Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Differential MicroRNA Expression in Human Macrophages with Mycobacterium tuberculosis Infection of Beijing/W and Non-Beijing/W Strain Types

  • Lin Zheng,

    Affiliation Department of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China

    ⨯
  • Eric Leung,

    Affiliation Department of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China

    ⨯
  • Nelson Lee,

    Affiliation Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China

    ⨯
  • Grace Lui,

    Affiliation Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China

    ⨯
  • Ka-Fai To,

    Affiliation Department of Anatomical & Cellular Pathology, The Chinese University of Hong Kong, Hong Kong SAR, China

    ⨯
  • Raphael C. Y. Chan,

    Affiliation Department of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China

    ⨯
  • Margaret Ip

    margaretip@cuhk.edu.hk

    Affiliation Department of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China

    ⨯

Abstract

Objectives

The role of microRNAs in association with Mycobacterium tuberculosis (MTB) infection and the immunology regulated by microRNAs upon MTB infection have not been fully unravelled. We examined the microRNA profiles of THP-1 macrophages upon the MTB infection of Beijing/W and non-Beijing/W clinical strains. We also studied the microRNA profiles of the host macrophages by microarray in a small cohort with active MTB disease, latent infection (LTBI), and from healthy controls.

Results

The results revealed that 14 microRNAs differentiated infections of Beijing/W from non-Beijing/W strains (P<0.05). A unique signature of 11 microRNAs in human macrophages was identified to differentiate active MTB disease from LTBI and healthy controls. Pathway analyses of these differentially expressed miRNAs suggest that the immune-regulatory interactions involving TGF-β signalling pathway take part in the dysregulation of critical TB processes in the macrophages, resulting in active expression of both cell communication and signalling transduction systems.

Conclusion

We showed for the first time that the Beijing/W TB strains repressed a number of miRNAs expressions which may reflect their virulence characteristics in altering the host response. The unique signatures of 11 microRNAs may deserve further evaluation as candidates for biomarkers in the diagnosis of MTB and Beijing/W infections.

Citation: Zheng L, Leung E, Lee N, Lui G, To K-F, Chan RCY, et al. (2015) Differential MicroRNA Expression in Human Macrophages with Mycobacterium tuberculosis Infection of Beijing/W and Non-Beijing/W Strain Types. PLoS ONE 10(6): e0126018. https://doi.org/10.1371/journal.pone.0126018

Academic Editor: Selvakumar Subbian, Public Health Research Institute at RBHS, UNITED STATES

Received: December 12, 2014; Accepted: March 27, 2015; Published: June 8, 2015

Copyright: © 2015 Zheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE65810 for the human miRNA and GSE65811 for THP-1 cells miRNA profiles (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE810; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE811) respectively. Additional Tables and Figures are in Supporting Information (S1 and S2 Tables, S1, S2, and S3 Figs.

Funding: The study was supported by the Health and Medical Research Fund (previously Research Fund for the Control of Infectious Diseases), Food and Health Bureau, HKSAR government (RFCID No. 09080392). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Tuberculosis is one of the most common causes of death from infectious diseases. Studies have shown that one-third of the world’s population is infected with M. tuberculosis (MTB). The people who are infected with MTB but who do not have active tuberculosis have latent infection (LTBI), and they have a 10% lifetime chance that they will progress to having the active disease.

Macrophages play a key role in the immune defence, and in particular, early clearance of MTB. MTB invade and replicate within alveolar macrophages. They evade the host defence system by blocking the formation of the apoptotic envelope [1] or inhibiting plasma membrane repair [2], which lead to macrophage necrosis and dissemination of infection in the lung.

MicroRNAs (miRNAs) are small, non-coding RNAs that have an important regulatory role in gene expression programs [3]. Each miRNA has the potential to repress the expression of hundreds of genes [4]. Disease-associated miRNAs represent a new class of diagnostic marker or therapeutic targets [5]. Several of these have recently been demonstrated to regulate the components of important inflammation signalling pathways under the challenge of specific MTB antigens [6–12]. For example, miR-144* were over-expressed in the T cells of active TB patients [6], miR-146a regulating IL-6 production in dendritic cells [7]. High miR-125b expression and low miR-155 expression with correspondingly low TNF production regulate the macrophage inflammatory response [9–10], while the miR-155/miR-155* ratio was increased in PBMCs of MTB patients [12].

The effect of miRNA expression on the infection of various MTB strain types is as yet unknown. While most studies used laboratory strains, clinical strains such as that of the Beijing/W family have been associated with outbreaks and multidrug resistance, and may harbour a genetic advantage for disease. We hypothesized that miRNAs have a role in regulating the unique gene expression of macrophages in a strain- and host-dependent way. In this study, we examined the expression of 384 unique human-specific and widely expressed miRNAs from PMA-treated THP-1 derived macrophages infected with different clinical MTB strains. The results revealed unique signatures that differentiated infections of Beijing/W from non-Beijing/W strains. In addition, we also revealed that differentially expressed miRNA profiles of macrophages of patients with active MTB infection differed from those of LTBI patients and healthy controls. Pathway analyses suggested that cell membrane and extracellular matrix metabolite involve glycosaminoglycan biosynthesis and fatty acid biosynthesis; and that immune-regulatory interactions involving TGF-β signalling pathway take part in the dysregulation of critical TB processes in the macrophages. These miRNAs profiles may serve as disease-associated markers and enhance our understanding in the host-bacterial interactions in MTB infections.

Materials and Methods

Bacterial Strains

Twelve clinical isolates of MTB, including six Beijing/W, six non-Beijing/W strains previously isolated from patients at the Prince of Wales Hospital, Hong Kong were examined. The phenotypes and genotypes of these strains were respectively confirmed by MIC and DTM-PCR methods, as described by Chen et al. [13]. Briefly, DTM-PCR used three primers in a multiplex PCR to target the RD105 deletion in Beijing/W genotypes and produced a 1,466 bp product for the non-Beijing genotype and a 761 bp for the Beijing/W genotype.

Patient recruitment and characteristics

Participants were recruited from the Prince of Wales Hospital, Hong Kong. All participants were older than 18 years and gave written informed consent. Patients who were pregnant, immune-suppressed, or who had diabetes or autoimmune disease were excluded. From each individual in the three cohorts: the healthy (n = 3), the latent (n = 4), and the active TB patients (n = 3), whole blood specimens were collected for monocytes isolation. Patients with active TB were confirmed by a positive acid-fast smear and culture. Active TB patients were prospectively recruited and sampled before any anti-mycobacterial treatment was started. LTBI cases were identified to be positive in the IFN-γ release assay (IGRA) but without their having signs and symptoms of active disease. Healthy controls were volunteers who were excluded from any known acute or chronic infections and who were negative by IGRA. Ethics approval was obtained from the Joint Chinese University of Hong Kong, New Territories East Cluster Clinical Research Ethics Committee. All participants were older than 18 years and gave written informed consent.

IFN-γ release assay (IGRA) testing

The QuantiFERON TB-Gold Test (Cellestis) was performed in accordance with the manufacturer’s instructions.

PBMC isolation from whole blood

PBMCs were freshly harvested from the patients’ whole blood by using the Ficoll-Hypaque column (GE healthcare) in accordance with manufacturer’s instructions. The supernatant containing the autologous donor-specific plasma was saved and heat inactivated at 56°C for 30 min. The PBMC was resuspended in ice-cold monocyte adhesion medium (RPMI1640 + 7.5% autologous plasma, 1% penicillin-streptomycin) and incubated in a petri dish for 90minutes at 37°C. The adherent monocytes were washed with warm RPMI medium several times to remove loosely attached cells. The monocytes were detached by incubation with PBS containing 5 mM EDTA for 10–20 minutes at room temperature and were collected by centrifugation. The differentiation into macrophages was according to protocol previously described [14]. The monocytes were refed by fresh medium every 2 days and allowed to differentiate into macrophages for 10 days in vitroRNA of macrophages was harvested and kept for downstream TaqMan miRNA array experiments.

Infection of macrophages

THP-1 cells were maintained in RPMI 1640 (Gibco,Carlsbad, CA) supplemented with 10% fetal bovine serum (Gibco). Cells were incubated with phorbolmyristate acetate (5ng/ml PMA; Sigma-Aldrich, St Louis, MO) for 48 hours to induce differentiation into a macrophage phenotype [15]. MTB isolates were cultured in Middlebrook7H9 (BD Biosciences) at 37°C, 5% CO2 until the cultures reached McFarland 1 (about 107 CFU/mL). The MTB cells were harvested by centrifugation and the pellet was resuspended in RPMI medium and added to the macrophages. Macrophages were infected at a multiplicity of infection (MOI) of 3 bacilli/cell for 2 hours, and the excess free-floating bacilli were removed by washing the culture with fresh RPMI containing 10μg/ml gentamicin. The culture was incubated in a fresh RPMI medium without antibiotics at 37°C, 5% CO2for 72-hours. Uninfected control cultures of THP-1 or human macrophages were setup with identical corresponding treatments but without MTB infection.

RNA isolation and Quantification

RNA was isolated from macrophages with the mirVana miRNA Isolation Kit (Ambion, Austin, TX, USA) in accordance with the manufacturer’s instructions. The purity and quantity of RNA were measured by NanoDrop (ND-1000 spectrophotometer, Thermo Scientific, Wilmington, DE, USA). The samples were used immediately or stored at -80°C.

TaqMan microRNA Array Quantitative PCR

The TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) was used for preparation of cDNA. RT reactions were performed on a GeneAmp PCR System 9600 (Applied Biosystems) with the following conditions: 40 cycles of 16°C, 2 min; 42°C, 1 min; 50°C, 1 sec; and 1 cycle of holding at 85°C, 5 minutes. All samples were analysed with the Human TaqMan low density miRNA array (TLDA, Applied Biosystems) which covered 384 different miRNAs simultaneously and performed using a fast real-time PCR system (ABI Prism 7900HT). The cycle threshold (Ct) raw data was analyzed by two manufacturer’s softwares; SDS 2.4 and RQ Manager 1.2.1. The uninfected control results were set as the baseline against the infected in the analyses.

Analysis of potential mRNAs targeted by differentially expressed microRNAs

Family names were specified by miRBase release 19, while clustered microRNA described in miRBase release 19 were assumed to be polycistronic pri-miRNAs. Possible mRNA targets of the differentially expressed miRNA were identified by using the miRwalk databases [16], through an integrative evaluation with different algorithms: DIANA-mT (http://diana.cslab.ece.ntua.gr/), miRanda (http://www.microrna.org/microrna/home.do), miRDB (http://mirdb.org/miRDB/), RNA22(http://cbcsrv.watson.ibm.com/rna22.html), and TargetScan v 6.2 (http://www.targetscan.org/). Only mRNA predicted by at least three of these algorithms were considered as potential targets. Cellular pathway analysis of the differentially expressed miRNAs was performedby using the DIANA-miRPath v2.0 [17], based on information from DIANA-microT-CDS (http://diana.cslab.ece.ntua.gr/micro-CDS/?r=search) and the KEGG pathway database (http://www.genome.jp/kegg/pathway.html).

Statistical analysis

The expression level of each miRNA was calculated by the relative quantity (RQ value) (2-ΔΔCt) method. The internal control, Mammal U6, was selected for normalization across all experiments. The Ct raw data were determined by using an automatic baseline and a threshold of 0.2(RQ Manager, ABI, Life Technologies). Significant differences were evaluated in SPSS (v20.0 for Windows). Only miRNAs with a P value of ≤0.05 and with consistent expression in all of the samples were considered as differentially expressed. Unsupervised clustering analysis, using DataAssist v 3.01 of ABI (Life Technologies), was performed to identify the different sub-groups defined by miRNA expression profiles. "The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [18] and are accessible through GEO Series accession number GSE65810 for the human miRNA and GSE65811 for THP-1 cells miRNA profiles (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE810; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE811) respectively.

Results

1. MicroRNA expression profiling of THP-1macrophages infected with MTB strains

PMA-induced THP-1 macrophages were infected separately with six Beijing/W and six non-Beijing/W strains. MicroRNAs were quantitated by RT-PCR using the Human TaqMan Low Density Array (TLDA). Of the miRNAs that were fully expressed in all samples, statistically significant differential expression (p < 0.05) of 14 miRNAs in macrophages of Beijing/W MTB infection were identified when compared with that of non-Beijing/W strains (Table 1). Of these, 13 miRNAs (hsa-let-7e, hsa-let-7f, hsa-miR-10a, hsa-miR-21, hsa-miR-26a, hsa-miR-99a, hsa-miR-140-3p, hsa-miR-150, hsa-miR-181a, hsa-miR-320, hsa-miR-339-5p, hsa-miR-425, and hsa-miR-582-5p) were repressed in the Beijing/W TB infected group (Fig 1). The cluster analysis is shown in S1 Fig.

thumbnail
Download:
Table 1. MicroRNAs differentially expressed in THP-1 macrophages infected with Beijing/W and non-Beijing/W clinical TB strains.

https://doi.org/10.1371/journal.pone.0126018.t001

thumbnail
Download:
Fig 1. miRNAs expression level in the THP-1 macrophages infected with Beijing/W and non-Beijing/W clinical TB strains.

The relative quantity (RQ, 2-ΔΔCt) was used to normalize the relative gene expression data. Statistical analysis between two groups was performed using Mann-Whitney test. Individual values were denoted by black dots/squares from each group of Beijing/W versus non-Beijing strains. The mean RQ and S.D. of each group were represented by the ------- bar and short bars --- in each figure, respectively.

https://doi.org/10.1371/journal.pone.0126018.g001

Based on these significantly altered miRNA profiles, a number of biological processes were highlighted in infection with different MTB strains. The pathway analyses of miRNA profiles induced by Beijing/W versus non-Beijing/W strains (Table 2) showed that immune-regulatory interactions of the TGF-β signalling pathway were involved. In particular, a change of pathways leading to cell communication (Gap junction, focal adhesion, and adherens junction) and cellular process (endocytosis and apoptosis), as well as signal transduction through MAP kinases, mTOR, ECM receptor, and Wnt were implicated.

thumbnail
Download:
Table 2. Biological pathways potentially affected by the differentially expressed microRNAs in THP-1 macrophages infected with Beijing/W and non-Beijing/W TB clinical strains.

https://doi.org/10.1371/journal.pone.0126018.t002

2. MicroRNA Expression in host macrophages of active MTB, latent infection and healthy controls

The miRNA expression in macrophages of active MTB (n = 3), LTBI infection (n = 4), and healthy controls (n = 3) were examined. Details of the subjects are listed (S1 Table). Eleven miRNAs was found to be differentially expressed in the active MTB versus the latent/healthy controls (p < 0.05) (Table 3). Among these 11 miRNAs, no differences were observed between the latent and healthy controls groups. Seven miRNAs had different expression levels between active TB and healthy controls: six miRNAs (hsa-miR-16, hsa-miR-137, hsa-miR-140-3p, hsa-miR-193a-3p, hsa-miR-501-5p, and hsa-miR-598) were upregulated while hsa-miR-95 was down-regulated. Two miRNAs (hsa-miR-101 and hsa-miR-150) were found to differentiate the LTBI group from the MTB active disease group (S2 Fig). Interestingly, hsa-miR-146b-3p and hsa-miR-296-5p were expressed in all of LTBI group but not in the active MTB and healthy controls. Fig 2 shows a tendency for these 11 differentially expressed miRNAs to cluster independently the groups of active MTB disease and the LTBI or healthy controls.

thumbnail
Download:
Table 3. miRNAs differentially expressed in human macrophages with active MTB and latent infections against healthy controls.

https://doi.org/10.1371/journal.pone.0126018.t003

thumbnail
Download:
Fig 2. Clustering analysis of the 11 miRNAs was performed using DataAssist 3.0v based on ΔCt-values of the TLDA results.

Upregulated miRNAs are designated by various shades of red and down-regulated miRNAs by various shades of green. Clinical phenotypes are labelled in different colours: active MTB infection (red), latent infection (blue), and healthy controls (green).

https://doi.org/10.1371/journal.pone.0126018.g002

The biological pathways potentially implicated by these differentially expressed miRNAs are listed in Table 4. Pathway analyses identified that the change of cell membrane and extracellular matrix metabolite involving glycosaminoglycan biosynthesis-HS and fatty acid biosynthesis might play a role in MTB infection. This might result in signal transduction through MAP kinases, mTOR, ECM receptor, and Wnt, and finally activate the immune-regulatory interactions involving the TGF-β signalling pathway and the T cell receptor signalling pathway.

thumbnail
Download:
Table 4. Biological pathways potentially affected by the differentially expressed microRNAs of significance from macrophages of active MTB disease, LTBI and healthy controls.

https://doi.org/10.1371/journal.pone.0126018.t004

Discussion

MiRNAs can modulate the innate and adaptive immune responses to pathogens by affecting mammalian immune cell differentiation and the development of diseases of immunological origin [19], because various bacterial cell wall components, such as peptidoglycan (PG), lipoproteins and lipopolysaccharide (LPS) could upregulate the miRNAs levels [20].

In our studies, miRNA profiles in the macrophages were found to be altered in MTB infection in a strain- and host-dependent way. The Beijing genotype strain is the most predominant M. tuberculosis strain in south China, and it has caused large outbreaks of MDR-TB. The Beijing strains showed increased transmission fitness when they acquired streptomycin resistance [21]. Beijing genotype strains were also found to induce the STAT1 activation and interferon-related immune response [22–23]. We showed for the first time that the Beijing/W strains repressed a number of miRNAs as compared to the non-Beijing/W TB strains, which might reflect their virulence characteristics in altering the host response. Hsa-miR485-3p was found to be upregulated in Beijing/W infected macrophages. Hsa-miR-485-3p has been shown to be involved in cell survival [24] and knockdown of this miRNA in hepatic cells increased apoptosis [25]. Previous report indicated that miR-485-3p post-transcriptionally targeted NF-YB [24], a direct transcriptional repressor of Top2α gene and of MDR1 and CCNB2 genes [26] in regulation of the cell cycle, Our results suggest that high miR-485-3p possibly facilitates survival of the Beijing/W strains in macrophages and evades apoptosis or alters macrophage lysis and subsequent downstream immune response toward clearance of MTB.

The difficulty in discriminating the spectrum of MTB infections and of latency is prompting the need to search for new biomarkers for MTB infection. Previous studies that have utilized such microarrays as diagnostic markers are listed in Table 5. Studies used whole-genome transcriptional profiling of peripheral blood mononuclear cells (PBMCs) [27] or whole blood cells [28] found that FcGR1B (CD64) and Fc gamma receptor 1B (FCGRIB) were the most differentially expressed genes in the individuals with active TB. A recent report found a dominant TNF-a+ MTB–specific CD4+ T cell response that discriminated between LTBI and active disease [29]. The miRNA expression profile of PBMCs [30] and sputum supernatant [31] exhibited a characteristic expression in MTB infection, while the miRNA signatures from serum also associated to different phases of TB infections [32–35]. These data may shed some light to the roles of miRNAs in MTB infections, but do not yet explain the transition of latency to active TB disease. We were able to distinguish with the expression of 11-miRNA signature profiles of the active TB group from that of the LTBI group but not that of latent and healthy groups. When we carried out the analysis using group-wise comparisons, the variations between individual group members showed that 10–25% of the latent patients remained clustered with the active TB patients, and this corroborated with a previous study which concluded that the whole-blood transcript dominated by neutrophil-driven interferon (IFN)-inducible genes correlated with the radiological extent of active MTB [36].

thumbnail
Download:
Table 5. Potential biomarkers for latent and active TB infections based on miRNA or whole genome microarray studies.

https://doi.org/10.1371/journal.pone.0126018.t005

The microRNA profile in the human macrophage was quite different from that of whole blood, sputum and PBMCs from the literature. In our study, some of the miRNAs were proven to play key roles in the immune and inflammatory pathways, and their biological targets in MTB infection have been previously described (Table 6). The miRNA-146 family was found to play key roles in the anti-inflammatory reaction. miR-146b could be induced by LPS or PG from bacteria [42]. miR-146a/b was a negative regulator of constitutive NF-kB activity, which results in the suppression of IL-1 receptor-associated kinase 1 and TNF receptor-associated factor 6 protein levels [42,43]. In our study, the expression of miR-146b in the LTBI group was significantly higher than that in active TB infections. We propose that hsa-146b-3p may be highly related to the LTBI.

thumbnail
Download:
Table 6. Previously reported microRNAs with differential expression related to current study of MTB infections and their validated transcript targets.

https://doi.org/10.1371/journal.pone.0126018.t006

miR-21 can be induced after Bacillus Calmette-Guerin (BCG) vaccination by NF-kB activation. miR-21 suppressed the IL-12 production by targeting IL-12p35, which impaired anti-mycobacterial T cell responses both in vitro and in vivo. Additionally, miR-21 also promoted dendritic cell (DC) apoptosis by targeting Bcl-2. Therefore, miR-21 may potentially be involved in the fine-tuning of the anti-mycobacterial Th1 response and in regulating the efficacy of BCG vaccination [44–47].

miR-150 has been one of the extensively studied miRNAs, and it has been demonstrated to be selectively expressed in mature naive B and T cells, being down-regulated in their progenitors or in lymphocyte activation and strongly upregulated as maturation progresses [48–51]. The well-known targets for miR-150 are NOTCH3 (a member of the Notch receptor family) and c-Myb (a transcription factor that plays an essential role in the hematopoietic process that plays important roles both in T-cell differentiation and leukemogenesis). In our study, the Beijing/W clinical strains suppressed the miR-150 and miR-21expression and they may play a role in virulence. Lower expression of miR-150 in the active TB patients compared with the latent and healthy controls may be due to the reduced mature T cells and B cells in patients with active TB, as previous studies have shown [36].

Both miR-150 and miR-140-3p were differentially expressed in macrophages infected in vitro and those from active TB patients. These two miRNAs are related with the secondary signal transduction pathway, which and likely involved in MTB infection. Four predicted pathways, including Wnt signalling pathway, insulin signaling pathway, TGF-β signalling pathway and glycosaminoglycan biosynthsis, are involved in Beijing/W & non-Beijing/W (Table 2) and active MTB& LTBI (Table 4) studies. This reaffirms the involvement of the inflammatory defence and signal transduction and cell communication in the macrophages in in MTB infection in vitro and in the host.

Four pathways of cell membrane and communication (adherens junction, gap junction, glycosaminoglycan biosynthsis-heparan sulfate/keratin sulfate metabolite), suggesting that Beijing/W TB strain may affect macrophage survival by altering their cell membrane structure and limit the downstream host immunological defence reaction.

The inflammatory miRNA miR-146b-3p, miR-101 and the cell survival miRNA miR-193a-3p and miR-296-5p were only found differentially expressed in macrophages of active TB group, suggesting response that alters macrophage survival in the infected host.

In addition, compared with whole blood, the microRNA profile revealed from the adherent human macrophages reflect the molecular changes in the TB-engulfed macrophages, bringing insights into the immunological defence mechanisms of these macrophages, where the initial clearance of MTB takes place during infection. On the contrary, the microRNA profiles of blood are the orchestrated outcome of all inflammatory cells and their immune mediators in the host-bacterial interaction, not simply MTB infection of a single immune cell type [11, 28, 32, 33]. Differentially expressed miRNAs and their transcriptional targets might potentially affect the regulation of multiple biological networks. Pathway analysis of our expression profile determined different transcripts that were modified by these miRNAs. These results provide clues for the identification of transcriptionally regulated mechanisms of key biological processes in TB, enhance our understanding of the fundamental biology of MTB, and offer leads for new diagnostics in the future.

Supporting Information

S1 Fig. Clustering analysis of the 16 miRNAs was performed using DataAssist 3.0v based on ΔCt-values of the TLDA results.

Upregulated miRNAs are designated by various shades of red and down-regulated miRNAs by various shades of green.

https://doi.org/10.1371/journal.pone.0126018.s001

(TIF)

S2 Fig. miRNA expression levels in human macrophages with LTBI, active MTB disease and in healthy controls.

Statistical analysis between two groups was performed using the unpaired t-test. Individual values were denoted by black dots/squares/triangles from each group. The mean RQ and S.D. of each group were represented by the ------- bar and short bars --- in each figure, respectively.

https://doi.org/10.1371/journal.pone.0126018.s002

(TIF)

S1 Table. Characteristics of active TB, latent and healthy controls in this study.

https://doi.org/10.1371/journal.pone.0126018.s003

(DOCX)

Acknowledgments

The study was supported by the Health and Medical Research Fund (previously Research Fund for the Control of Infectious Diseases), Food and Health Bureau, HKSAR government (RFCID No. 09080392). We are also indebted to the administrators and technicians of the BSL3 Laboratory, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong for their invaluable advice and support in this study.

Author Contributions

Conceived and designed the experiments: EL MI KFT RCYC. Performed the experiments: LZ EL. Analyzed the data: LZ EL MI. Contributed reagents/materials/analysis tools: LZ EL NL GL KFT RCYC MI. Wrote the paper: LZ MI NL.

References

  1. 1. Gan H, Lee J, Ren F, Chen M, Kornfeld H, Remold HG.Mycobacterium tuberculosis blocks crosslinking of annexin-1 and apoptotic envelope formation on infected macrophages to maintain virulence. Nat Immunol. 2008;9: 1189–1197. pmid:18794848
  2. 2. Divangahi M, Chen M, Gan H, Desjardins D, Hickman TT, Lee DM, et al. Mycobacterium tuberculosis evades macrophage defenses by inhibiting plasma membrane repair. Nat Immunol. 2009;10: 899–906. pmid:19561612
  3. 3. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004;116: 281–297. pmid:14744438
  4. 4. Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, et al. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 2005;433: 769–773. pmid:15685193
  5. 5. Jackson AL, Levin AA. Developing microRNA therapeutics: approaching the unique complexities. Nucleic Acid Ther. 2012; 22:213–225. pmid:22913594
  6. 6. Liu YH, Wang XJ, Jiang J, Cao ZH, Yang BF, Cheng X. Modulation of T cell cytokine production by miR-144* with elevated expression in patients with pulmonary tuberculosis. Mol Immunol. 2011;48: 1084–1090. pmid:21367459
  7. 7. Chatterjee S, Dwivedi VP, Singh Y, Siddiqui I, Sharma P, Van Kaer L, et al. Early secreted antigen ESAT-6 of Mycobacterium tuberculosis promotes protective T helper 17 cell responses in a Toll-Like receptor-2-dependent manner. PLoS Pathogens 2011;7:e1002378. pmid:22102818
  8. 8. Singh Y, Kaul V, Mehra A, Chatterjee S, Tousif S, Dwivedi VP, et al. Mycobacterium tuberculosis controls miR-99b expression in infected murine dendritic cells to modulate host immunity. J Biol Chem. 2013; 288: 5056–5061. pmid:23233675
  9. 9. Rajaam MV, Ni B, Morris JD, Brooks MN, Carlson TK, Bakthavachalu B, et al. Mycobacterium tuberculosis lipomannan blocks TNF biosynthesis by regulating macrophage MAPK-activated protein kinase 2 (MK2) and microRNA miR-125b. Proc Natl Acad Sci USA 2011;108: 17408–17413. pmid:21969554
  10. 10. O’Connell RM, Taganov KD, Boldin MP, Cheng G, Baltimore D. MicroRNA- 155 is induced during the macrophage inflammatory response. Proc Natl Acad Sci USA 2007;104: 1604–1609. pmid:17242365
  11. 11. Fu Y, Yi Z, Wu X, Li J, Xu F. Circulating microRNAs in patients with active pulmonary tuberculosis. J Clin Microbiol. 2011;49:4246–4251. pmid:21998423
  12. 12. Wu J, Lu CY, Diao N, Zhang S, Wang S, Wang F, et al. Analysis of microRNA expression profiling identifies miR-155 and miR-155* as potential diagnostic markers for active tuberculosis: a preliminary study. Hum Immunol 2012;73:31–37. pmid:22037148
  13. 13. Chen J, Tsolaki AG, Shen X, Jiang X, Mei J, Gao Q, et alDeletion-targeted multiplex PCR (DTM-PCR) for identification of Beijing/W genotypes of Mycobacterium tuberculosis. Tuberculosis (Edinb.) 2007;87:446–449. pmid:17632035
  14. 14. Wong KC, Leong WM, Law HK, Ip KF, Lam JT, Yuen KY, et al. Molecular characterization of clinical isolates of Mycobacterium tuberculosis and their association with phenotypic virulence in human macrophages. Clin Vaccine Immunol. 2007;10: 1279–84. pmid:17715326
  15. 15. Tsuchiya S, Kobayashi Y, Goto Y, Okumura H, Nakae S, Konno T, et al. Induction of maturation in cultured human monocytic leukemia cells by a phorboldiester. Cancer Research 1982;42: 1530–1536. pmid:6949641
  16. 16. Dweep H, Sticht C, Pandey P, Gretz N. miRWalk—database: prediction of possible miRNA binding sites by “walking” the genes of 3 genomes. J Biomed Inform. 2011; 44:839–847. pmid:21605702
  17. 17. Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, et al. DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res 2012;40:W498–504. pmid:22649059
  18. 18. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30: 207–10. pmid:11752295
  19. 19. Baltimore D, Boldin MP, O’Connell RM, Rao DS, Taganov KD. MicroRNAs: new regulators of immune cell development and function. Nat Immunol. 2008;9: 839–845. pmid:18645592
  20. 20. Moschos SA, Williams AE, Perry MM, Birrell MA, Belvisi MG, Lindsay MA. Expression profiling in vivo demonstrates rapid changes in lung microRNA levels following lipopolysaccharide-induced inflammation but not in the anti-inflammatory action of glucocorticoids. BMC Genomics 2007;240:1–12.
  21. 21. Buu TN, van Soolingen D, Huyen MN, Lan NT, Quy HT, Tiemersma EW, et al. Increased transmission of Mycobacterium tuberculosis Beijing genotype strains associated with resistance to streptomycin: a population-based study. PLoS One 2012;7: e42323. pmid:22912700
  22. 22. Subbian S, Bandyopadhyay N, Tsenova L, O Brien P, Khetani V, Kushner NL, et al. Early innate immunity determines outcome of Mycobacterium tuberculosis pulmonary infection in rabbits. Cell Commun Signal 2013;11:60. pmid:23958185
  23. 23. Wu K, Dong D, Fang H, Levillain F, Jin W, Mei J, et al. An interferon-related signature in the transcriptional core response of human macrophages to Mycobacterium tuberculosis infection. PLoS One 2012;7: e38367. pmid:22675550
  24. 24. Lucotti S, Rainaldi G, Evangelista M, Rizzo M. (2013) Fludarabine treatment favors the retention of miR-485-3p by prostate cancer cells: implications for survival. Mol Cancer 12(1):52. pmid:23734815
  25. 25. Yang H, Cho ME, Li TW, Peng H, Ko KS, Mato JM, et al. MicroRNAs regulate methionine adenosyltransferase 1A expression in hepatocellular carcinoma. J Clin Invest. 2013;123: 285–298. pmid:23241961
  26. 26. Chen CF, He XL, Arslan AD, Mo YY, Reinhold WC, Pommier Y, et al. Novel regulation of nuclear factor-γb by miR-485-3p affects the expression of DNA topoisomerase IIα and drug responsiveness. Mol Pharmacol. 2011;79: 735–741. pmid:21252292
  27. 27. Jacobsen M, Repsilber D, Gutschmidt A, Neher A, Feldmann K, Mollenkopf HJ, et al. Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis. J Mol Med (Berl.) 2007;85: 613–621. pmid:17318616
  28. 28. Maertzdorf J, Repsilber D, Parida SK, Stanley K, Roberts T, Black G, et al. Human gene expression profiles of susceptibility and resistance in tuberculosis. Genes Immun. 2011;12: 15–22. pmid:20861863
  29. 29. Harari A, Rozot V, Enders FB, Perreau M, Stalder JM, Nicod LP, et al. Dominant TNF-alpha(+) Mycobacterium tuberculosis-specific CD4(+) T cell responses discriminate between latent infection and active disease. Nat Med. 2011;17:372–U174. pmid:21336285
  30. 30. Wang C, Yang S, Sun G, Tang X, Lu S, Neyrolles O, et al. Comparative miRNA expression profiles in individuals with latent and active tuberculosis. PLoS One 2011; 6:e25832. pmid:22003408
  31. 31. Yi Z, Fu Y, Ji R, Li R, Guan Z. Altered microRNA signatures in sputum of patients with active pulmonary tuberculosis. PLoS One 2012;7: e43184. pmid:22900099
  32. 32. Qi Y, Cui L, Ge Y, Shi Z, Zhao K, Guo X, et al. Altered serum microRNAs as biomarkers for the early diagnosis of pulmonary tuberculosis infection. BMC Infect Dis. 2012;12:384. pmid:23272999
  33. 33. Miotto P, Mwangoka G, Valente IC, Norbis L, Sotgiu G, Bosu R, et al. miRNA signatures in sera of patients with active pulmonary tuberculosis. PLoS One 2013;8: e80149. pmid:24278252
  34. 34. Zhang X, Guo J, Fan S, Li Y, Wei L, Yang X, et al. Screening and identification of six serum microRNAs as novel potential combination biomarkers for pulmonary tuberculosis diagnosis. PLoS One 2013;8: e81076. pmid:24349033
  35. 35. Zhang H, Sun Z, Wei W, Liu Z, Fleming J, Zhang S, et al. Identification of serum microRNA biomarkers for tuberculosis using RNA-seq. PLoS One 2014;9: e88909. pmid:24586438
  36. 36. Berry MP, Graham CM, McNab FW, Xu Z, Bloch SA, Oni T, et al.An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 2010;466: 973–U998. pmid:20725040
  37. 37. Stavrum R, Valvatne H, Stavrum AK, Riley LW, Ulvestad E, Jonassen I, et al. Mycobacterium tuberculosis Mce1 protein complex initiates rapid induction of transcription of genes involved in substrate trafficking. Genes Immun. 2012;13: 496–502. pmid:22695749
  38. 38. Sharbati J, Lewin A, Kutz-Lohroff B, Kamal E, Einspanier R, Sharbati S, et al. Integrated microRNA-mRNA-analysis of human monocyte derived macrophages upon Mycobacterium avium subsp. hominissuis infection. PLoS One 2011;6:e20258. pmid:21629653
  39. 39. Kumar M, Kumar Sahu S, Kumar R, Subuddhi A, Kumar Maji R, Jana K, et al. MicroRNA let-7 Modulates the Immune Response to Mycobacterium tuberculosis Infection via Control of A20, an Inhibitor of the NF-κB Pathway. Cell Host & Microbe 2015;17: 345–56.
  40. 40. Furci L, Schena E, Miotto P, Cirillo DM. Alteration of human macrophages microRNA expression profile upon infection with Mycobacterium tuberculosis. Int J Mycobacteriol. 2013;2: 128–34.
  41. 41. Dasa K, Saikolappana S, Dhandayuthapani S. Differential expression of miRNAs by macrophages infected with virulent and avirulent Mycobacterium tuberculosis. Tuberculosis 2013;93; Supplement: S47–S50.
  42. 42. Taganov KD, Boldin MP, Chang KJ, Baltimore D. NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci USA2006;103: 12481–12486. pmid:16885212
  43. 43. Bhaumik D, Scott GK, Schokrpur S, Patil CK, Campisi J, Benz CC, et al. Expression of microRNA-146 suppresses NF-kappaB activity with reduction of metastatic potential in breast cancer cells. Oncogene 2008;27:5643–5647. pmid:18504431
  44. 44. Bao L, Yan Y, Xu C, Ji W, Shen S, Xu G, et al. MicroRNA-21 suppresses PTEN and hSulf-1 expression and promotes hepatocellular carcinoma progression through AKT/ERK pathways. Cancer Lett 2013;337: 226–236. pmid:23684551
  45. 45. Zhu S, Wu H, Wu F, Nie D, Sheng S, Mo YY. MicroRNA-21 targets tumor suppressor genes in invasion and metastasis. Cell Res. 2008;18: 350–359. pmid:18270520
  46. 46. Liu C, Yu J, Yu S, Lavker RM, Cai L, Liu W, et al. MicroRNA-21 acts as an oncomir through multiple targets in human hepatocellular carcinoma. J Hepatol. 2010;53: 98–107. pmid:20447717
  47. 47. Wu Z, Lu H, Sheng J, Li L. Inductive microRNA-21 impairs anti-mycobacterial responses by targeting IL-12 and Bcl-2. FEBS Lett. 2012;586: 2459–2467. pmid:22710123
  48. 48. Lin YC, MW Kuo, J Yu, HH Kuo, RJ Lin, WL Lo, et al. c-Myb is an evolutionary conserved miR-150 target and miR-150/c-Myb interaction is important for embryonic development. Mol Biol Evol. 2008;25: 2189–2198. pmid:18667440
  49. 49. Xiao C, Calado DP, Galler G, Thai TH, Patterson HC, Wang J, et al. miR-150 controls B cell differentiation by targeting the transcription factor c-Myb. Cell 2007;131: 146–159. pmid:17923094
  50. 50. Zhou B, Wang S, Mayr C, Bartel DP, Lodish HF. miR-150, a microRNA expressed in mature B and T cells, blocks early B cell development when expressed prematurely. Proc Natl Acad Sci USA 2007;104: 7080–7085. pmid:17438277
  51. 51. Ghisi M, Corradin A, Basso K, Frasson C, Serafin V, Mukherjee S, et al. Modulation of microRNA expression in human T-cell development: targeting of NOTCH3 by miR-150. Blood 2011;117:7053–7062. pmid:21551231
  52. 52. Schaefer JS, Montufar-Solis D, Vigneswaran N, Klein JR. Selective upregulation of microRNA expression in peripheral blood leukocytes in IL-10-/- mice precedes expression in the colon. J Immunol. 2011;187: 5834–5841. pmid:22043014
  53. 53. Jude JA, Dileepan M, Subramanian S, Solway J, Panettieri RA Jr, Walseth TF, et al. miR-140-3p regulation of TNF-α-induced CD38 expression in human airway smooth muscle cells. Am J Physiol Lung Cell Mol Physiol. 2012;303: L460–468. pmid:22773691
  54. 54. He Q, Zhou X, Li S, Jin Y, Chen Z, Chen D, et al. MicroRNA-181a suppresses salivary adenoid cystic carcinoma metastasis by targeting MAPK-Snai2 pathway. Biochim Biophys Acta 2013;1830: 5258–5266. pmid:23911747
  55. 55. Shin KH, Bae SD, Hong HS, Kim RH, Kang MK, Park NH, et al. miR-181a shows tumor suppressive effect against oral squamous cell carcinoma cells by downregulating K-ras. Biochem Biophys Res Commun. 2011;404:896–902. pmid:21167132
  56. 56. Zhang R, Tian A, Wang J, Shen X, Qi G, et al. miR26a Modulates Th17/T reg Balance in the EAE Model of Multiple Sclerosis by Targeting IL6. Neuromolecular Med. 2015;17:24–34. pmid:25362566
  57. 57. Zhang X, Xiao D, Wang Z, Zou Y, Huang L, Lin W, et al. MicroRNA-26a/b regulate DNA replication licensing, tumorigenesis, and prognosis by targeting CDC6 in lung cancer. Mol Cancer Res. 2014;12: 1535–46. pmid:25100863
  58. 58. Dong J, Sui L, Wang Q, Chen M, Sun H. MicroRNA-26a inhibits cell proliferation and invasion of cervical cancer cells by targeting protein tyrosine phosphatase type IVA 1. Mol Med Rep. 2014;10: 1426–32. pmid:24939702
  59. 59. Kwon JE, Kim BY, Kwak SY, Bae IH, Han YH. Ionizing radiation-inducible microRNA miR-193a-3p induces apoptosis by directly targeting Mcl-1. Apoptosis 2013;18: 896–909. pmid:23546867
  60. 60. Yu T, Li J, Yan M, Liu L, Lin H, et al. MicroRNA-193a-3p and -5p suppress the metastasis of human non-small-cell lung cancer by downregulating the ERBB4/PIK3R3/mTOR/S6K2 signaling pathway. Oncogene 2015;34: 413–23. pmid:24469061
  61. 61. Hong L, Han Y, Zhang H, Li M, Gong T, Sun L, et al. The prognostic and chemotherapeutic value of miR-296 inesophageal squamous cell carcinoma. Ann Surg. 2010; 251: 1056–1063. pmid:20485139