034] The first aspect of the embodiments of the present application provides an application of a quantitative detection reagent of a genetic marker in the preparation of a product for evaluating the risk of active tuberculosis of an HIV infected person, the genetic marker comprising one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2; wherein the mRNA sequence of BEX5 is shown in Seq. No. 1, the mRNA sequence of CEL is shown in Seq. No. 2, the mRNA sequence of DUSP14 is shown in Seq. No. 3, the mRNA sequence of EIF3I is shown in Seq. No. 4, the mRNA sequence of PPFIA4 is shown in Seq. No. 5, and the mRNA sequence of TSEN2 is shown in Seq. No. 6.

[0035] The application of the quantitative detection reagent of the genetic marker provided by the first aspect of the embodiments of the present application in the preparation of the product for evaluating the risk of active tuberculosis of the HIV infected person, in which one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2 are applied to the preparation of the product for evaluating the risk of active tuberculosis of the HIV infected person, fills the technical gap at the technical level, realizes accurate risk identification, and solves the problem that there is no reliable tool to identify the high-risk population of active tuberculosis in the HIV infected person in the prior art. Compared with traditional clinical symptoms or bacteriological detection, the disease progression trend can be captured earlier and more accurately from the genetic level, which lays a foundation for targeted implementation of tuberculosis preventive treatment (TPT); in terms of application flexibility, flexibility and effectiveness are taken into account, and single or multiple genetic markers can be used in combination, which can meet the rapid screening needs in the limited resource scenario, and can improve the prediction accuracy through multiple markers, avoiding the missed detection and false detection problems of a single marker; in terms of standardization construction, the standardization foundation is laid, and the uniqueness and repeatability of the genetic marker are ensured by the clear mRNA sequence, which provides a unified technical basis for the standardized production and clinical popularization of subsequent detection reagents and kits, and ensures the comparability of detection results in different laboratories and regions.

[0036] The full names of the above-mentioned 6 genes are provided by the NCBI (National Center for Biotechnology Information (nih.gov)) platform, as follows:

[0037] BEX5: brain expressed X-linked 5;

[0038] CEL: carboxyl ester lipase;

[0039] EIF3I: eukaryotic translation initiation factor 3 subunit I;

[0040] DUSP14: dual specificity phosphatase 14;

[0041] PPFIA4: PPFI scaffold protein A4 PTPRF interacting protein alpha 4;

[0042] TSEN2: tRNA splicing endonuclease subunit 2.

[0043] In some embodiments, the mRNA sequence of BEX5 is as set forth in Seq. No. 1, which is as follows:

[0044] AGTTGCAGTCTGGTAGTTGTCGCTGGCCGTGTGACGGCTCGCTGTTGCCCTGAAGGCAGGCGAGCCAGCTGCCCAGGAAAGGTGGAAAGTGGTAGAAGCTGACCCCTGAGCCCTGGCAGGTCTTTAAGTGCGTTTGTGCAGCCGATTTCAAGGCTAAGAGAGAAAGACTGCCTCTGATCCCTGAAGGAAGAAAAAAAAAAAAAAAACAGGAAAAAAACTCAACATGGAAAATGTCCCCAAGGAAAACAAAGTTGTGGAGAAGGCCCCAGTGCAGAATGAAGCCCCCGCTTTAGGAGGTGGTGAATACCAGGAGCCTGGAGGAAATGTTAAAGGGGTTTGGGCTCCACCTGCCCCGGGTTTTGGAGAGGATGTGCCCAATAGGCTTGTCGATAACATTGATATGATAGATGGAGATGGAGATGATATGGAACGGTTCATGGAGGAGATGAGAGAGCTAAGGAGGAAAATTAGGGAACTTCAGTTGAGGTACAGTCTGCGCATTCTTATAGGGGACCCTCCTCACCATGATCATCATGATGAGTTTTGCCTTATGCCTTGAATCTTGAGGTTAATAATCATAAAATCCCTGCTTTCTAAATTCGCATTTTTCCTGGTGTACCTTTAATGTGAACCTTTTGGCATTCTTCTGCAATTTTCTGATTGGAGATTGCATTTTGACCTAGTCTGTAAGTTTTTCTGTCAGAAGAGGACTTTCATCAACTTTCATGGAAAGATGTTTATTGCATACTGTAAAGTTAATAAAGCAATTTAAAAGCA.

[0045] In some embodiments, the mRNA sequence of the CEL is as set forth in Seq. No. 2, which is as follows:

[0046]

[0047] In some embodiments, the mRNA sequence of DUSP14 is as set forth in Seq. No. 3, which is as follows:

[0048]

[0049] In some embodiments, the mRNA sequence of EIF3I is set forth in Seq. No. 4, which is as follows:

[0050]

[0051] In some embodiments, the mRNA sequence of PPFIA4 is set forth in Seq. No. 5, which is as follows:

[0052]

[0053] TGCCTGGTATGT

[0054] GGCAGCCTGCCGGGCCAACGTCAAGAGTGGTGCCATCATGTCCGCTCTGTCGGACACAGAGAT

[0055] CCAGCGGGAGATCGGCATCAGCAATGCCCTGCACCGGCTCAAGCTCCGCCTGGCCATTCAGGA

[0056] GATGGTGTCATTGACCAGCCCCTCTGCCCCACCCACCTCCAGGACTTCTTCTGGGAATGTCTGG

[0057] GTCACCCATGAAGAGATGGAAACTCTGGAAACATCTACTAAAACAGACAGTGAGGAGGGCAGC

[0058] TGGGCTCAGACCCTGGCCTATGGGGACATGAACCATGAGTGGATTGGGAATGAATGGCTACCCA

[0059] GCCTGGGGCTCCCGCAGTACCGCAGCTACTTCATGGAGTGCCTGGTGGACGCCCGCATGCTGGA

[0060] CCACCTCACCAAGAAGGACCTGCGGGTCCACCTGAAGATGGTGGACAGCTTCCATCGAACCAG

[0061] TCTTCAGTATGGCATCATGTGTCTGAAGAGGCTGAATTATGACCGGAAGGAGCTGGAGAAGAGG

[0062] CGAGAGGAGAGCCAGCATGAGATCAAGGATGTGTTAGTCTGGACCAACGACCAGGTGGTTCAT

[0063] TGGGTCCAGTCTATTGGGCTCCGGGACTACGCAGGAAACCTGCATGAGAGTGGTGTGCATGGA

[0064] GCCTTGCTGGCCCTGGACGAGAACTTCGACCACAACACACTGGCCCTGATCCTCCAGATCCCCA

[0065] CACAGAACACCCAGGCACGCCAAGTGATGGAAAGAGAGTTCAATAACCTGTTGGCCTTGGGCA

[0066] CAGACCGGAAGCTGGATGACGGGGATGACAAGGTGTTTCGCCGCGCGCCCTCCTGGAGGAAGC

[0067] GCTTCCGGCCGCGGGAGCACCACGGTCGCGGCGGCATGCTCAGCGCTTCCGCGGAGACCCTCC

[0068] CGGCGGGCTTCCGTGTGTCCACCCTGGGGACCCTGCAGCCCCCACCGGCCCCGCCAAAGAAGA

[0069] TCATGCCTGAAGCTCACTCCCACTATCTCTACGGACACATGCTCTCCGCCTTCCGGGACTAGCCA

[0070] TGGCCCCCAGGGCTGGCTTCCTCCTTCTGGGTTTCACAGGCTCCTCTGGCCCTGACCCCTCTTG

[0071] CTCGTTCCCCTTCCTTCCGCAGCTCCTAGTCTCGTCCGTGACTTTCCGGTTGCCCTGGATCTCAG

[0072] AATATATTCGTCCACCCCCTCGGCACCCCATTACCCCGAGTCCCACCGTGTGTCCGTTGTAAGTC

[0073] CGGTGGATGTGGCTGGGGTTTCCTGGTATTGTGGAGGCACCCAGGTTGTCCATGCTTGGGATTC

[0074] TGGGGGAAGGAGAGAAGGGCAGCTCAGGGTGGATGTGAAGCCACCCTTCCTCTTCTGGACCCA

[0075] GCCTGGTCTGCACTGCAACCTCCACCAGGACCAGGATCCTGGGCCACAGGCTGGGATGGTCCT

[0076] TCCAAGAAAGGGTCATTTCAGACGCAGCCCTGCTTGGGCTATTCAATCTTAGGGTGTCTATCCAC

[0077] GTCTGGCTGTGCCAAATGGTCTGGCAGCTGGTTTTGGCATCCCCAGCATCACCACTCTCCCAAC

[0078] CCATCACCGTGACTGCAGTTCCTGCCCCCATTCTCTTGGGGTCAGGGAGGGGCTGGGAAGGGCT

[0079] ACTGAAGGCCCCATTCTCCCACAGGATGGTGAGGCTGGGAGGAGGAAGACTGAGGTAGAGATT

[0080] CCAGGCCCTGGCATAAGCTGAATCCCAAATTTGAGTTTGGGAAGAACCAGAGAGAAATGGATC

[0081] CCTGAGCTCTGAGCCAAGGGTGAGGATGGGGAAACTCTAAGCTCCCACCTAATAAGAAGCATA

[0082] GGCAGACCAGCCAGAGGGAGAGCCAATGGCCTCTGGTAGCCTTAAGCCCAAAGGGCAGTGGG

[0083] AATGTCCCCTGCCCCAACCATCGGGTGGAGCTCCTGCTGGGCTATGGGGAAGGGAGGTTGTGC

[0084] GGATCTTGACTCTAGGGCAGAACAGATCTAACCATGCATTGCTAGCTCTGCTCCCAGCATCCCTT

[0085] CCCCTTCTCTCCTCCTCTGCCTCACTTCTTTAGTAATCCCAACCCTATAAAAATGAACCTAATGGG

[0086] TGGATTGAATATACATTGAGCCCAAAGTCAAGTTTGGGGAAAAGGCAGACTAAGGCCTCCTTTC

[0087] TCTGACCTCCCAGGAAGAAAATAGCTTCTCCTACAGTGATTCATGTCCCAGGTCCAGGAAATCC

[0088] AATGTTGGTGAAGGCAGCCACTCTCTTGCTTGTCCCCAAATCACCTAACCCTCATCCAGGGCTAT

[0089] TTTGGTGGGCAGGGACTGCCTCCTCCCGGAATTCCTAAGATCCGCCCAGCTGCCACCATTTTCAT

[0090] TGCTTTCCCCAGCAGCATGATGGGAACCCAAGCTGAGGGATACAGGTCCTGATTTGGTAGGAAT

[0091] ATTATTCCCAAGAAATACCCGCTCCTCACCTACTCCCTCATCCTACCAAGGTGCCTGAAAATGTT

[0092] CAAGACTTATGTTCAGGGTGGGATGATGGAACCGAGGGCTTCATCAAAGTGAGAGGAAAGGAA

[0093] AAGCATCTGGCATGTGTTTCTTGGATAGGGGCCAGTGCAGTGCCATCCTACAGGTGGCTGGAGC

[0094] AGCTGCTTTGCAACCTGATCACCTTGAGTTCTGAGCAGGGACTAGGCTTGCAGGTGAGATAATG

[0095] GGCCAGGGCACCCAGTCCAGAAGGAGCAATGGCACCTGGGCAGTGCCAGGGCTTAAAGCCCG

[0096] CTGCTCCTTTTCGGTAGAGGAGAGGCCCATCACTGGTGTGGTGGGGTGGGCTCTCCCTTAGGCT

[0097] TGGGCAAGGCAGCCACCTGCCCTTGCTCTCCCTTAGTGTTCCCTGGCCTCCCTGCCATCAGGTT

[0098] GCTGGGAGTGGAGATGGAGGGATTATTGAGCAGAAAATGAGTTGGATGGAGATAAACAGCTCC

[0099] CATCCCTGGGTAATGGATGGTAAGATGATGGAGATTCCTAAGATTGGTGGAGTTGGGCAATGCAT

[0100] AGCCATCTGACTCCTTCAGGGTGCTCTTGATGGGCTGGCTGTAAGGGAGACTCAGTCCCAGCCT

[0101] CTCCCCTCTACAACTCCTGCCACTGTTGGCCATGTCGTAAGGCAGCAGCTGTGCCAGGATAGCT

[0102] GGGTCCATTCAGAGCACCTTGAGAAGTGTTGCAGGGAGGTGTTAAGAAGAGAACTCTGTGCAA

[0103] ACAGTGATGGAAGGCTGTTGTCTTGGTGTATCCCTTGCCTCATAGTCAATATATTTTTTTTTTGGC

[0104] GAGTCACCAGTGACCCGAGCCCTCCACACCAGCCTCCTGTATCTCATCAGGTCCCTTCTCAGTA

[0105] CTGTATTTGCTCAGTGCATCAGGAATGGGTGTATGGGTGTGTGTGGGTGGGTGTGAGTGTGGGTGTGTACGTACCAATAAACAACCTGGTTTTAAGACAATGTA.

[0106] In some embodiments, the mRNA sequence of TSEN2 is set forth in Seq. No. 6, which is as follows:

[0107]

[0108] In some embodiments, the product is a kit for assessing the risk of active tuberculosis in HIV infected persons, wherein the kit comprises a primer set for detecting the genetic markers. The product of the kit is provided, which, in use, avoids the cumbersome process of designing and formulating reagents by the clinical laboratory itself, reduces the operation threshold (without the need for professional molecular design ability), while unifying the reagent composition, dosage, reducing the detection error caused by the difference of reagents, meeting the needs of clinical "rapid and stable detection". And the primer set for detecting the above- provided genetic markers in the reaction is limited, which ensures that the mRNA expression of the genetic markers can be accurately quantified by mature technologies such as qPCR in the future.

[0109] In some embodiments, the primer set comprises at least one pair of specific primer sets as follows:

[0110] (a) a first specific primer set for detecting BEX5, wherein the first specific primer set comprises a first upstream primer sequence as shown in Seq. No. 7 and a first downstream primer sequence as shown in Seq. No. 8;

[0111] (b) a second specific primer set for detecting CEL, wherein the second specific primer set comprises a second upstream primer sequence as shown in Seq. No. 9 and a second downstream primer sequence as shown in Seq. No. 10;

[0112] (c) a third specific primer set for detecting DUSP14, wherein the third specific primer set comprises a third upstream primer sequence as shown in Seq. No. 11 and a third downstream primer sequence as shown in Seq. No. 12;

[0113] (d) a fourth specific primer set for detecting EIF3I, wherein the fourth specific primer set comprises a fourth upstream primer sequence as shown in Seq. No. 13 and a fourth downstream primer sequence as shown in Seq. No. 14;

[0114] (e) a fifth specific primer set for detecting PPFIA4, wherein the fifth specific primer set comprises a fifth upstream primer sequence as shown in Seq. No. 15 and a fifth downstream primer sequence as shown in Seq. No. 16;

[0115] (f) a sixth specific primer set for detecting TSEN2, wherein the sixth specific primer set comprises a sixth upstream primer sequence as shown in Seq. No. 17 and a sixth downstream primer sequence as shown in Seq. No. 18.

[0116] The up and down primer sequences designed for each gene are based on the unique regions of the mRNA of each gene, which can precisely bind to the target sequence and avoid non-specific binding to other homologous genes or non-target RNA, thereby reducing "false positives" (misjudgment caused by non-specific amplification) and "false negatives" (missed judgment caused by ineffective amplification of target sequence), ensuring high specificity and high sensitivity (can detect low expression of mRNA) of quantitative results.

[0117] In some embodiments, reagents for detecting the following substances can also be provided, including:

[0118] (1) BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2 genes;

[0119] (3) proteins encoded by BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2 genes.

[0120] In some embodiments, the kit further comprises a seventh specific primer set for amplifying the internal reference gene β-ACTIN, which comprises a seventh upstream primer sequence as shown in Seq. No. 19 and a seventh downstream primer sequence as shown in Seq. No. 20.

[0121] The above sequences are shown in Table 1.

[0122] Table 1

[0123]

[0124] In gene quantitative detection, the amount of RNA extracted from the sample, the reverse transcription efficiency and the PCR amplification efficiency will directly affect the accuracy of the Ct value (cycle threshold value). β-ACTIN is a housekeeping gene that can be stably expressed in cells and is not affected by disease status. By correcting the Ct value of the target gene (calculating ΔCt = target gene Ct - internal reference gene Ct), the above interference factors can be effectively offset, ensuring the authenticity and comparability of the quantitative results, improving the data reliability and reducing the risk of misjudgment caused by technical errors, and providing "high-quality data input" for subsequent risk assessment models.

[0125] In some embodiments, the kit further comprises reagents for real-time fluorescent quantitative reaction.

[0126] Real-time fluorescent quantitative reaction requires the cooperation of multiple reagents, such as enzymes, buffers, fluorescent dyes, etc. Reagents from different manufacturers may have compatibility problems, leading to unstable amplification efficiency. The kit pre-integrates the quantitative reaction reagents, which can ensure the optimal proportion and good compatibility of each component, avoid experimental failure caused by improper reagent matching, and significantly improve the repeatability of the detection.

[0127] In some embodiments, the kit further comprises a logistic regression model for assessing the risk of developing active tuberculosis in the HIV infected person.

[0128] The ACt value of the genetic marker is raw data, and it is difficult for a clinician to directly determine the "risk level" through the ACt value. Therefore, a logistic regression model is provided, which is trained based on the gene expression data and clinical outcomes of a large number of HIV infected persons (case group / control group), can automatically integrate the ACt values of multiple genes, calculate the "predicted probability of developing active tuberculosis", convert abstract data into "quantifiable risk value", and the algorithm of the model is fixed and repeatable, ensuring that different doctors and different institutions have consistent risk assessment results for the same patient, and achieving the standardization of the evaluation criteria.

[0129] In some embodiments, the product is a system for evaluating the efficacy of anti-tuberculosis treatment, wherein the system comprises:

[0130] A data acquisition unit for performing gene detection on the sample to obtain expression data Ct values of the genetic markers in the sample obtained by gene detection;

[0131] A data analysis unit for standardizing the Ct values to obtain ACt values, processing the ACt values using the logistic regression model, and analyzing the predicted probability value of the sample;

[0132] A data prediction unit for comparing the predicted probability value with a threshold value to obtain the result of predicting the risk of developing active tuberculosis in the HIV infected person.

[0133] In some embodiments, the gene detection includes but is not limited to the RT-qPCR method. Further, the expression level data is corrected by variance stabilization transformation. The system can detect the Ct values of the genetic markers multiple times (such as before treatment, 1 month after treatment, and 3 months after treatment) during the anti-tuberculosis treatment, analyze the change trend of the ACt values, and further determine whether the treatment is effective, thereby achieving the purpose of dynamic monitoring.

[0134] In some embodiments, the threshold value is 0.45, when the output value of the result of predicting the risk of developing active tuberculosis in the HIV infected person is lower than the threshold value, it is determined as high risk; when the output value of the result of predicting the risk of developing active tuberculosis in the HIV infected person is higher than the threshold value, it is determined as high risk. According to the predicted values of the training queue data and the verification queue data, the optimal threshold value of the model is calculated using the Youden index, i.e. the threshold value is 0.45.

[0135] In some embodiments, the active tuberculosis includes at least one of pulmonary tuberculosis, extrapulmonary tuberculosis, or blood-borne disseminated pulmonary tuberculosis.

[0136] The second aspect of the embodiment of the present application provides a kit for evaluating the risk of active tuberculosis of an HIV infected person, the kit comprising a quantitative detection reagent for a genetic marker, and the genetic marker comprises one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2; wherein the mRNA sequence of BEX5 is shown as Seq. No. 1, the mRNA sequence of CEL is shown as Seq. No. 2, the mRNA sequence of DUSP14 is shown as Seq. No. 3, the mRNA sequence of EIF3I is shown as Seq. No. 4, the mRNA sequence of PPFIA4 is shown as Seq. No. 5, and the mRNA sequence of TSEN2 is shown as Seq. No. 6.

[0137] The kit for evaluating the risk of active tuberculosis of an HIV infected person provided by the second aspect of the embodiment of the present application is based on the quantitative detection reagent of one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2 (the mRNA sequences are shown as Seq. Nos. 1-6, respectively), which can accurately solve the diagnosis dilemma of active tuberculosis of an HIV infected person, break through the problems of low sensitivity, specificity and difficulty in obtaining samples in the traditional detection of this population through specific genetic markers, can detect peripheral blood samples and the sensitivity is expected to reach more than 85% and the specificity is expected to reach more than 90%, can dynamically monitor the treatment effect optimization scheme, the markers can be combined to synergistically reduce the limitations of a single marker, the results can be obtained within 2-4 hours based on the RT-qPCR technology and the equipment is popular, which is suitable for the clinical rapid diagnosis demand, can improve the prognosis of an HIV infected person, reduce the mortality rate of tuberculosis and reduce the spread of tuberculosis.

[0138] The following will be described in combination with specific embodiments.

[0139] Embodiment 1

[0140] 1. The clinical cohort enrollment criteria of the present embodiment.

[0141] This example employed a nested case-control study approach. Participants were recruited from the NIH Registry for Clinical Research (OSPWH, NCT04667026). This example included PWH from the same center and different time periods who were not on antiretroviral ART therapy for HIV. The training cohort PWH were enrolled between 2015 and 2020, and the validation cohort PWH were enrolled between 2020 and 2024. The training cohort included 31 PWH with concurrent active TB and 82 PWH without concurrent active TB, and the validation cohort included 11 PWH with concurrent active TB and 30 PWH without concurrent active TB. All enrolled PWH underwent systematic baseline assessments, including standardized clinical assessment for Mtb infection, chest X-ray, HIV viral load, CD4 cell count, and routine blood tests, and were followed up at 1, 2, 3, 6, 9, 12, 24, and 36 months after standardized antiretroviral ART therapy. During follow-up, PWH were immediately evaluated for TB diagnosis if they presented with any suspicious TB symptoms. The diagnostic criteria for TB were: (1) positive culture or GeneXpert detection of any sputum, blood, or alveolar lavage fluid; (2) positive acid-fast staining (AFB); and (3) typical evidence of pulmonary TB imaging and response to anti-TB treatment. PWH diagnosed with TB at baseline or with a history of TB were not included in the follow-up cohort. Any newly diagnosed TB cases after the start of ART were defined as new TB cases. The timing and regimen of ART and anti-TB therapy were selected according to national treatment guidelines.

[0142] 2. Baseline peripheral blood mononuclear cell (PBMC) collection and RNA extraction.

[0143] All PWHs were collected 4 mL fasting EDTA anti-coagulation venous blood at enrollment, diluted with 4 mL PBS buffer, and the diluted blood was slowly added to the lymphocyte separation liquid (TBD, LTS1077), and centrifuged at 2500 rpm / min for 20 minutes. The middle white membrane layer (rich in PBMC) was aspirated, washed with PBS buffer to obtain PBMC, and frozen in liquid nitrogen for standby. When the example was carried out, the PBMC frozen in liquid nitrogen was taken out, quickly dissolved in a 37°C water area, centrifuged at 2500 rpm / min for 5 min at room temperature, and the supernatant was discarded. Resuspend with 1 mL PBS, repeat the above centrifugation step, and discard the supernatant. Resuspend the cells with 0.5 mL Trizol, shake vigorously until the solution is transparent, and stand at room temperature for 5 minutes. Add 0.1 mL chloroform, mix well, and centrifuge at 12,000 x g at 4°C for 15 minutes. Extract the upper aqueous phase into a new 1.5 mL EP tube. Add an equal volume of isopropanol to the aqueous phase, and stand at -20°C for 20 minutes, centrifuge at 12,000 x g at 4°C for 15 minutes, and a white precipitate (RNA) is visible at the bottom of the tube. Discard the supernatant, add 1 mL 75% ethanol to wash the precipitate, centrifuge at 7,500 x g at 4°C for 5 minutes. Add 50 uL DEPC water to dissolve the RNA and measure the concentration, and freeze at -80°C.

[0144] 3. qRT-PCR quantitative detection and analysis of PBMC-related marker genes.

[0145] The extracted RNA was taken out and dissolved on ice. Total RNA was reverse transcribed using HiScript III All-in-one RT SuperMix Perfect for qPCR (Vazyme, Shanghai, China). The reverse transcription system was as follows: 1 ug RNA, RNase-free ddH2O supplemented to 20 uL, 5 x All-in-one qRT SuperMix 4 uL, Enzyme Mix 1 uL. The reverse transcription reaction program was as follows: 50 °C for 15 min, 85 °C for 5 sec. RT-qPCR was performed using ChamQ SYBR qPCR Master Mix (High ROX Premixed) (Vazyme). The RT-qPCR system was as follows: cDNA template 2.0 uL, 2 x ChamQ SYBR qPCR Master Mix (High ROX Premixed) 10.0 uL, forward primer (10 uM) 0.4 uL, reverse primer (10 uM) 0.4 uL, RNase-free ddH2O supplemented to 20.0 uL. The RT-qPCR program was as follows: reaction program: 95 °C for 30 sec; cycle program: 95 °C for 10 sec, 60 °C for 30 sec, 40 cycles of reaction, to obtain the CT values of six target genes BEX5, CEL, DUSP14, EIF3I, PPFIA4, TSEN2 and the internal reference gene β-ACTIN for data analysis. The specific primer sequences used for each gene detection are shown in Table 1 above.

[0146] 4. Expression difference of each gene marker in different cohorts and its prediction performance evaluation for active tuberculosis in HIV-infected patients.

[0147] The example calculates the ΔCT value by extracting, reverse transcribing and qRT-PCR quantitatively detecting the PBMC RNA of HIV-infected patients with concurrent tuberculosis and patients without active tuberculosis. Through scale normalization processing, the ROC curve of a single gene is constructed, and it is found that in the training set, each gene marker is used to predict the risk of tuberculosis, and the AUC values are as follows: BEX5 0.81 (95% CI: 0.72-0.90), CEL 0.52 (95% CI: 0.39-0.65), DUSP14 0.57 (95% CI: 0.45-0.70), EIF3I 0.69 (95% CI: 0.57-0.80), PPFIA4 0.83 (95% CI: 0.72-0.93), TSEN2 0.54 (95% CI: 0.42-0.66) (see Figure 1); and the AUC values for each of the gene markers in predicting the risk of developing TB in the test set were: BEX50.72 (95% CI: 0.56-0.88), CEL 0.77 (95% CI: 0.62-0.92), DUSP140.74 (95% CI: 0.58-0.89), EIF3I 0.67 (95% CI: 0.47-0.87), PPFIA4 0.77 (95% CI: 0.60-0.94), TSEN2 0.80 (95% CI: 0.63-0.97) (see Figure 2 ).

[0148] 5. Model building based on the above gene markers and its performance evaluation in predicting the development of active TB in HIV-infected individuals.

[0149] The model was built based on the Python scikit-learn module, and the automatic parameter tuning method selected GridSearchCV, and set 7-fold cross-validation to fit the best performance of the model for the data, and the final modeling was performed according to the optimal parameters selected by GridSearchCV. As shown in Figure 3 , the LR model values in the training queue and the validation queue were significantly lower in the HIV-infected individuals with concurrent TB group than in the non-TB group.

[0150] Then, according to the predicted values of the training queue data and the validation queue data, the best threshold of the model was calculated using the Youden index. The ROC curve further clarified the effectiveness of the combination of the gene markers, i.e., the LR model value in predicting HIV-infected individuals with concurrent active TB: as shown in Figure 4 , the best critical point (“threshold”) of the model was 0.45. The AUC of the training set was 0.87 (95% CI: 0.78-0.95), the sensitivity was 0.83 (95% CI: 0.73-0.91), the specificity was 0.78 (95% CI: 0.58-0.91), the accuracy was 0.82 (95% CI: 0.73-0.89), the positive predictive value and the negative predictive value were 0.91 (95% CI: 0.82-0.97) and 0.62 (95% CI: 0.44-0.78), respectively. The AUC of the test set was 0.73 (95% CI: 0.52-0.93), the sensitivity was 0.71 (95% CI: 0.51-0.87), the specificity was 0.82 (95% CI: 0.48-0.98), the accuracy was 0.74 (95% CI: 0.58-0.87), the positive predictive value and the negative predictive value were 0.91 (95% CI: 0.71-0.99) and 0.53 (95% CI: 0.28-0.77), respectively.

[0151] 6. Conclusion

[0152] In summary, the embodiments of the present application can effectively predict the risk of HIV infected persons complicated with active tuberculosis by RT-qPCR detection and statistical analysis of six genes BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2 in peripheral blood mononuclear cells, and by the calculation of the established LR model.

[0153] In summary, the application provides a quantitative detection reagent of a gene marker for preparing a product for evaluating the risk of HIV infected persons suffering from active tuberculosis. In the application, one or more gene markers of BEX5, CEL, DUSP14, EIF3I, PPFIA4 and TSEN2 are applied to prepare a product for evaluating the risk of HIV infected persons suffering from active tuberculosis. In the technical level, the application fills the technical gap, realizes accurate risk identification, and solves the problem in the prior art that there is a lack of reliable tools to identify high-risk groups of active tuberculosis in HIV infected persons. Compared with traditional clinical symptoms or bacteriological detection, the application can capture the disease progression trend from the gene level earlier and more accurately, and lays a foundation for targeted implementation of tuberculosis preventive treatment. In the application flexibility, flexibility and effectiveness are taken into account, and single or multiple gene markers are combined for use. The application can meet the rapid screening needs in the limited resource scene, and can improve the prediction accuracy through multiple markers, and avoid the missed detection and false detection problems of single marker. In the standardization construction, the application lays a standardization foundation, and the clear mRNA sequence limitation ensures the uniqueness and repeatability of the gene marker, provides a unified technical basis for the standardization production and clinical popularization of subsequent detection reagents and kits, and guarantees the comparability of detection results in different laboratories and regions.

[0154] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. The application of a quantitative detection reagent for a genetic marker in the preparation of products for assessing the risk of active tuberculosis in HIV-infected individuals, characterized in that, The genetic markers include one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4, and TSEN2; wherein the mRNA sequence of BEX5 is shown in Seq. No. 1, the mRNA sequence of CEL is shown in Seq. No. 2, the mRNA sequence of DUSP14 is shown in Seq. No. 3, the mRNA sequence of EIF3I is shown in Seq. No. 4, the mRNA sequence of PPFIA4 is shown in Seq. No. 5, and the mRNA sequence of TSEN2 is shown in Seq. No.

6.

2. The application of the quantitative detection reagent for gene markers according to claim 1 in the preparation of products for assessing the risk of active tuberculosis in HIV-infected individuals, characterized in that, The product is a kit for assessing the risk of active tuberculosis in HIV-infected individuals, wherein the kit includes a primer set for detecting the genetic marker.

3. The application according to claim 2, characterized in that, The primer set includes at least one pair of specific primers: (a) A first specific primer set for detecting BEX5, wherein the first specific primer set includes a first upstream primer sequence as shown in Seq. No. 7 and a first downstream primer sequence as shown in Seq. No. 8; (b) A second specific primer set for detecting CEL, wherein the second specific primer set includes a second upstream primer sequence as shown in Seq. No. 9 and a second downstream primer sequence as shown in Seq. No. 10; (c) A third specific primer set for detecting DUSP14, wherein the third specific primer set includes a third upstream primer sequence as shown in Seq. No. 11 and a third downstream primer sequence as shown in Seq. No. 12; (d) A fourth specific primer set for detecting EIF3I, wherein the fourth specific primer set includes a fourth upstream primer sequence as shown in Seq. No. 13 and a fourth downstream primer sequence as shown in Seq. No. 14; (e) A fifth specific primer set for detecting PPFIA4, wherein the fifth specific primer set includes a fifth upstream primer sequence as shown in Seq. No. 15 and a fifth downstream primer sequence as shown in Seq. No. 16; (f) A sixth specific primer set for detecting TSEN2, wherein the sixth specific primer set includes a sixth upstream primer sequence as shown in Seq. No. 17 and a sixth downstream primer sequence as shown in Seq. No.

18.

4. The application according to claim 2, characterized in that, The kit also includes a seventh specific primer set for amplifying the internal reference gene β-ACTIN, the seventh specific primer set including the seventh upstream primer sequence as shown in Seq. No. 19 and the seventh downstream primer sequence as shown in Seq. No.

20.

5. The application according to claim 2, characterized in that, The kit also includes reagents for use in real-time quantitative PCR.

6. The application according to any one of claims 2-5, characterized in that, The kit also includes a logistic regression model for assessing the risk of active tuberculosis in HIV-infected individuals.

7. The application according to claim 1, characterized in that, The product is a system for evaluating the efficacy of anti-tuberculosis treatment, wherein the system includes: Data acquisition unit: used to perform gene detection on the sample and obtain the Ct value obtained by real-time fluorescence quantitative reaction of the gene marker as described in claim 1 in the sample; Data analysis unit: used to standardize the Ct value to obtain the ΔCt value, process the ΔCt value using the logistic regression model described in claim 6, and analyze the predicted probability value of the sample; Data prediction unit: used to compare the predicted probability value with a threshold to obtain the result of predicting the risk of sudden active tuberculosis in HIV-infected individuals.

8. The application according to claim 7, characterized in that, The threshold is 0.

45. A high risk is defined as a predicted risk of sudden active tuberculosis in an HIV-infected individual when the output value is lower than the threshold, and vice versa. The active tuberculosis includes at least one of pulmonary tuberculosis, extrapulmonary tuberculosis, or hematogenous disseminated pulmonary tuberculosis.

9. The application according to claim 7, characterized in that, The active tuberculosis includes at least one of pulmonary tuberculosis, extrapulmonary tuberculosis, or hematogenous disseminated pulmonary tuberculosis.

10. A kit for assessing the risk of active tuberculosis in HIV-infected individuals, characterized in that, The kit includes reagents for the quantitative detection of gene markers, and the gene markers include one or more of BEX5, CEL, DUSP14, EIF3I, PPFIA4, and TSEN2; wherein the mRNA sequence of BEX5 is shown in Seq. No. 1, the mRNA sequence of CEL is shown in Seq. No. 2, the mRNA sequence of DUSP14 is shown in Seq. No. 3, the mRNA sequence of EIF3I is shown in Seq. No. 4, the mRNA sequence of PPFIA4 is shown in Seq. No. 5, and the mRNA sequence of TSEN2 is shown in Seq. No. 6.

Images