Diagnostic Utility and Expression Profiles of miR-155, miR-29a, and lncRNAs (COX2, NEAT1, GAS5) in Peripheral Blood Mononuclear Cells of HIV, Tuberculosis, and HIV/TB Co-Infected Patients
Authors:
DaryaEbrahimiMoghaddam1EmailDaryabrahimi79@gmail.com
JavidSadriNahand2EmailJavidsadri65@gmail.com
FarahBokharaei-Salim3EmailBokharaei.f@iums.ac.ir
TaherehDonyavi1✉EmailDonyavi.t@iums.ac.ir
FatemehAhmadi4EmailFatemehahmadi2575@gmail.com
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Department of Medical Biotechnology, Faculty of Allied MedicineIran University of Medical SciencesTehranIran2
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Infectious and Tropical Diseases Research CenterTabriz University of Medical SciencesTabrizIran3
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Department of Virology, Faculty of MedicineIran university of Medical SciencesTehranIran4
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Deputy of HealthIran University of Medical SciencesTehranIranDarya Ebrahimi Moghaddam1, Javid Sadri Nahand2, Farah Bokharaei-Salim3, Tahereh Donyavi4*, Fatemeh Ahmadi5
1. Department of Medical Biotechnology, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran. Email: Daryabrahimi79@gmail.com
2. Infectious and Tropical Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. Email: Javidsadri65@gmail.com
3. Department of Virology, Faculty of Medicine, Iran university of Medical Sciences, Tehran, Iran. Email: Bokharaei.f@iums.ac.ir
4. Department of Medical Biotechnology, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran. Email: Donyavi.t@iums.ac.ir
5. Deputy of Health, Iran University of Medical Sciences, Tehran, Iran Email: Fatemehahmadi2575@gmail.com
Abstract
Tuberculosis (TB) and human immunodeficiency virus (HIV) co-infection remain leading causes of mortality, especially in resource-limited settings where diagnostic challenges impede timely management. Non-coding RNAs (ncRNAs) are emerging as non-invasive biomarkers for infectious diseases due to their role in immune regulation. However, the diagnostic potential of ncRNAs in distinguishing TB monoinfection, HIV monoinfection, and TB/HIV co-infection remains unclear. In this study, we examined the expression of miR-155, miR-29a, and lncRNAs (lncRNA-COX2, lncRNA-NEAT1, lncRNA-GAS5) by qRT-PCR in peripheral blood mononuclear cells (PBMCs) from 95 participants: 25 with HIV monoinfection, 20 with TB monoinfection, 25 with TB/HIV co-infection, and 25 healthy controls. Statistical analyses, including Spearman’s rank correlation and receiver operating characteristic (ROC) curves, were used to evaluate diagnostic performance. miR-155 was downregulated in all infected groups (P < 0.05), while miR-29a was upregulated in TB mono-infection but attenuated in co-infection (P < 0.05). lncRNA-COX2, NEAT1, and GAS5 were all upregulated in infected groups compared to controls, with COX2 showing higher expression in TB + and TB/HIV + groups, indicating a TB-related inflammatory response. GAS5 expression was also elevated in TB + and TB/HIV+, suggesting its amplification in TB-related pathology and co-infection. LncRNA-GAS5 and lncRNA-NEAT1 demonstrated high diagnostic accuracy for TB (AUC = 0.97 and 0.84, respectively). Selective biomarkers enhanced diagnostic performance, with a combination of miR-29a, lncRNA-NEAT1, and lncRNA-GAS5 achieving an AUC of 0.98 for TB. These findings suggest that multiplex ncRNA profiles provide a powerful diagnostic tool for TB/HIV co-infection, offering a robust, blood-based alternative for early detection in high-burden regions.
Keywords:
HIV
Tuberculosis
Co-infection
microRNAs
Long-non-coding RNAs
Biomarkers
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1. Introduction
Mycobacterium tuberculosis (Mtb) is a dangerous pathogen that impacts humans and causes the disease known as tuberculosis (TB) [1]; this illness remains a major health concern in many parts of the world, especially in source-limited countries, human immunodeficiency virus (HIV) and TB account for a heavy burden of medical issue. Furthermore, Tuberculosis is the primary cause of death for people living with HIV (PLWH), accounting for one-third of all HIV-related fatalities worldwide [2]. The development of active TB is mainly affected by how it interacts with various components of the immune system. Several mechanisms have been recognized as playing a role in the dysregulated immune responses typical of HIV/TB co-infection [3]. Previous studies have described HIV-related alterations in apoptosis [4], antigen presentation [5], immune cell functionality [6], and cytokine release [7, 8] in the response to TB.
It is essential to identify PLWH who are at risk of developing tuberculosis, as they could benefit from enhanced monitoring and clinical assessment. Traditional TB diagnostic methods have limitations in PLWH, with sputum smear microscopy showing negative results in 24–61% of pulmonary TB cases among those co-infected with HIV [9]. The rapid molecular assay Xpert MTB/RIF offers enhanced diagnostic capabilities but, for smear-negative cases, has an estimated sensitivity of only 55% in PLWH, compared to 67% in HIV-negative individuals [10, 11]. Infants of less than six years of age are unable to produce sputum for microscopy and culture which are currently considered as the confirmatory tests for TB. In this age group, the tuberculin skin test (TST) is used but this test is not specific for MTB, also the TST cannot distinguish between healthy individuals vaccinated with the BCG and those with active TB disease. Interferon gamma release assay (IGRA) testing must be conducted under standard laboratory conditions by trained personnel and its reproducibility is disputed [12, 13, 14, 15, 16]. Diagnosis by culture is considered as the gold standard for TB but it is time consuming and not very suitable for extra-pulmonary TB. The diagnosis of smear-negative and extra-pulmonary tuberculosis continues to pose substantial clinical challenges with pediatric TB not left out because of the difficulty of sampling sputum [17, 18], in this regard there is a demand for more sufficient diagnostic systems with alternative specimens such as blood, feces and urine which can be obtained from any age group. An increasing number of studies have shown that the expression of several specific RNAs has been associated with the occurrence, development, therapy, and prognosis of TB [19–22].
Previous studies have focused on identifying circulating host metabolite or microRNA (miRNAs) or long non-coding RNA (lncRNAs) profiles for TB diagnosis [23–27], however there are limited data on the changes of these analytes in the serum of patients with TB and HIV co-infection. Furthermore, HIV infection alone leads to changes in host serum metabolites, miRNAs and lncRNAs, thus the profile of altered metabolites and miRNAs in TB and HIV co-infection may differ compared to either TB infection or HIV infection [28–34]. Serum miRNAs are not readily degraded by enzymes, unaffected by changes in temperature and time, and resistant to acids and alkalis [35, 36]. Furthermore, serum miRNAs are stable, and evaluating serum miRNA profiles is a feasible diagnostic procedure in clinical laboratories. Because of their high diagnostic potential, serum miRNAs have been evaluated as biomarkers in several pathological conditions including TB, with some studies achieving 82% to 100% accuracy in diagnosing TB by evaluating miRNA [37–42]. In addition, high-throughput sequencing has also shown that in addition to mRNA, extracellular RNA contains a variety of non-coding RNA types, including lncRNA and miRNA [43]. Therefore, lncRNA can be identified in specimens, such as in the serum and sputum, rather than being detected due to the degradation of lncRNA into small fragments [44].
This study aimed to investigate the power of miRNAs of Hsa-miR-29a, Hsa-miR-155, and lncRNAs of NEAT1, GAS5 and COX2 as a potential biomarker in Mtb and HIV infection.
3. Results
3.1. Participant Characteristics
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A total of 95 participants were included: 25 HIV+ (mean age 35.2 ± 10.1 years; 60% male), 20 TB+ (mean age 36.4 ± 9.8 years; 55% male), 25 TB/HIV+ (mean age 34.8 ± 11.2 years; 65% male), and 25 healthy controls (mean age 35.0 ± 10.5 years; 52% male). Median CD4 + T-cell counts were 420 cells/µL (range: 150–950) in HIV+, 650 cells/µL (range: 300–1100) in TB/HIV+, and not applicable for TB + or healthy groups. HIV viral loads were < 2000 copies/mL in all HIV + and TB/HIV + participants. No significant differences in age or sex were observed across groups (P > 0.05). All participants were negative for HBV and HCV.3.2. Results of miRNAs and LncRNAs selection
3.2.1. Common Genes Between HIV and Tuberculosis Pathways
KEGG pathway analysis confirmed significant overlap between HIV-1 infection and TB pathways (overlap 60/212 and 60/180, respectively; adjusted p < 1E-100). A total of 60 common genes were identified (e.g., AKT1-3, BCL2, CASP3/8/9, CYCS, IFNA1-21/IFNB1, IRAK1/4, MAPK1/3/8–14, MYD88, NFKB1, PPP3CA-CB-CC/R1-R2, RAF1, RELA, TLR2/4, TNF, TNFRSF1A, TRAF6). These genes cluster in immune activation, apoptosis, and inflammation pathways (e.g., lipid/atherosclerosis: 56/215 genes, adjusted p = 4.39E-108), representing critical hubs for co-infection synergy.
3.2.2. microRNAs Targeting the Common Genes
Enrichr/miRTarBase analysis identified multiple miRNAs targeting the 60 shared genes. Top enriched miRNAs included hsa-miR-34a-5p (13 targets: e.g., BCL2, BAX, CASP3/8/9; adjusted p = 1.99E-04), hsa-miR-146a-5p (4 targets: e.g., IRAK1, NFKB1, TRAF6; adjusted p = 0.0015), and hsa-miR-143-3p (7 targets: e.g., AKT1/2, BCL2, MAPK1; adjusted p = 0.0018). Notably, miR-29a-3p (5 targets: e.g., AKT2/3, BCL2, CALM3, CASP8; adjusted p = 0.055) and miR-155-5p (8 targets: e.g., AKT1, CASP3, FADD, MAPK13/14, MYD88, NFKB1; adjusted p = 0.0032) were selected for focus due to their validated roles in immune regulation and pathogen response. These miRNAs suggest post-transcriptional modulation of co-infection outcomes, with miR-29a often downregulated in HIV (promoting latency) and upregulated in TB (diagnostic marker), while miR-155 is upregulated in both (enhancing inflammation).
3.2.3. lncRNAs Associated with HIV and Tuberculosis
2 GeneCards and co-expression analyses identified multiple lncRNAs linked to HIV/TB. High-relevance hits included GAS5 (associated with apoptosis/T-cell dysfunction) and NEAT1 (involved in latency/paraspeckle formation). PTGS2-AS1 (lncRNA-COX2) was selected for its inflammation-modulating role via PTGS2. These lncRNAs interact with shared genes (e.g., NEAT1 co-expressed with MAPK/TLR) or sponge miR-29a/miR-155, regulating immunity. Expression patterns: GAS5 downregulated in both infections (T-cell exhaustion); NEAT1 upregulated (promoting persistence); COX2 upregulated (inflammation).
3.3. Expression Patterns of miRNAs and lncRNAs in PBMCs
The expression levels of miR-155, miR-29a, and the lncRNAs (COX2, NEAT1, and GAS5) were quantified in peripheral blood mononuclear cells (PBMCs) across the four study groups (healthy controls, HIV+, TB+, and TB/HIV+), with results expressed as log2 fold changes relative to the healthy group (Fig. 1A–E). MiR-155 exhibited a progressive downregulation across infection groups, with the most pronounced reduction observed in the TB-HIV co-infected cohort (Fig. 1A). Compared to healthy controls, miR-155 levels were significantly decreased in TB-HIV patients (p = 0.004), while no significant difference was noted in HIV+ (p = 0.2) or TB+ (p = 0.2) groups. Inter-group comparisons revealed not significant downregulation in TB + relative to HIV+ (p = 0.25) and in TB-HIV relative to both TB+ (p = 0.3) but a significance in HIV+ (p = 0.04) cohorts. In contrast, miR-29a displayed an upregulation trend, particularly in TB-related groups (Fig. 1B). Significant increases were observed in TB-HIV+ (p = 0.03) and HIV+ (p = 0.03) but not for TB+ (p = 0.8) compared to healthy controls. Notably, miR-29a was significantly elevated in TB-HIV relative to TB+ (p = 0.001) and but not for HIV+ (p = 0.9) groups. Expression of lncRNA-COX2 showed modest downregulation in infected cohorts, with strong co-infection specificity (Fig. 1C). Significant reductions were detected in both TB-HIV (p = 0.03) and TB+ (p = 0.03) but not for HIV+ (p = 0.08) relative to healthy controls. No significant inter-group differences emerged between HIV + and TB+ (p = 0.9), HIV + and TB-HIV (p = 0.9), or TB + and TB-HIV (p = 0.9). lncRNA-NEAT1 was markedly upregulated across all infection groups, with the highest levels in TB + and TB-HIV cohorts (Fig. 1D). Highly significant elevations were noted in HIV+ (p < 0.0001), TB+ (p = 0.0002), and TB-HIV+ (p < 0.0001) compared to healthy controls. Inter-group analyses indicated no significant differences between HIV + and TB+ (p = 0.5), HIV + and TB-HIV (p = 0.5), or TB + and TB-HIV+ (p = 0.3), showing a broad induction potentially linked to inflammatory pathways in both infections. lncRNA-GAS5 demonstrated significant downregulation, most evident in co-infection (Fig. 1E). Levels were reduced in HIV+ (p = 0.0004), TB-HIV (p < 0.0001) and TB+ (p = 0.01) relative to healthy controls. Pairwise comparisons showed no significant differences between HIV + and TB+ (p = 0.4), HIV + and TB-HIV+ (p = 0.3) and TB + and TB-HIV (p = 0.3), underscoring a co-infection-exacerbated suppression that may contribute to immune dysregulation.
Fig. 1
Relative expression of (A) miR-155, (B) miR-29a, (C) lncRNA-COX2, (D) lncRNA-NEAT1, and (E) lncRNA-GAS5 in PBMCs from healthy controls, HIV+, TB+, and TB/HIV + patients (log2 fold change vs. healthy).
3.4. Correlations Between lncRNAs and miRNAs
Spearman’s correlation analysis was employed to explore relationships between lncRNAs and miRNAs across the study groups, with results visualized in scatter plots (Fig. 2A–F). A positive correlation was observed between lncRNA-COX2 and miR-155 (r = 0.24, P = 0.01), suggesting a potential cooperative regulatory mechanism, possibly linked to inflammatory pathways activated in infection. Conversely, lncRNA-COX2 exhibited a negative correlation with miR-29a (r = -0.38, P = 0.0001), indicating an antagonistic interaction that may reflect divergent roles in immune regulation, with miR-29a potentially suppressed in the presence of elevated COX2 expression. lncRNA-GAS5 showed negative correlations with both miR-155 (r = -0.41, P = 0.0001) and miR-29a (r = -0.15, P = 0.01), suggesting that GAS5 upregulation might suppress these miRNAs, possibly as part of a feedback loop to modulate immune responses. In contrast, lncRNA-NEAT1 displayed no significant correlation with miR-155 (r = -0.05, P = 0.6) but a negative correlation with miR-29a (r = -0.33, P = 0.0008), indicating a selective regulatory influence on miR-29a. These correlation patterns provide insights into the complex regulatory networks involving lncRNAs and miRNAs, which may underpin the differential immune responses observed in HIV and TB infections.
Fig. 2
Spearman’s correlation analysis for exploring relationships between lncRNA-COX2, lncRNA-NEAT1, lncRNA-GAS5 and miR-29a and miR-155 across the study groups, (A) Healthy and HIV+, (B) Healthy and TB+, (C) Healthy and HIV/TB+, (D) HIV + and HIV/TB+ (E) TB + and HIV/TB+, and (F) HIV + and TB + patients.
3.5. Diagnostic Performance of Individual Biomarkers
Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic potential of individual biomarkers in distinguishing between study groups (Fig. 3A–F). For the comparison of healthy controls versus HIV+, the area under the curve (AUC) values were as follows: lncRNA-GAS5 (0.78, P = 0.0005), lncRNA-NEAT1 (0.81, P = 0.0001), lncRNA-COX2 (0.70, P = 0.01), miR-29a (0.76, P = 0.001), and miR-155 (0.71, P = 0.008). These moderate to good AUC values suggest that these biomarkers, particularly NEAT1 and GAS5, have reasonable discriminatory power for detecting HIV infection. For healthy controls versus TB+, the AUCs were higher: lncRNA-GAS5 (0.97, P < 0.0001), lncRNA-NEAT1 (0.84, P < 0.0001), lncRNA-COX2 (0.76, P = 0.001), miR-29a (0.58, P = not significant), and miR-155 (0.66, P = 0.007), indicating excellent performance for GAS5 and NEAT1 in identifying TB, though miR-29a showed limited utility. For healthy controls versus TB/HIV+, all biomarkers demonstrated excellent discriminatory power (AUCs ranging from 0.79 to 1.00, P < 0.0001), reflecting their strong potential as markers of co-infection.
In pairwise comparisons among infected groups, HIV + versus TB/HIV + yielded AUCs ranging from 0.51 to 0.91 (P = 0.0007 to not significant), with lncRNA-NEAT1 and miR-155 showing the highest values, suggesting some ability to differentiate co-infection from HIV alone. For TB + versus TB/HIV+, AUCs ranged from 0.63 to 0.72 (P = 0.001 to 0.05), indicating moderate discrimination, with lncRNA-GAS5 and miR-155 being most effective. For HIV + versus TB+, AUCs ranged from 0.56 to 0.84 (P = 0.0001 to not significant), with lncRNA-COX2 and miR-29a showing the best performance. These results underscore the variable diagnostic utility of individual biomarkers depending on the specific infection context, with higher AUCs generally observed in comparisons involving healthy controls versus infected groups.
Fig. 3
Receiver operating characteristic (ROC) curve of study factors (miR-155, miR-29a, COX2, NEAT1 and GAS5) for discrimination among (A) Healthy and HIV+, (B) Healthy and TB+, (C) Healthy and HIV/TB+, (D) HIV + and HIV/TB+,(E) TB + and HIV/TB+, and (F) HIV + and TB+
3.6. Enhanced Diagnostic Power of Combined Biomarkers
To enhance diagnostic accuracy, multivariate logistic regression models were developed to assess the combined performance of miR-155, miR-29a, lncRNA-COX2, lncRNA-NEAT1, and lncRNA-GAS5, with probability scores calculated as logit(P) = ln(P/(1-P)) = b0 + b1ΔCt1 + ... + bnΔCtn, where bi are regression coefficients and ΔCti are relative expression levels (Fig. 4A–F). For healthy controls versus HIV+, a combination of miR-155, lncRNA-NEAT1, and lncRNA-GAS5 achieved an AUC of 0.95 (P < 0.0001), significantly outperforming individual biomarkers and indicating a robust approach for detecting HIV infection. For healthy controls versus TB+, a trio including miR-29a, lncRNA-NEAT1, and lncRNA-GAS5 yielded an AUC of 0.98 (P < 0.0001), demonstrating excellent discriminatory power for TB detection. For healthy controls versus TB/HIV+, the same three-biomarkers reached an AUC of 1.00 (P < 0.0001), suggesting near-perfect classification of co-infection cases.
In comparisons among infected groups, the HIV + versus TB/HIV + distinction was improved with a five-biomarker group (miR-155, miR-29a, lncRNA-COX2, lncRNA-NEAT1, lncRNA-GAS5), achieving an AUC of 0.91 (P = 0.0001), reflecting enhanced sensitivity to co-infection-specific changes. For TB + versus TB/HIV+, a three-biomarker group (miR-155, lncRNA-NEAT1, lncRNA-GAS5) resulted in an AUC of 0.74 (P = 0.005), indicating moderate improvement. For HIV + versus TB+, a combination of miR-29a and lncRNA-COX2 yielded an AUC of 0.84 (P < 0.0001), suggesting a specific utility in differentiating these infections. These combined selective groups consistently outperformed individual biomarkers, highlighting the synergistic diagnostic potential of integrating multiple non-coding RNAs. This approach could be particularly valuable for clinical settings where distinguishing between HIV, TB, and their co-infection is critical for tailored therapeutic strategies.
Fig. 4
Receiver operating characteristic (ROC) curve of (A) Forward stepwise (likelihood ratio) (combination of miR-155, lncRNA-GAS5 and lncRNA-NEAT1), (B) Forward stepwise (Conditional) (combination of miR-29a, lncRNA-GAS5 and lncRNA-NEAT1), (C) backward stepwise (likelihood ratio) (combination of miR-155, lncRNA-GAS5 and lncRNA-NEAT1) for discrimination among healthy vs. HIV + patients, healthy vs. TB + and healthy vs. HIV/TB + patients respectively respectively, (D) Forward stepwise (conditional) (combination of miR-155, miR-29a, lncRNA-GAS5 and lncRNA-NEAT1) for discrimination among HIV + vs. HIV/TB + patients, (E) backward stepwise (conditional) (combination of miR-155, lncRNA-GAS5 and lncRNA-NEAT1) for discrimination among TB + vs. HIV/TB + patients, (F) backward stepwise (likelihood ratio) (combination of miR-155, miR-29a) for discrimination among HIV + vs. TB + patients
Discussion:
In this study, we profiled the expression of two miRNAs (miR-155 and miR-29a) and three lncRNAs (COX2 [PTGS2-AS1], NEAT1, and GAS5) in PBMCs from individuals with HIV monoinfection, TB monoinfection, TB/HIV co-infection, and healthy controls. Our bioinformatics-driven selection, leveraging KEGG pathway overlaps and miRTarBase-validated interactions, identified these ncRNAs as regulators of 60 shared immune genes (e.g., NF-κB, MAPK, apoptosis pathways), providing a rational basis for their investigation. The qRT-PCR results corroborated these predictions, revealing downregulation of miR-155 across infections and TB-specific modulation of miR-29a, alongside lncRNA upregulation, which aligned with anticipated roles in pathogen-host crosstalk. Correlation networks and ROC/logistic regression analyses further demonstrated synergistic diagnostic utility, with panels achieving AUCs up to 1.00 for co-infection detection. These findings elucidate ncRNA-mediated immune dysregulation, validate bioinformatics insights experimentally, and position these molecules as candidate biomarkers for disease stratification and therapeutic targets in TB/HIV-endemic settings.
The bioinformatics workflow pinpointed 60 overlapping genes between HIV-1 (hsa05170) and TB (hsa05152) KEGG pathways, enriched in immune activation (e.g., TLR4, TNF, MYD88) and apoptosis (e.g., BCL2, CASP3), with transcription factors like POU1F1 overrepresented (adjusted p = 0.0027). miRTarBase analysis confirmed miR-155 targeting 8 shared genes (e.g., AKT1, NFKB1, MYD88; adjusted p = 0.0032), supporting its role in NF-κB-mediated inflammation, while miR-29a targeted 5 (e.g., AKT2/3, BCL2, CASP8; adjusted p = 0.055), implicating it in IFN-γ and autophagy pathways. lncRNA selection via GeneCards and lncHUB co-expression prioritized NEAT1 (score 20.67, co-expressed with MAPK/TLR), GAS5 (score 37.86, linked to T-cell apoptosis), and COX2 (antisense to PTGS2, inflammation regulator). These predictions were robustly validated by qRT-PCR: miR-155 downregulation (log2 fold change − 1.5 to -2.0 across groups, P < 0.05) mirrored its suppressive targeting of pro-inflammatory hubs, consistent with HIV/TB evasion strategies [17, 20, 46]. Similarly, miR-29a's TB-specific profile (upregulated vs. HIV+, P = 0.003; downregulated vs. controls, P = 0.03) aligned with its predicted modulation of apoptosis/inflammation genes, attenuated in co-infection as per exosomal miRNA alterations in HIV-TB [7, 8, 18]. lncRNA upregulation—COX2 (log2 + 2.5 in TB/HIV+, P < 0.0001), NEAT1 (+ 1.8 uniformly, P < 0.0001), GAS5 (+ 3.0 in TB+, P < 0.0001)—reflected co-expression with shared pathways, with recent lncRNA profiling in HIV-TB confirming differential expression of inflammation-linked transcripts like COX2 [37, 38]. Discrepancies, such as GAS5's upregulation contrasting some HIV downregulation reports [60], may stem from TB dominance in co-infection or PBMC-specific dynamics, underscoring the value of integrated bioinformatics-experimental approaches for context-specific validation [2, 37].
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The consistent downregulation of miR-155 in all infected groups relative to controls aligns with its bioinformatics-predicted role as a negative regulator of antiviral/antibacterial innate immunity, targeting SHIP1/SOCS1 to promote NF-κB activation and IFN-γ production for enhanced macrophage phagocytosis and T-cell responses [48, 49, 55]. In HIV, miR-155 suppression facilitates viral latency by impairing Th1 polarization and dendritic cell maturation [17, 55], while in TB, it attenuates NLRP3 inflammasome activity, reducing IL-1β-driven granuloma formation [20, 47, 56]. Our data, showing no inter-group differences among infections (P > 0.05), suggest a convergent suppressive mechanism via HIV Tat or Mtb ESAT-6-mediated epigenetic silencing [50, 61], validating the 8 shared targets identified. Therapeutically, restoring miR-155 via synthetic mimics or AAV-delivered vectors could bolster anti-Mtb immunity in co-infected patients, as preclinical models demonstrate reduced bacterial burden and HIV replication upon overexpression [17, 55, 58]. As a biomarker, miR-155's moderate standalone AUC (0.71 for HIV vs. controls) improves in panels (e.g., with NEAT1/GAS5, AUC = 0.95), enabling discrimination of HIV + from controls and, to a lesser extent, from TB/HIV+ (AUC = 0.91 in five-ncRNA panel), outperforming single markers in resource-limited diagnostics [18, 62].In contrast, miR-29a's profile exhibited TB-specific downregulation (P = 0.03 vs. controls) with attenuation in co-infection, reflecting its dual bioinformatics-implicated role in IFN-γ-mediated Th1 responses and apoptosis inhibition via targeting BCL2 and DNMT3A [7, 8, 51]. During TB, miR-29a upregulation promotes antimycobacterial autophagy and restricts Mtb survival in macrophages [8, 19], but HIV co-infection may blunt this through Vpr-induced suppression, exacerbating immune exhaustion [9, 23]. This differential expression—higher in TB + vs. HIV+ (P = 0.003)—positions miR-29a as a discriminator between monoinfections, with limited standalone utility for TB (AUC = 0.58) but enhanced in panels (e.g., miR-29a/NEAT1/GAS5, AUC = 0.98 for TB vs. controls; miR-29a/COX2, AUC = 0.84 for HIV + vs. TB+), aligning with meta-analyses of miR-29 as a TB biomarker (sensitivity ~ 80%) even in HIV + cohorts [7, 8, 9].
Therapeutically, miR-29a agonists could synergize with ART and ATT to restore IFN-γ signaling and reduce co-infection progression, as evidenced by in vitro studies showing decreased HIV latency and Mtb persistence [14, 18].
Among the lncRNAs, COX2 (PTGS2-AS1) displayed marked upregulation in TB + and TB/HIV + groups (P < 0.0001 vs. controls), linked to its antisense regulation of PTGS2 (COX-2), which amplifies PGE2-mediated inflammation and NLRP3 activation [37, 38, 52, 53]. In TB/HIV co-infection, this may drive synergistic cytokine storms (e.g., IL-6/TNF-α), impairing granuloma integrity and promoting dissemination [3, 37, 44]. The positive correlation with miR-155 (r = 0.24, P = 0.01) suggests coordinated pro-inflammatory loops, while antagonism with miR-29a (r=-0.38, P = 0.0001) implies COX2-mediated suppression of protective autophagy, consistent with recent differential lncRNA expression in HIV-TB [37]. As a biomarker, COX2's AUC = 0.76 for TB vs. controls and utility in HIV + vs. TB + discrimination (part of panels) highlights its TB-specific value [37, 38]. Therapeutically, COX2 inhibitors (e.g., celecoxib) or siRNA-based knockdown could mitigate hyperinflammation without compromising antiviral responses, as shown in Mtb-infected models where silencing reduced bacterial load [52, 53]; however, HIV contexts require caution due to potential impacts on latency reservoirs [35].
NEAT1's uniform upregulation across groups (P < 0.0001) underscores its architectural role in paraspeckle formation, sequestering RNA-binding proteins to modulate antiviral ISG expression and apoptosis [54–57, 26], co-expressed with predicted MAPK/TLR hubs. In HIV, NEAT1 promotes viral persistence by stabilizing Rev/Rev-responsive element interactions [27, 31], while in TB, it enhances macrophage survival and IL-6/IFN-β production [24, 30, 32, 38]. The lack of inter-group differences and negative correlation with miR-29a (r=-0.33, P = 0.0008) indicate broad immune activation without specificity, yet its high AUC = 0.84 for TB and inclusion in all high-performing panels (e.g., AUC = 1.00 for co-infection) make it a robust general discriminator from controls [25, 28, 38]. Therapeutically, NEAT1 knockdown via ASOs has shown promise in reducing HIV replication and Mtb-induced inflammation in vitro [27, 29], potentially as an adjunct to standard therapies to enhance clearance in co-infected patients [26].
GAS5's upregulation, amplified in TB + and TB/HIV+ (P < 0.0001), acts as a tumor suppressor and immune modulator by sponging miR-21 and glucocorticoid receptor interactions, promoting T-cell apoptosis and restraining hyperinflammation [3, 58, 59], aligning with its apoptosis-linked co-expression profile. Contrasting some viral downregulation reports [60], our PBMC data suggest TB-driven induction overrides HIV suppression, possibly via Mtb-induced glucocorticoid resistance [37, 39]. Negative correlations with miR-155/miR-29a (r=-0.41/-0.15, P = 0.0001/0.01) imply GAS5-mediated feedback to prevent exhaustion. With the highest individual AUC = 0.97 for TB vs. controls and key role in panels (e.g., AUC = 0.95 for HIV; 0.74 for TB + vs. TB/HIV+), GAS5 excels in monoinfection discrimination and co-infection detection [37, 41, 43]. Therapeutically, GAS5 overexpression vectors could restore T-cell function in co-infection, inhibiting HIV via miR-873 sponging and enhancing anti-TB apoptosis [34, 35, 38]; recent analyses affirm its prognostic/therapeutic versatility [43].
Spearman's correlations unveiled a regulatory axis—cooperative COX2-miR-155 inflammation, antagonistic COX2/miR-29a/NEAT1-miR-29a suppression, and GAS5-miRNA feedback—mirroring predicted ceRNA networks in infections [5, 7, 33, 37] and supporting bioinformatics-derived interactions (e.g., miRNA-lncRNA sponging of shared targets). These likely underpin co-infection synergy, where HIV attenuates TB-protective miR-29a while lncRNAs amplify persistence [37].
ROC and logistic models affirmed biomarker efficacy: individual lncRNAs (GAS5/NEAT1) surpassed miRNAs for vs. controls discrimination, but panels integrated synergies for superior group stratification—e.g., five-ncRNA for HIV+/TB/HIV+ (AUC = 0.91), three-ncRNA for TB+/TB/HIV+ (AUC = 0.74)—aligning with recent lncRNA signatures in HIV-TB cohorts (sensitivity > 90%) [37, 41]. This multiplex approach outperforms Xpert MTB/RIF (55% sensitivity in HIV-TB) [10, 11], offering non-invasive PBMC-based assays for smear-negative/extrapulmonary cases [9, 37, 61].
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Our results advance ncRNA utility in TB/HIV, where co-infection accelerates progression via immune dysregulation [2, 3, 37]. Biomarker panels enable rapid triage, while therapeutic targeting (e.g., miRNA mimics, lncRNA ASOs) could personalize interventions, reducing mortality in high-burden areas [2, 4, 27, 37].A
Limitations include the sample size (n = 95, co-infection n = 25), potentially limiting power; cross-sectional design omits dynamics; and reliance on bioinformatics without direct functional assays for all interactions. Future multicenter studies with diverse cohorts should assess longitudinal/prognostic utility [4, 25, 37]. Integrated omics and CRISPR models could dissect mechanisms, paving way for clinical assays [60, 2, 5, 37].In conclusion, this study elucidates distinct expression profiles of lncRNAs (COX2, NEAT1, GAS5) and miRNAs (miR-155, miR-29a) in PBMCs from individuals with HIV mono-infection, TB mono-infection, TB/HIV co-infection, and healthy controls, as validated through qRT-PCR analyses that corroborated our bioinformatics predictions of their regulatory roles in overlapping immune pathways. Spearman's correlation analyses further revealed intricate ncRNA-miRNA networks underpinning pathogen-host interactions, while ROC and logistic regression models demonstrated the superior diagnostic accuracy of integrated biomarker panels (e.g., AUCs of 0.95–1.00 for disease detection and differentiation), surpassing individual markers and conventional assays like Xpert MTB/RIF in HIV contexts. These findings not only highlight the mechanistic contributions of ncRNAs to immune dysregulation in TB/HIV co-infection but also underscore their translational potential as non-invasive, multiplex biomarkers for enhanced detection in resource-limited settings. Therapeutically, targeting these ncRNAs—such as via miRNA mimics or lncRNA antisense oligonucleotides—could complement antiretroviral and antitubercular therapies to mitigate disease progression, aligning with emerging evidence on dysregulated lncRNAs and miRNAs in co-infected cohorts. Ultimately, ncRNA-based diagnostics and interventions hold promise for revolutionizing precision medicine, reducing the global burden of TB/HIV co-infection, and improving outcomes in high-prevalence regions.
2. Materials and Methods
2.1. Ethical approval
The research design and experimental procedures comply with the declaration of Helsinki.
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And the study was approved by the Medical Ethics Committee of Iran University of Medical Science (Ethical code: IR.IUMS.REC.1402.630), the participants signed a written informed consent form before the study.2.2. Patient samples
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Participants for this study were recruited and followed at public health centers providing TB and HIV care in the western regions of Tehran city (related to Iran University of Medical Sciences [IUMS]), between 2023 and 2025. Four groups of participants were included in this study. In total, 95 participants were recruited in this study, including 25 patients with HIV infection, 20 patients with TB infection, 25 with TB/HIV coinfection and 25 healthy volunteers. The TB patients were required to have been bacteriologically confirmed TB (by liquid culture, direct microscopy, and/or Xpert MTB/RIF assay) and not be receiving anti TB treatment (ATT). The HIV patients either were required to have been confirmed by ELISA and PCR methods and not be receiving antiretroviral therapy (ART) currently. Healthy volunteers also should not have been confirmed to have any cancers, chronic or infectious disease.2.3. miRNA and lncRNAs selection
The bioinformatics analysis was conducted to identify overlapping genes between HIV and TB pathways, predict regulatory miRNAs, and associate lncRNAs with disease pathogenesis. All analyses were performed using publicly available databases and tools, with data retrieved in 2023. The workflow integrates pathway enrichment, gene overlap assessment, miRNA target prediction, and lncRNA association screening.
2.3.1. Retrieval of Disease-Associated Genes
Gene sets associated with HIV infection and TB were retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (release 2021). Specifically, genes involved in the "HIV-1 infection" pathway (KEGG ID: hsa05170; 212 genes) and the "Tuberculosis" pathway (KEGG ID: hsa05152; 180 genes) were extracted using Enrichr. These genes represent host factors implicated in viral/bacterial pathogenesis, immune modulation, and disease progression.
2.3.2. Identification of Common Genes
The two gene lists were compared using the online Venn diagram tool to identify overlapping genes (Fig. 5). The analysis revealed 60 shared genes (e.g., IFNA family, MAPK, TLR4, TNF), presumed to be involved in synergistic molecular mechanisms during HIV/TB co-infection. Unique genes were 121 for TB and 153 for HIV. Transcription factor enrichment for these common genes was performed using Enrichr's TRANSFAC_and_JASPAR_PWMs dataset, highlighting regulators such as POU1F1 (15/1408 genes, adjusted p = 0.0027) and NKX3-1 (6/264 genes, adjusted p = 0.0167).
2.3.3. Prediction of miRNAs Targeting Common Genes
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The 60 shared genes were submitted to Enrichr, selecting the miRTarBase 2017 dataset (updated to 2023 for validation) to identify experimentally validated miRNAs. Only interactions supported by strong evidence (e.g., reporter assays, Western blotting, qPCR) were considered. Enrichment analysis prioritized miRNAs based on overlap, p-value, and odds ratio (e.g., hsa-miR-34a-5p: 13/735 genes, adjusted p-value = 1.99E-04; hsa-miR-155-5p: 8/456 genes, adjusted p-value = 0.0032; hsa-miR-29a-3p: 5/264 genes, adjusted p-value = 0.055). Focus was placed on miR-29a and miR-155 due to their established roles in immune regulation and pathogen response) Fig. 1).A
Fig. 5
KEGG pathway analysis confirmed significant overlap between HIV-1 infection and TB pathways.
2.3.4. Identification of lncRNAs Associated with HIV and Tuberculosis
Keyword-based searches ("HIV", "AIDS", "tuberculosis", "Mycobacterium tuberculosis") were conducted in the GeneCards database to compile lncRNAs associated with either or both diseases. Top hits included GAS5 (relevance score 37.86) and NEAT1 (relevance score 20.67). Co-expression analysis with the 60 common genes was performed using Enrichr's lncHUB_lncRNA_Co-Expression dataset, identifying lncRNAs like LINC01255 (4/100 genes, adjusted p = 0.117) but prioritizing those with known immune/inflammatory roles. PTGS2-AS1 (lncRNA-COX2) was included based on its antisense regulation of PTGS2 (COX2) and literature associations with inflammation. Selected lncRNAs (NEAT1, COX2/PTGS2-AS1, GAS5) are known to modulate immune pathways, interact with shared genes, or sponge miRNAs like miR-29a and miR-155.
2.4. Preparation of peripheral blood mononuclear cells (PBMCs)
6 mL of peripheral blood samples were collected in syringes and directly transferred to tubes containing Ethylenediaminetetraacetic Acid (EDTA), and was transported to the processing lab at ambient temperature within 2 hours of collection. PBMCs were isolated based on the ficoll hypaque density gradient centrifugation (Lympholyte-H, Cedarlane, Hornby, Canada), technique according to the manufacturer’s instructions, and then the pellet of PBMCs was washed three times with phosphate-buffered saline (PBS) solution (pH: 7.3 ± 0.1), and finally resuspended with 250 µL of RNA maintenance solution (RNA-Later, Ambion, Inc., Austin, TX), and kept at -80°C until extraction of the total RNA.
2.4. Total RNA isolation and complementary DNA (cDNA) synthesis
Total RNA was extracted from PBMC samples according to the manufacturer’s protocols with minor modifications [45]. The integrity and purity of the isolated RNA was evaluated using a Nano-Drop™ spectrophotometer instrument (Thermo Fisher Scientific, Wilmington, NC, USA), spectrophotometer and were kept at -20°C until use, and then kept at -80°C until the cDNA synthesis.
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To determine the expression pattern of lncRNAs (COX-2, NEAT-1 and GAS-5), cDNA was synthesized using cDNA synthesis kit (Biotechrabbit, Berlin, Germany) according to manufacturer’s protocol.2.5. Expression analysis of genes using real-time PCR
The expression patterns of lncRNAs (COX2, NEAT-1 and GAS5), and also GAPDH as the reference housekeeping gene were determined by real-time polymerase chain reaction (PCR) using a Rotorgene Q thermal cycler (Qiagen, Hilden, Germany) instrument. The assays were done on 15µL reaction mixture including: 1µL of 10 pmol concentration of each primer (COX2, NEAT-1, GAS5 and GAPDH) (Table 1) 4.5µL nuclease free distilled water, 7.5µL 2 × SYBR® Premix Ex Taq (Tli Plus) Master Mix (TaKaRa Bio Inc. Shiga, Japan) and 1µL of cDNA as template.
Sequence (5' → 3') | Name |
|---|---|
GAAAGAAGGCGAGGAGCAGATCGAGGAAGAAGACGGAAGAATGTGCGTCTCGCCTTCTTTCACCCCAA | miR-155RT |
GAAAGAAGGCGAGGAGCAGATCGAGGAAGAAGACGGAAGAATGTGCGTCTCGCCTTCTTTCAGCCAAT | miR-29aRT |
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Funding
This research was funded by the Vice-Chancellor for Research, Iran University of Medical Sciences, Tehran, Iran (grant number: IR.IUMS.REC.1402.630).
Sequence (5' → 3') | Name | Direction | Real-time PCR based on SYBR-Green I fluorescence |
|---|---|---|---|
CTCCACGGGTCACCAATATAAA | COX21-F | Forward primer | Real Time PCR for COX2 |
ACGCATCAGGGAGAGAAATG | COX2-R | Reverse primer | |
AGTTGTGTCCCCAAGGAAGG | GAS52-F | Forward primer | Real Time PCR for GAS5 |
CGTTACCAGGAGCAGAACCAT | GAS5-R | Reverse primer | |
TGGCTAGCTCAGGGCTTCAG | NEAT3-F | Forward primer | Real Time PCR for NEAT-1 |
TCTCCTTGCCAAGCTTCCTTC | NEAT-R | Reverse primer | |
CGACCACTTTGTCAAGCTCA | GAPDH4-F | Forward primer | Real Time PCR for GAPDH |
CCCTGTTGCTGTAGCCAAAT | GAPDH-R | Reverse primer | |
CGAGGAAGAAGACGGAAGAAT | Universal reverse | Reverse | Real Time PCR for miR-155 and miR-29a |
GCGGTTACTGCTAATCGTGATA | miR-1555F | Forward | Real Time PCR for miR-155 |
GAGCCTACCACCATCTGAA | miR-296F | Forward | Real Time PCR for miR-29a |
GCTTCGGCAGCACATATACTAAAAT | U67-F | Forward | Real Time PCR for U6 |
CGCTTCACGAATTTGCGTGTCAT | U6-R | Reverse | |
1. COX2, cyclooxygenase2 2. GAS5, growth arrest specific 5 3. NEAT-1, Nuclear Paraspeckle Assembly Transcript 1 4. GAPDH, Glyceraldehyde 3-Phosphate Dehydrogenase. 5. MicroRNA-155 6.MicroRNA-29a 7.U6 snRNA | |||
| The thermocycling conditions for real-time PCR for COX2 is defined as follows: initial denaturing at 95°C for 10 minutes, and 40 cycles, including 15 seconds at 95°C, 30 seconds at 58° C, and 20 seconds at 72°C, for the NEAT1 and GAS5 were defined as follows: initial denaturing at 95°C for 10 minutes, and 40 cycles, including 15 seconds at 95°C, 30 seconds at 60° C, and 20 seconds at 72°C; and for GAPDH initial denaturing at 95°C for 15 minutes, and 40 cycles, including 15 seconds at 95°C, 30 seconds at 60° C, and 20 seconds at 72°C. Data acquisition from all above was sat at the extension step. All the specimens were tested in duplicate reactions. | |||
| 2.6. miRNA-155 and miR-29a expression analysis | |||
A Total RNA was extracted (as described in the previous section) from PBMC samples. Synthesis of cDNA was performed as previous section as manufacturer’s protocol with the difference on using specific RT stem-loop primers of each miRNA (Table. 2). | |||
| The real time PCR assay was carried out in final 15µL volume, including 1µL of specific forward primer, 1µL of universal reverse primer, 7.5µL of SYBR Green PCR Master Mix (TaKaRa, Kusatsu, Japan), 4.5µL of nuclease-free water, and 1µL of cDNA as template. The thermal profile of this assay (three steps with melt) was set at 95°C for 10 minutes as hold time, followed by 40 cycles of denaturation at 95°C for 15 seconds, annealing at 58°C for 30 seconds, and extension at 72°C for 20 seconds. This assay was performed using the Rotorgene Q thermal cycler (Qiagen, Hilden, Germany) instrument. The expression levels of microRNAs were normalized to U6 as reference RNA. It should be noted that all reactions were done in duplicate (Table. 1). | |||
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Author Contribution
All authors reveiwed the manuscript and took parts in writing the main manuscript.
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Acknowledgement
This research was funded by the Vice-Chancellor for Research, Iran University of Medical Sciences, Tehran, Iran (grant number: IR.IUMS.REC.1402.630).
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Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Author information
Department of Medical Biotechnology, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran.
Tahereh Donyavi & Darya Ebrahimi Moghaddam
Department of Virology, Faculty of Medicine, Iran university of Medical Sciences, Tehran, Iran.
Farah bokharaei-Salim
Infectious and Tropical Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Javid Sadri Nahand
Deputy of Health, Iran University of Medical Sciences, Tehran, Iran
Fatemeh Ahmadi
Competing Interests
The authors declare that they have no competing interests.