Early diagnosis biomarker of leprosy and its detection reagent and application

Abstract

The application belongs to the field of biological medicine, and particularly relates to a leprosy early diagnosis biomarker, a detection reagent and application thereof. The application provides a leprosy early diagnosis biomarker, which is one or more of CCL2 / MCP-1, IL-8, JAKM, ATP, ND1, SERP, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22 and NCF1C, 12 genes. The application finds that the differentially expressed genes can be used as useful biomarkers, and provide a good basis for the development of a blood detection method for rapid diagnosis of leprosy.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, and in particular relates to a biomarker for early diagnosis of leprosy, its detection reagent, and its application. Background Technology

[0002] Leprosy is a chronic infectious disease caused by Mycobacterium leprae. Although leprosy can be cured with various medications, it remains a serious health problem in Southeast Asia (e.g., India), North and Central Africa (e.g., the Central African Republic and the Democratic Republic of Congo), Oceania (e.g., Indonesia and Papua New Guinea), and the Americas (e.g., Brazil and Mexico) (WHO, 2017). According to WHO data from 2020, more than 230,000 new cases of leprosy were associated globally, with 10,816 cases of secondary deformity identified (https: / / www.who.int / health-topics / leprosy) (WHO, 2021). Early diagnosis of leprosy can break the chain of transmission and reduce the number of leprosy-related cases with secondary deformity in specific communities. However, accurate diagnosis of leprosy remains a challenge. Acid-fast staining of skin smears is rapid and inexpensive, but has very low sensitivity and specificity (Nag et al., 2019). A definitive diagnosis of leprosy based on clinical and pathological features requires experienced physicians. Although host biomarkers that elicit a positive immune response to Mycobacterium leprae via whole blood tests have potential diagnostic value for leprosy (Chen et al., 2019; Geluk et al., 2010; Hungria et al., 2017; Sampaio et al., 2011), there is currently no diagnostic test in China based on transcriptome analysis of leprosy patients. Therefore, there is an urgent need to develop an accurate clinical diagnostic test.

[0003] Studies on tuberculosis have shown that IL-8 (CXCL8), a chemokine primarily produced by macrophages and mesothelial cells (Park et al., 2003), plays a crucial role in recruiting lymphocytes and monocytes into the immune system in tuberculosis patients (Kurashima et al., 1997). IL-8 plays a central role in the normal immune response against Mycobacterium tuberculosis and is essential for granuloma formation (Miranda et al., 2012; Kirkaldy et al., 2003). Studies on leprosy have shown the presence of neutrophil-absent chemotactic IL-8 in leprosy lesions, strongly suggesting its role as a recruiter of monocytes and lymphocytes in these lesions (Park et al., 2003). Compared to leprosy controls, IL-8 production is significantly increased in leprosy patients (Negera et al., 2018). Our previous studies using Luminex technology also found that stimulation with the Mycobacterium leprae antigen ML2044 increased the expression of the IL-8 cytokine in leprosy patients (Chenet et al., 2019), which confirms our findings at the transcriptomic level in this study. Therefore, we found increased IL-8 mRNA / gene expression in the blood of leprosy patients, which is consistent with the potential role of this chemokine in the pathogenesis of leprosy (Negera et al., 2018; Bhat and Vaidya, 2020).

[0004] Early diagnosis is crucial for controlling leprosy. Currently, the gold standard diagnostic test for leprosy is based on acid-fast bacillus staining and histopathological examination of skin lesion biopsies. Mycobacterium leprae cannot be cultured in vitro, and acid-fast staining requires a large number of bacilli in skin biopsies, resulting in low sensitivity and poor specificity (Mohanty et al., 2020). Serological tests, such as the NDO-LID rapid test and Mycobacterium leprae antigen ELISA, are useful tools to help identify leprosy patients, especially those with multibacillary leprosy, but they have low sensitivity and high specificity (Chen et al., 2018). Some new methods have been developed, such as immunological analyses. For example, the whole blood method for assessing Mycobacterium leprae antigen-specific IFN-γ release, which measures IFN-γ production after co-culturing whole blood samples with specific Mycobacterium leprae antigens, has diagnostic value for sparse leprosy patients and TB in the population of Southwest China, but has no diagnostic value for sparse leprosy patients, family contact controls, or healthy controls (Chen et al., 2018). Therefore, it is necessary to screen for new Mycobacterium leprae-specific antigens and establish a multi-cytokine analysis model in combination with different Mycobacterium leprae antigens in order to more effectively diagnose leprosy. Many studies have used whole blood tests and leprosy patients to identify characteristic genes or proteins that can differentiate leprosy patients, healthy controls, and family contact controls (Chen et al., 2018, 2019; Geluk et al., 2012; Hungria et al., 2017; Oliveira et al., 2014; Sampaio et al., 2011). However, the relationship between transcriptome features and the usefulness of early leprosy diagnosis has been rarely discussed (Tió-Coma et al., 2019; Wen et al., 2014). A similar study in Vietnamese leprosy patients showed that after ultrasound antigen stimulation of PBMCs (peripheral blood mononuclear cells) of multibacillary leprosy patients, IFN-γ pathway genes, including IFN-γ, STAT1, IRF8, and IL-12, were upregulated (Manry). (J. et al. 2017). However, our results did not find any association between the expression of these genes and leprosy in our patients, which may be due to differences in ethnicity or disease status.

[0005] Recently, a combination of four microRNAs (miR-101, miR-196b, miR-27b, and miR-29c) was found to distinguish healthy controls from leprosy patients with 80% sensitivity and 91% specificity (Jorge et al., 2017). This set of microRNAs can also distinguish between leprosy and tuberculoid leprosy patients with 83% sensitivity and 80% specificity, potentially showing good diagnostic potential for leprosy (Jorge et al., 2017). In this study, we used different PBMC transcriptome analysis methods and found that IL-8, CCL2, and SERP can distinguish not only leprosy patients from healthy controls, but also multibacillary and seldombollary leprosy patients. Furthermore, LOC34487, LOC101928143, CCL2, and NCF1C have the potential to distinguish between multibacillary and seldombollary leprosy patients, as well as leprosy patients and family contact controls. This result is consistent with our previous study in a population in southwestern China, where leprosy antigen (ML2044)-induced IL-8 can distinguish between leprosy patients with seldom bacteriophages and healthy controls in leprosy-endemic areas (Chen et al., 2019; Bobosha et al., 2014). We also performed receiver operating characteristic (ROC) analysis in a validation cohort, suggesting that IL-8 and SERPs have potential diagnostic value in distinguishing leprosy patients and leprosy subtypes from healthy controls. NCF1C can be used to distinguish leprosy patients and leprosy subtypes from family-contact controls. Summary of the Invention

[0006] To address the above issues, this application provides a biomarker for the early diagnosis of leprosy.

[0007] Biomarkers for early diagnosis of leprosy include one or more of the following 12 genomes: CCL2 / MCP-1, IL-8, JAKM, ATP, ND1, SERP, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22, and NCF1C.

[0008] in,

[0009] Five genome combinations—IL-8, CCL2 / MCP-1, SERP, LINC00659, and FLJ10489—were used to distinguish leprosy markers from healthy individuals in endemic areas.

[0010] IL-8, CCL2 / MCP-1, SERP, and LINC00659 are diagnostic markers for multibacterial serotypes; IL-8, CCL2 / MCP-1, SERP, and FLJ10489 are diagnostic markers for oligobacterial serotypes.

[0011] NCF1C is a diagnostic marker that distinguishes leprosy patients from those with family contact, while CCL2 is a diagnostic marker that distinguishes different types of leprosy patients.

[0012] IL-8, CCL2 / MCP-1, and SERP, three genomes, work together as diagnostic biomarkers for leprosy.

[0013] The present invention also provides reagents for detecting diagnostic biomarkers of leprosy and their application in early leprosy kits.

[0014] A reagent for detecting one or more of the following 12 genomes at the RNA expression level or cytokine protein level: CCL2 / MCP-1, IL-8, JAKM, ATP, ND1, SERP, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22, and NCF1C.

[0015] The primer sequences for the 12 biomarker genes are as follows:

[0016] Biomarker Real-time Quantitative PCR Primer Sequence

[0017]

[0018]

[0019]

[0020] Antibodies against the 12 biomarkers were used to detect the biomarkers using Enzyme-Linked Immuno-Sorbant Assay (ELISA) or multiplex arrays; alternatively, qRT-PCR was used with nucleotide primers targeting the above biomarkers to detect changes in the expression RNA levels of the above biomarker genes in host peripheral blood mononuclear cells (PBMCs) after stimulation with leprosy antigen to diagnose leprosy.

[0021] The reagent for detecting 12 biomarkers is used in the preparation of a rapid blood test kit for early leprosy.

[0022] Among them, reagents for detecting five genomic combinations of IL-8, CCL2 / MCP-1, SERP, LINC00659, and FLJ10489 were used to prepare diagnostic kits to distinguish between leprosy patients and healthy individuals in endemic areas.

[0023] Reagents for detecting IL-8, CCL2 / MCP-1, SERP, and LINC00659 are used to prepare diagnostic kits for multibacterial strains; reagents for detecting IL-8, CCL2 / MCP-1, SERP, and FLJ10489 are used to prepare diagnostic kits for oligobacterial strains.

[0024] The NCF1C detection reagent was used to prepare a diagnostic kit to differentiate between leprosy patients and those with family contact.

[0025] The CCL2 detection reagent was used to prepare a diagnostic kit to differentiate between different leprosy patients.

[0026] A reagent for detecting IL-8, CCL2 / MCP-1, and SERP, a combination of three genes, was used to prepare a diagnostic kit for leprosy.

[0027] A kit comprising reagents for detecting one or more of the following 12 genomes at the RNA expression level or cytokine protein level: CCL2 / MCP-1, IL-8, JAKM, ATP, ND1, SERP, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22, and NCF1C.

[0028] The mRNA expression level was detected using real-time quantitative PCR (qRT-PCR), and the cytokine level was determined using an antigen-antibody detection method.

[0029] The RNA expression level of peripheral blood mononuclear cells (PBMCs) after stimulation with leprosy antigen was detected by quantitative real-time PCR (qRT-PCR) (primer sequences are listed in the sequence listing), while the cytokine level was determined by antigen-antibody detection methods such as enzyme-linked immunosorbent assay (ELISA) or multiplex arrays.

[0030] This invention uses RNA sequencing (RNA-seq) to identify candidate host biomarkers that are differentially expressed in peripheral blood mononuclear cells from patients with multibacterial and oligobacterial leprosy stimulated by Mycobacterium leprae, as well as non-leprosy controls (including healthy family contacts and healthy controls). The differentially expressed genes identified in this study can serve as useful biomarkers and provide a good foundation for the development of rapid blood tests for the diagnosis of leprosy.

[0031] This invention identifies a specific host transcriptome in peripheral blood mononuclear cells (PBMCs) to differentiate between leprosy patients and non-leprosy patients, thereby enabling early diagnosis of the disease. Methods: The cohort included 16 patients: 8 leprosy patients (4 with multibacillary leprosy and 4 with sparse leprosy) and 8 non-leprosy controls (4 healthy family contacts and 4 healthy controls from endemic areas). Differences in the transcriptome response of PBMCs to Mycobacterium leprae antigens were investigated between leprosy patients and the non-leprosy control group. Results: Twelve differentially expressed genes were identified (CCL2 / MCP-1, IL-8, JAKM, ATP, ND1, SERP, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22, and NCF1C). In a validation cohort of 82 individuals (13 multibacillary leprosy patients, 10 oligobacillary leprosy patients, 37 family contact controls, and 22 healthy controls), the accuracy of 12 differentially expressed genes was further validated using real-time quantitative PCR. We found that the combination of five genes—IL-8, CCL2 / MCP-1, SERP, LINC00659, and FLJ10489—performed well in distinguishing leprosy patients from healthy individuals in endemic areas. Furthermore, compared to healthy individuals in endemic areas, elevated expression of IL-8, CCL2 / MCP-1, SERP, and LINC00659 was associated with the diagnosis of multibacillary leprosy, while elevated expression of IL-8, CCL2 / MCP-1, SERP, and FLJ10489 was found to be useful markers for oligobacillary leprosy. Additionally, we found that NCF1C expression in leprosy patients could distinguish leprosy patients from family contacts, while CCL2 expression could differentiate between different types of leprosy patients. In summary, among the 12 candidate genes identified, the combination of IL-8, CCL2 / MCP-1, and SERP genes showed the best performance in distinguishing leprosy patients from healthy controls. These findings may have implications for developing a rapid blood test for the early diagnosis of leprosy. Attached Figure Description

[0032] Figure 1 Flowchart for screening biomarkers for early diagnosis of leprosy.

[0033] Figure 2 This is a map of differentially expressed genes after RNA sequencing. The number of expressed transcripts changed in leprosy (MB+PB) patients compared to non-leprosy controls. DEGs: differentially expressed genes; RNA-seq: RNA sequencing; MB: multibasal leprosy patients; PB: sparse leprosy patients; HHC: healthy family contacts; EC: healthy controls from endemic areas.

[0034] Figure 3 3D Wien diagram of differentially expressed genes between different leprosy patient groups and control groups;

[0035] Among them, 3A is the Venn diagram of DEGs between leprosy patients and ECs, HHCs, and non-leprosy controls; 3B is the Venn diagram of DEGs between MB-type leprosy patients and ECs, HHCs, and PB-type leprosy patients; 3C is the Venn diagram of DEGs between PB-type leprosy patients and ECs, HHCs, and MB-type leprosy patients. MB: multibacillary leprosy patients; PB: oligobacillary leprosy patients; HHC: healthy family contacts; EC: healthy controls in endemic areas.

[0036] Figure 4 A heatmap of genetic differences between patients and healthy controls in an epidemic area;

[0037] Among them, there are: 4A. Genetic heatmap showing the difference between multibacillary leprosy patients and their family contacts; 4B. Genetic heatmap showing the difference between leprosy patients and healthy controls in endemic areas; 4C. Genetic heatmap showing the difference between multibacillary leprosy patients and healthy controls in endemic areas; and 4D. Genetic heatmap showing the difference between leprosy patients and healthy controls in endemic areas.

[0038] Figure 5 Volcano diagram showing differential gene expression between leprosy patients and non-leprosy patients.

[0039] The logarithmic fold change of an individual gene (x-axis) corresponds to the p-value (y-axis) of the negative logarithm to base 10. Compared to non-leprosy controls, a positive log2 (fold change) value represents upregulation of gene expression in leprosy patients, while a negative value represents downregulation. The circles above the dashed lines represent differentially expressed genes between leprosy patients and the control group. Red indicates upregulation, and green indicates downregulation.

[0040] Figure 6 Results of differential gene validation.

[0041] To validate gene expression levels in PBMCs stimulated by Mycobacterium leprae in a cohort. Differences in the expression levels of ATP, CCL2 / MCP-1, IL-8, JAKM, MTND1, SERP, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22, and NCF1C in (AL)M-stimulated PBMCs from leprosy patients, HHCs, and ECs were investigated. Statistical differences were assessed using the Mann-Whitney test for nonparametric data. MB: multibacillary leprosy patients; PB: sparse leprosy patients; HHC: healthy family contacts; EC: healthy controls from endemic areas; DEG: differentially expressed genes; PBMCs: peripheral blood mononuclear cells.

[0042] Figure 7 ROC curves of different patients compared with those in epidemic areas;

[0043] In the validation trials, the performance of each selected gene was tested in PBMCs from leprosy patients and non-leprosy control groups. The area under the ROC curve showed the sensitivity and specificity of differentially expressed genes in the PBMC samples. ROC curves for the selected genes in leprosy patients and endemic area controls in validation trial 7A; ROC curves for the selected genes in leprosy patients and endemic area controls in validation trial 7B; and ROC curves for the selected genes in leprosy patients and endemic area controls in validation trial 7C.

[0044] Figure 8 Decision tree results for combined gene expression analysis;

[0045] in,

[0046] The combined expression levels of 8A IL8 and FLJ10489 were used to differentiate between leprosy patients and healthy individuals using a decision tree. The study included 22 leprosy patients and 22 healthy individuals. The sensitivity, specificity, and accuracy were all 90.9%.

[0047] The combined expression levels of 8B SERP and JAKM were used in a decision tree to distinguish between leprosy patients and healthy individuals. The study included 22 leprosy patients and 22 healthy individuals. The sensitivity was 81.8%, specificity was 95.5%, and accuracy was 88.6%.

[0048] The combined expression levels of 8C MIR22 and ATP6 were used in a decision tree to distinguish between leprosy patients and long-term contacts. The study included 22 leprosy patients and 19 long-term contacts. The sensitivity was 77.3%, the specificity was 84.2%, and the accuracy was 80.5%. Detailed Implementation

[0049] In October 2014, we recruited 8 leprosy patients (4 multibacillary and 4 oliguric) and 8 non-leprosy controls (4 family-contact controls and 4 healthy controls) from Honghe Tujia and Miao Autonomous Prefecture, Yunnan Province, as an exploratory cohort. All participants in this study were of the same ethnicity. Among the recruited patients, there were 3 men and 5 women. From February 2014 to May 2016, 82 individuals (13 multibacillary, 10 oliguric, 37 family-contact controls, and 22 healthy controls) were recruited from the same region for validation. Leprosy patients were classified using the Ridley-Jopling classification (Ridley and Jopling, 1966) and divided into multibacillary and oliguric groups according to the World Health Organization classification (Reibel et al., 2015). Basic information and clinical characteristics of the leprosy patients were recorded. The median and interquartile range (IQR) of treatment duration for patients with multibacillary and oliguric leprosy were 4 months (range 1–7 months) and 1 month (range 1–5 months), respectively. The age at diagnosis ranged from 21 to 59 years, with a median of 39 years. The exploratory cohort included 10 men and 6 women, while the validation cohort included 45 men and 37 women. Family contact controls lived in the same household as adult leprosy patients. Healthy controls served as a normal control group living in the same community as leprosy patients (Table 1).

[0050] Table 1 Clinical information of leprosy patients and controls

[0051]

[0052] World Health Organization: Leprosy classification proposed by the World Health Organization

[0053] Number of patients; RJ Ridley-Jopling classification; BL: marginal leprosy; BT: marginal tuberculoid; TT: tuberculoid; IQR: interquartile range; MB: multibacillary leprosy; PB: seldom bacterioid leprosy; HHC: healthy family contacts; EC: healthy controls in endemic areas.

[0054] The whole-cell ultrasound antigen of Mycobacterium leprae was obtained from Colorado State University.

[0055] 1. Blood sample and patient PBMC collection

[0056] Peripheral blood (15 mL) was placed in an EDTA tube. Ficoll-Paque separation was performed using a CEDARLANE (Catno. CL5020). Simply put, peripheral blood was centrifuged at 3000×g for 10 minutes to obtain patient PBMCs. The supernatant containing plasma was removed, and the PBMCs were separated using an equal volume of RPMI-1640 (Invitrogen-GIBCO). A total of 8 mL of the diluted PBMCs was layered onto 4 mL of Ficoll-Paque (CEDARLANE). The layers were then centrifuged at 3000×g for 20 minutes at 20–30°C. Patient PBMCs were carefully pipetted and washed. Cell count and viability were determined using an automated cell counter (Invitrogen). Non-viable cells were identified by trypan blue staining, and cell viability was calculated using the total viable cell count and adjusted to 2×10⁻⁶ cells in the RPMI-1640. 6 Cells / mL. The cells were then divided into two aliquots (990 μL per well in a 12-well plate), and 10 μL (1 mg / mL) of Mycobacterium leprae whole-cell sonication antigen was added to each well. The cells were incubated at 37°C for 12 h.

[0057] 2. Total RNA extraction and RNA sequencing

[0058] Total RNA was extracted from patient PBMCs (Life Technologies, Grand Island, NY) preserved in trizol, following the manufacturer's instructions described previously (Yuan et al., 2017). To determine RNA concentration, absorbance at 260 nm was measured using a Nanodropone spectrometer (Thermo Fisher Scientific Inc., Waltham, MA). RNA integrity number (RIN) and a 28S:18S ratio of 7.0 and 1.8 were determined for subsequent experiments.

[0059] 3. Preparation and sequencing of cDNA libraries

[0060] Library and RNA-seq construction were performed by Shanghai Novel Bioinformatics Company (China). Then, following the protocol proposed by Illumina, a cDNA library with an insert size of 150 bp was prepared. The cDNA library was sequenced on the Illumina HiSeq 2000 genome sequencing platform at Shanghai Novel Bioinformatics Company.

[0061] 2.7 RNA-seq Data Analysis

[0062] Differentially expressed genes between samples were analyzed using the DESeq algorithm (Anders et al., 2010). A p-value was then obtained and corrected for using the false discovery rate method (Benjamini Y, et al., 2001). Significant differences were classified as parameters ≥ 2-fold difference and transcript abundance ≥ 5000 raw reads.

[0063] Functional annotations of differentially expressed genes were determined by searching the National Center for Biotechnology Information (NCBI), UniProt, GO (gene ontology), and KEGG databases (http: / / www.genome.jp / KEGG / ) and performing alignments using the BLAST (Basic Local Alignment Search Tool). The best match was selected to annotate the differentially expressed genes. Finally, GO and KEGG functional analyses were performed on the differentially expressed genes using default parameters to identify the main GO and KEGG categories of the differentially expressed genes.

[0064] 4. Validate RNA-Seq data using RT-qPCR.

[0065] To validate RNA-seq data, RT-qPCR was performed using the SYBR Premix Ex Taq™ II Kit (Perfect RealTime). Sixteen RNA samples used in the discovery phase were also used for validation RNA-seq analysis. Primers were screened and analyzed using PrimerPremier software (version 5.0) (Supplementary Table S1). The reaction mixture contained 6.25 μL of SYBR Premix Ex Taq™ II (2×), 0.25 μL of ROX Reference Dye II (50×), 1 μL of 10 μM primer mixture, 1 μL of cDNA, and water, for a final volume of 12.5 μL. Cycling conditions were: 95℃ for 30 s, 95℃ for 5 s, and 60℃ for 34 s. All RT-qPCR experiments were performed on an Applied Biosystems 7500 Fast real-time PCR system (Applied Biosystems), using three biological replicates. GAPDH mRNA was used as an internal control, and the fold change was calculated using the 2-ΔΔCT method (where ΔCt = Ct mRNA - Ct GAPDH, ΔΔCt = ΔCtstimulated - ΔCt unstimulated).

[0066] 5. Statistical Analysis

[0067] Statistical analysis was performed using GraphPad Prism 8.0 software (GraphPad software Inc., San Diego, CA, USA) and SPSS 25.0 software. The nonparametric Mann-Whitney u test was used to analyze differences between the two groups. A p-value < 0.05 was considered statistically significant.

[0068] 6 Results

[0069] 3.1 Overview of the research design and basic characteristics of the participants

[0070] Our research follows a two-step design ( Figure 1 First, we included 8 leprosy patients (4 with multibacillary leprosy and 4 with oligobacillary leprosy) and 8 non-leprosy controls (4 family-exposed controls and 4 healthy controls). By comparing gene expression stimulated by leprosy antigen in whole blood microcirculation cells (PBMCs) of the two groups (leprosy patients and non-leprosy controls) using RNA-seq (all subjects' PBMCs were stimulated with Mycobacterium leprae ultrasound antigen; differentially expressed genes were identified in RNA-seq analysis with a fold change ≥2, P < 0.05, and raw reads ≥ 5,000), we obtained a set of differentially expressed genes between leprosy patients and controls. Next, we utilized differentially induced upregulated genes (RNA obtained from PMCs stimulated with Mycobacterium leprae antigen) in a validation cohort of 23 leprosy patients (13 polybacillary and 10 oligobacillary) and 59 non-leprosy controls (37 family contact controls and 22 healthy controls). Selected genes were validated by RT-qPCR as diagnostic biomarkers for leprosy. Figure 1 ).

[0071] Table 1 lists the demographic and clinical characteristics of the cohorts. In the exploratory cohort, the median treatment duration and IQR for multibacillary and oliguric leprosy patients were 4 months (1–7 months) and 1 month (1–5 months), respectively. In the validation cohort, the median treatment duration and IQR for multibacillary and oliguric leprosy patients were 5.5 months (1–7 months) and 3.5 months (1–7 months), respectively.

[0072] 3.2 RNA-seq analysis of differences in PBMC transcriptomes between leprosy patients and control patients stimulated by leprosy antigens.

[0073] We analyzed the differentially expressed genes in 16 samples using RNA-seq. Figure 2When the transcriptome profiles of leprosy patients were compared with those of non-leprosy controls, a total of 423 differentially expressed genes were obtained, of which 260 and 163 were upregulated and downregulated, respectively. Furthermore, 723 and 314 differentially expressed genes were obtained from multibacillary and seldombollary leprosy patients, respectively. Compared with non-leprosy controls, 272 and 232 differentially expressed genes were upregulated in multibacillary and seldombollary leprosy patients, respectively, while 451 and 82 differentially expressed genes were downregulated. Figure 2 ).

[0074] 3.3 Differentially expressed genes between leprosy patients and non-leprosy controls may serve as potential diagnostic biomarkers.

[0075] We used Venn diagram analysis to select genes with the highest differential expression, such as Figure 4 The image shown is a heatmap. We found that 27 differentially expressed genes were upregulated in leprosy patients compared to healthy controls. Figure 4 A), 45 differentially expressed genes were upregulated in patients with multifocal serotype compared to healthy controls. Figure 4 B), 16 differentially expressed genes were upregulated in patients with seldom bacteriophageal leprosy compared to healthy controls. Figure 4 C). Compared with family exposure controls, 18 differentially expressed genes were upregulated in patients with multibacillary leprosy (MBL). Figure 4 D). See Supplementary Table S2 and [other tables] for more details. Figure 5 .

[0076] 3.4 Performance of the selected differentially expressed genes in distinguishing leprosy patients from the control group in the validation cohort.

[0077] To validate the relevance of the above genes, we evaluated their performance in a validation cohort comprising 23 leprosy patients (13 polybacillary and 10 oligobacillary) and 59 non-leprosy controls (37 family contact controls and 22 healthy controls). Based on RNA-seq results showing a more than 2-fold increase in leprosy patients compared to non-leprosy controls, and subsequent RT-PCR confirmation, the following 12 genes were identified and evaluated as potential leprosy diagnostic biomarkers: CCL2 / MCP-1, IL-8, JAKMIP2, ATP, ND1, SERPINB2, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22, and NCF1C. Supplementary Table S3 lists the accession numbers / IDs of the genes mentioned in this paper and those included in the NCBI search as potential diagnostic biomarkers. Receiver operating characteristic (ROC) curve analysis of 23 leprosy patients and 22 healthy controls revealed the following performance of candidate genes: Area under the curve (AUC) was 0.9070 (95% confidence interval [CI] 0.8164–0.9976), with a sensitivity of 81.82% and a specificity of 95.45%, including an 18.2-fold change in IL-8; AUC was 0.8662 (95% CI 0.7545–0.9778), with a sensitivity of 90% and a specificity of 95.45%, including a 56.8-fold change in CCL2; AUC was 0.8182 (95% CI 0.6813–0.9551), with a sensitivity of 85.71% and a specificity of 81.82%, including a 10.5-fold change in SERPs; AUC was 0.7803 (95% CI 0.6217–0.9388). The sensitivity was 77.3%, and the specificity was 68.2% for FLJ10489, with a fold change of 5.7 (%). Figure 7 (See Tables 2 and 3). Furthermore, we evaluated the validation ability of these genes in 23 leprosy patients and 37 healthy controls. We found that NCF1C had an AUC of 0.8448 (95% CI 0.7165–0.9731), a sensitivity of 72.22%, a specificity of 82.35%, and a fold change of 3.8 (Table 2). Using a decision tree approach, we performed combined gene diagnosis to differentiate leprosy patients from healthy individuals. We found that the IL8 and FLJ10489 genes showed the best combined diagnostic effect for both leprosy patients and healthy individuals, with a sensitivity of 90.9%, a specificity of 90.9%, and a differentiation accuracy of 90.9%. The combined diagnostic results are shown in Table 3 below. Figure 8A and 8B. In addition, combined genetic diagnosis was used to differentiate leprosy patients from long-term contacts. Evaluation showed that the combined diagnosis of MIR22 and ATP6 genes was most effective, with a sensitivity of 77.3%, a specificity of 84.2%, and an accuracy of 80.5% in differentiating between patients and long-term contacts. The positive predictive value was 85%, and the negative predictive value was 84.2%. The combined diagnostic results are shown in Table 4. Figure 8 C. In summary, CCL2 and NCF1C showed good differentiation between leprosy patients and healthy controls. For details on the characteristics of the candidate genes, please refer to Supplementary Table S4 and... Figure 7 AC.

[0078] Table 2. Diagnostic efficacy of the selected single genes in leprosy patients and different control groups.

[0079]

[0080] PBMCs: Peripheral blood mononuclear cells; RT-qPCR: Reverse transcription quantitative polymerase chain reaction; MB: Multibacterial type patients; PB: Less bacterial type patients; HHC: Healthy family contacts; EC: Healthy controls from endemic areas; mRNA: Messenger RNA; lncRNA: Long non-coding RNA; AUC: Area under the curve; CI: Confidence interval. *: p<0.05

[0081] Table 3. Diagnostic effects of different gene combinations on leprosy patients and healthy individuals.

[0082]

[0083] Table 4. Diagnostic efficacy of different gene combinations in distinguishing leprosy patients from long-term contacts.

[0084]

[0085] 4 Discussion

[0086] In this study, we included patients with polybacillary and oligobacillary leprosy, family contact controls, and healthy controls. We stimulated their PBMCs with leprosy antigens, followed by RNA-seq, and identified a specific gene combination (CCL2 / MCP-1, IL-8, JAKM, ATP, ND1, SERP, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22, and NCF1C) that may be useful for rapid diagnosis of leprosy. These candidate genes were subsequently validated in a separate validation cohort using PBMCs from polybacillary and oligobacillary leprosy patients, family contact controls, and healthy controls with oligobacillary leprosy. We found that elevated expression of five genes—IL-8, CCL2 / MCP-1, SERP, LINC00659, and FLJ10489—could distinguish leprosy patients from healthy controls. Furthermore, increased expression of IL-8, CCL2 / MCP-1, SERP, and LINC00659 genes was associated with the diagnosis of multibacillary leprosy, while upregulation of IL-8, CCL2 / MCP-1, SERP, and FLJ10489 was found to be useful biomarkers for the diagnosis of oliguric leprosy. In addition, we evaluated gene expression between leprosy patients and family-contact controls. The results showed that NCF1C expression was reduced in leprosy patients, distinguishing them from family-contact controls. Furthermore, CCL2 expression was higher in multibacillary leprosy patients than in oliguric leprosy patients, further differentiating them from other leprosy patients. To our knowledge, this is the first report on the long non-coding RNA NCF1C that distinguishes leprosy patients from healthy controls.

Claims

1. A biomarker used to distinguish leprosy from healthy individuals in endemic areas, composed of 5 genes: IL-8, CCL2 / MCP-1, SERP, LINC00659 and FLJ10489.

2. A reagent for detecting the leprosy diagnostic biomarker of claim 1, comprising the following primers: 。 3. The application of the reagent for detecting five genomic combinations as described in claim 2 in the preparation of a diagnostic kit to distinguish between leprosy patients and healthy individuals in endemic areas.

4. According to the application described in claim 3, the detection reagents for IL-8, CCL2 / MCP-1, SERP, and LINC00659 are used for the diagnosis of polybacillary leprosy; the detection reagents for IL-8, CCL2 / MCP-1, SERP, and FLJ10489 are used for the diagnosis of oligobacillary leprosy.

5. A kit comprising reagents for detecting the biomarker of claim 1 at RNA expression levels or cytokine protein levels.

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