Hashimoto et al. Translational Medicine Communications
https://doi.org/10.1186/s41231-020-00078-7(2020) 5:25

RESEARCH

Translational Medicine
Communications

Open Access

Developing a diagnostic method for latenttuberculosis infection using circulating
miRNA
Shoji Hashimoto1, Hong Zhao2, Michiyo Hayakawa3, Koichi Nakajima2, Y-h Taguchi4 and Yoshiki Murakami5*

Abstract
Background: Mycobacterium tuberculosis is known to cause latent tuberculosis infection (LTBI) in 25–50% of thecases, of whom 10–20% develop active tuberculosis (TB). Notably, no marker currently exists for judging thetherapeutic effect of TB; it is currently judged by chest X-ray and clinical symptoms. We attempted to establish amarker for distinguishing LTBI from active TB and to identify the probability of recurrence after TB treatment, using
information on circulating miRNA expression.
Methods: In total, 32 patients were enrolled in this study: 16 with an onset or recurrence of active TB, and 16 with
LTBI showing positive interferon-gamma release assays (IGRA) test and chest X-ray. Total RNA from serum in an
exosome-rich fraction was first extracted, followed by miRNA expression analysis using a next-generation sequencer,
then, this data were analyzed using miRDeep2.
Results: Using the expression information of eight miRNAs, LTBI and TB could be diagnosed with an accuracy of
71.8% (odds ratio: 6.16, p value = 3.20e-02).
Conclusions: A novel method for efficiently differentiating between LTBI and active TB was established. Thismethod appears to be promising for evaluating the therapeutic effect of TB, as it can be performed in a minimallyinvasive manner.
Keywords: LTBI, TB, miRNA, Next-generation sequencing

Background
Tuberculosis (TB) is an infectious disease caused by
Mycobacterium tuberculosis (Mtb) that affects the lungs.
It is estimated that nearly 25% of the world’s population
is affected by TB [1]. The reason behind the uncontrollability of TB infection is not only poverty and coinfection
with human immunodeficiency virus but also the lack ofadequate information on the transmission and pathogenesis of this disease. Notably, nearly 5–15% of the people
infected with TB develop overt infection, whereas mostpatients have latent tuberculosis infection (LTBI). However, patients with overt infection are at a risk of* Correspondence: yoshikim@tokyo-med.ac.jp
5Department of Molecular Pathology, Tokyo Medical University, 6-1-1
Shinjuku, Shinjuku-ku, Tokyo 160-8402, Japan
Full list of author information is available at the end of the article

reactivation after completing their treatment [2]. Therefore, in TB treatment, it is important to diagnose LTBI
and prevent the development of TB from LTBI. Once infected with TB, most people remain asymptomatic andnoncontagious. However, 5–15% of cases with LTBI may
progress to active TB during their lifetime and become
infectious [3]. The risk of progression becomes evenhigher in younger children [4] and immunocompromised or immunosuppressed patients [5, 6].
Though for a long time tuberculin skin test (TST) had
been used for the diagnosis of TB, now blood-based
interferon-gamma release assays (IGRAs) are widelyused to diagnose TB and LTBI because of their simplicity and high specificity [7], and positive IGRA test, normal chest X-ray and contact history (especially within 2
years in high prevalence area) are required to diagnose© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Hashimoto et al. Translational Medicine Communications(2020) 5:25

Page 2 of 7

LTBI [8]. Moreover it is difficult to determine whether
LTBI, a potential infectious disease has been successfully
treated (prevented) [9, 10]. Therefore, an accurate andconvenient diagnostic method for LTBI has been
desired.
Generally, miRNA is a noncoding type of RNA thathas a length of about 20 bases, which regulates the expression of a target gene in a base-sequence-specificmanner. Many researchers have reported that abnormal
miRNA expression is related to the onset and exacerbation of many diseases for example, infectious diseases,
degenerative diseases and cancer [11]. In addition, considering the fact that miRNAs are released extracellularly
by cell-derived endoplasmic reticulum such as exosomes,
miRNA in exosome reflects cellular information, and it
is possible to obtain clinical information similar to thatof collecting relevant organs [12]. Infection with Mtb
first induces an interaction with macrophages, following
which macrophages infected with Mtb release particular
miRNAs packaged in exosomes into the extracellularspace [13]. Several researchers have reported that manyhighly expressed miRNAs (miR-484, miR-425, and miR96) in exosomes are useful in the TB diagnosis [14].
However, this method for analyzing the miRNA profiles
of TB and LTBI has not been applied in the diagnosis of
LTBI [15].
Reducing the incidence of active TB requires new
diagnostic tools and therapeutic options along with abetter understanding of the microbiological and clinicalspectrum of TB. In this study, we attempt to establish adiagnostic method using circulating miRNAs to develop
a biomarker that can help easily evaluate the diagnostic
efficiency and therapeutic effects of LTBI and active TB.

extracted RNA were qualified using an Agilent 2100
Bioanalyzer (Agilent Technologies, Santa Clara, CA,USA).
To select an 18–30 nt stripe and recycle, total RNA wassize-fractionated using polyacrylamide gel electrophoresis(PAGE) gel. An adaptor sequence was ligated with sizefractionated RNA and amplified. All PCR products were
purified using PAGE, and then the recycled products weredissolved in an nuclease free water and he PCR productprepared was labeled to create a library. The library wasthen quantified, and the qualified libraries were sequenced
using an Illumina HiSeq 4000 System (Illumina, San
Diego, CA, USA). All sequencing data were deposited in
NCBI’s Gene Expression Omnibus and are accessible
through the GEO accession number GSE149645.

Methods

is a core tensor and ul1 j ∈ℝ 1616 ,where G ∈ ℝ
2230173017
ul2 k ∈ℝ , and ul3 i ∈ℝare singular value orthogonal matrices. To identify the values of ul3 i used formiRNA selection, we identified which ul3 i represents patient- or normal-control-independent components andwhich ul1 j represents a distinction between healthy controls and normal controls. Subsequently, we verified
whether l1 = 1 and l2 = 2 represent the required properties. We then tried to find the largest absolute value ofG (1, 2, l3), which we found to be G (1, 2, 2). This meansthat u2i should be used for miRNA selection. Therefore,
the p-values were attributed to the ith miRNA as
follows:"  #u2i 2
Pi ¼ Pχ 2 >;σ2Statistics

In this study, all miRNAs were selected using the recently
proposed unsupervised feature extraction method basedon tensor decomposition (TD) [16]. In total, 32 FASTQ
files were mapped to the hg38 genome using miRDeep2(https://www.mdc-berlin.de/content/mirdeep2-documentation) under default settings. The generated “miRNAs_
expressed_all_samples_X.csv” (where X is the time stamp)
files were collected. Subsequently, the 32 expression profiles were formatted as a tensor, xijk ∈ ℝ3017 × 16 × 2, which
represents the expression of the ith miRNA of the jth normal controls (k = 1) or patients (k = 2). Notably, xijk was
P 2
normalized such that ∑ixijk = 0 andi xijk ¼ N . Tensor
decomposition was then obtained using higher-order singular value decomposition as follows:X
xijk ¼
G ðl1 ; l2 ; l3 Þul1 j ul2 k ul3 i ;
l l l
1 2 3

Clinical backgroundThe study subjects comprised 16 patients with active
TB, and 16 patients with LTBI (all above 18 years old)
who visited or were hospitalized at the Osaka Habikino
Medical Center for evaluation and initial treatment. This
study was performed according to the principles of the
Declaration of Helsinki. All study participants provided
informed consent. Moreover, this clinical analysis was
ethically reviewed and approved by the Osaka Habikino
Medical Center (no. 773–4) and Osaka City University(no. 3747).
RNA extraction and next-generation sequencing analysis

Using 900 μL of serum, an exosome-rich fraction was
extracted using ExoQuick (System Biosciences, Palo
Alto, CA, USA), and total RNA was extracted from a
200 μL ExoQuick-treated solution using an RNeasy Mini
Kit (Qiagen, Hilden, Germany). The concentration, integrity number, 28S/18S ratio, and sample size of the

16 × 2 × 3017

where P χ 2 ½> x is the cumulated χ2 distribution, in which
the argument is larger than x, and σ2 is the standard deviation. The p-values attributed to the ith miRNA wereHashimoto et al. Translational Medicine Communications(2020) 5:25

corrected by the Benjamini-Hochberg criterion, and ninemiRNA probes (hsa-let-7i-5p, miR-122-5p, miR-148a-3p,
miR-151a-3p, miR-21-5p, miR-423-5p, miR-451a, andtwo miR-486-5p probes), associated with adjusted pvalues less than 0.01 were selected. Then, the matrix
xin ∈ ℝ9 × 32, which represents the ith miRNA within thenine selected miRNAs of the nth patients/normal controls, was formatted. Then, xin was normalized such that
P 2
∑ixin = 0 andi xin ¼ N . Principal component analysis

Page 3 of 7

was then applied to xin such that the principal component loading is attributed to the nth normal controls
and patients. Then, the first and fourth principal component loadings were used to discriminate between the 16
normal controls and 16 patients. Linear discriminant
analysis was also performed with two selected principal component loadings with leave-one-out crossvalidation, and all p-values were calculated using Fisher’s exact test.

Table 1 Clinical background
Basic patient information

Diagnosis of TB

Sample ID

Diagnosis

Age

Sex

Complications

PCR

QFT

Sputum culture

LP01

LTBI

44

F

(−)

6.52

(−)

LP02

LTBI

52

M

(−)

5.6

(−)

LP03

LTBI

54

F

(−)

2.03

(−)

LP04

LTBI

52

F

(−)

1.13

(−)

LP05

LTBI

51

F

(−)

0.87

(−)

LP06

LTBI

61

F

(−)

7.8

(−)

LP07

LTBI

57

F

(−)

> 10

(−)

LP08

LTBI

24

F

(−)

7.59

(−)

LP09

LTBI

48

M

(−)

> 10

(−)

LP10

LTBI

50

F

(−)

2.45

(−)

LP16

LTBI

78

M

NP

0.34

(−)

LP17

LTBI

57

M

NP

2.38

(−)

LP18

LTBI

67

F

NP

0.98

(−)

LP22

LTBI

70

M

Gastric cancer

NP

2.45

(−)

LP25

LTBI

75

M

IP

NP

9.5

(−)

LP27

LTBI

81

F

NP

1.84

(−)

TP01

TB

49

F

(−)

−0.05

(−)

TP02

TB

56

M

Colon cancer

NP

2.23

(+)

TP03

TB

62

F

Pleuritis

(−)

3.27

(−)

TP04

TB

86

M

(+)

NP

(+)

TP05

TB

83

M

(+)

T-SPOT (+)

(+)

TP06

TB

75

M

(−)

NP

(−)

TP07

TB

62

M

CLL

(+)

T-SPOT (+)

(+)

TP08

TB

62

M

Tongue cancer

(+)

NP

(+)

TP09

TB

85

F

NP

NP

(+)

TP10

TB

76

M

(−)

NP

(+)

TP12

TB

35

F

(+)

NP

(+)

TP13

TB

29

M

LN (+)

NP

(−)

TP17

TB

29

F

(+)

NP

(+)

TP25

TB

54

F

(+)

NP

(+)

TP33

TB

46

F

(+)

NP

(+)

TP41

TB

51

M

(+)

NP

(+)

Miliary TB

Lymph node TB

OCDM

Abbreviations: TB tuberculosis, LTBI latent tuberculosis infection, QFT QuantiFERON-TB, IP Interstitial Pneumonia, CLL chronic lymphocytic leukemia, DM diabetes
mellitus, OC oral cavity cancer, NP not performed

Hashimoto et al. Translational Medicine Communications(2020) 5:25

Table 2 Diagnosis using miRNA information

Comparison of the expression of individual miRNAs

Prediction
LTBI
ResultPage 4 of 7

Active TB

LTBI

12

5

Active TB

4

11

Results
Clinical background

Among the study population, one patient with
LTBI had gastric cancer and four patients with active TB had colon cancer, tongue cancer, chronic
lymphocytic leukemia, and oral cancer. In
addition, one patient with LTBI had interstitial
pneumonia and one case of active TB had diabetes. On the other hand, in cases with active TB,
extrapulmonary lesions were observed in twocases (miliary TB, lymph node TB; Table 1 andSupplementary Table 1).

Using the expression information of eight miRNAs (hsalet-7i-5p, miR-122-5p, miR-148a-3p, miR-151a-3p, miR21-5p, miR-423-5p, miR-451a, and miR-486-5p), wewere able to classify our patients into 16 patients with
LTBI and 16 patients with active TB, with a diagnostic
accuracy of 71.8% (odds ratio: 6.16, p = 3.20e-02;
Table 2).
Notably, four miRNAs (hsa-miR-122-5p, miR-151a-3p,
miR-451a, and miR-486-5p) were found to exhibit ahigher expression in LTBI than in active TB, with a significant difference observed in miR-122-5p (p < 0.05).
On the other hand, four miRNAs were found to exhibit
a higher expression in active TB than in LTBI (hsa-let7i-5p, miR-148a-3p, miR-21-5p, and miR-423-5p), whichshowed no significant difference in terms of expression(Fig. 1).
Universally expressed novel miRNA candidates

Novel miRNA candidates were analyzed using miRDeep2 (Fig. 2). In total, four types were expressed in allcases, with the expression of these four miRNA

Fig. 1 Expression pattern of the nine miRNAs used in the diagnosis of active TB/LTBI. The vertical axis shows the relative expression level ofmiRNA, and the dots represent the relative expression levels in individuals

Hashimoto et al. Translational Medicine Communications(2020) 5:25

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Fig. 2 Virtual structure of novel miRNA candidates. The figure shows the structure of 11 miRNA candidates with significantly different expressionsof LTBI and active TB

candidates in LTBI being significantly higher than thatin active TB (p < 0.05; Table 3).
miRNA expression in airway-associated cells

To examine the origin of miRNA in circulating blood,
we compared the expression information of miRNA individually in the tracheal epithelial cells, bronchial epithelial cells, and alveolar epithelial cells of healthy
subjects. The expression of miRNA was quoted from themiRNA Atlas of RIKEN (https://fantom.gsc.riken.jp/5/
suppl/De_Rie_et_al_2017/) [17]. As shown in Fig. 3, theexpression of miR-122-5p and miR-451a is low, the expression of other miRNAs is abundant, and the expression of miR-21-5p in particular is enhanced. The

miRNAs of which expression level was high in TB as
compared LTBI, were highly expressed in airway related
cells. However, no significant differences were found in
the expression of these miRNAs among the three types
of epithelial cells mentioned above.

Discussion
Though IGRAs are considered to be useful for early
diagnosis of LTBI and active TB instead of TST [7], differential diagnosis of LTBI and active TB is often
difficult.
Patients with LTBI show positive IGRA test and normal chest X-ray. Oral isoniazid (INH) treatment for 6
months is considered an effective standard treatment toTable 3 List of novel miRNA candidates showing ubiquitous expression
Temporary miRNA name(precursor coordinate)

Mature read count
Average LTBI

Average TB

chr1:220118169..220118227:−1600.1

662.6

LTBI/
TP
ratio

p-value

miRNAwith thesame seed

2.41

1.96e-03

ptr-miR-194

chr18:58451087..58451145:+

1,356,949.3

482,662.4

2.81

5.85e-03

ptr-miR-122

chr10:101601428..101601483:−

3652.01607.72.27

2.91e-02

None

chr10:101601430..101601485:+

3652.01607.72.27

2.91e-02

None

Precursor coordinate: existing chromosome, gene start position and end position, sense strand (+), antisense strand (−). p-value: Student’s t-test. miRNA with thesame seed: a known miRNA in which the seed sequence of the new miRNA candidate is identical as analyzed by miRBase ver. 22.1 (www.mirbase.org)
LTBI latent tuberculosis infection, TB tuberculosis

Hashimoto et al. Translational Medicine Communications(2020) 5:25

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Fig. 3 miRNA expression levels in airway epithelial cells in next-generation sequencing. The figure shows the expression numbers of the eightmiRNAs used in the diagnosis of active TB and LTBI in bronchial epithelium (BE), tracheal epithelium (TE), alveolar epithelium (AE), and bronchialsmooth muscle (BS) cells. The miRNA expression profiles of the BE (BE-1, BE-2, and BE-4), TE (TE-2, TE-3), AE (AE-1, AE-2, and AE-3), and BS (BS-1,
BS-2) cells were obtained from three, two, three, and two healthy volunteers, respectively

prevent the development of TB in LTBI patients infected
within 2 years. Though oral anti-TB treatment reduces
the incidence of active TB by about one-third [18], somepatients with TB, especially those who originally haveliver dysfunction or heavy drinkers, develop liver dysfunction associated with TB treatment. In addition, LTBI
and active TB due to INH-resistant Mtb increase therate of treatment failure or relapse from 1.8% [95% confidence interval: 1.2–2.6] to 10.9% [95% confidence interval: 5.9–20] compared to Mtb susceptible to all anti-TB
drugs [19]. Therefore, three evaluations are important:
1) whether the case is LTBI before the onset of TB, 2)
there is no complicated liver dysfunction, and 3) the
source of infection is not INH-resistant TB. Since thetherapeutic effect cannot be judged using IGRA, it isjudged by the improvement or deterioration of chest Xray and clinical symptoms.
Several reports exist on analysis using miRNA information from body fluids. When healthy individuals
and individuals with active TB were diagnosed using
miRNAs from exosomes derived from macrophages,
an area under the receiver operating characteristic
curve of 0.78 was obtained [13]. In addition, the information obtained from the six miRNAs (miR-20a,
miR-20b, miR-26a, miR-106a, miR-191, miR-486) inplasma exosomes was useful for differentiating patients with tuberculous meningitis from those with
TB [20]. Moreover, exosome miRNAs in pleural effusion were compared between three groups: an active
TB group, a healthy control group, and a lung cancergroup. Both miR-148a-3p and miR-451a were foundto be highly expressed in the active TB group [21].
However, so far, no report exists on the differential
diagnosis of active TB and LTBI.

Moreover, no particular correlation was found between circulating miRNA expression and miRNA expression in normal airway epithelial cells. It was also
observed that the circulating expression of let-7i-5p,
miR-148a-3p, miR-21-5p, and miR-423-5p, which arehighly expressed in airway epithelial cells, is higher incases with active TB than in cases with LTBI. However, the miR-122-5p and miR-451a were found to
exhibit a higher expression in LTBI in circulating
blood than in active TB. We also obtained multiple
novel miRNA candidates that are expressed in allcases. Among these, the expression patterns of LTBI
and active TB were found to be significantly different.
Although the novel miRNA candidates need to be
identified as miRNAs, they are considered as new biomarker candidates.

Conclusions
We attempted to differentiate LTBI from active TB
using information on miRNAs in the circulating
blood. Our results showed high diagnostic accuracy.
Although the number of subjects in this study was
limited, this novel method is promising not only forthe differentiation between LTBI and active TB but
also for evaluating the treatment effect for both, ascompared to conventional IGRAs, chest X-ray, andclinical symptoms.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s41231-020-00078-7.
Additional file 1 : Supplementary Table 1. Clinical background indetail.

Hashimoto et al. Translational Medicine Communications(2020) 5:25

Abbreviations
LTBI: Latent tuberculosis infection; TB: Tuberculosis interferon-gamma releaseassays (IGRA) test; Mtb: Mycobacterium tuberculosis; TST: Tuberculin skin test;
PAGE: Polyacrylamide gel electrophoresis; TD: Tensor decomposition;
INH: Isoniazid

Page 7 of 7

8.

9.
Acknowledgments
The authors thank to Dr. Saori Itami-Matsumoto for preparing nucleic acid.
Authors’ contributions
Hong Zhao, Michiyo Hayakawa, and Koichi Nakajima performed theexperiments, contributed intellectual input, and provided the discussion.
Shoji Hashimoto, Koichi Nakajima, and Y-h Taguchi helped write the manuscript and provided the discussion. Y-h Taguchi performed the nextgeneration sequencing analysis and data processing. Shoji Hashimoto andYoshiki Murakami designed the experiments, analyzed and interpreted thedata, and wrote the manuscript. The authors read and approved the final
manuscript.

10.

11.

12.
13.

Funding
This research was supported by Grant-in-Aid for Scientific Research 16
K09037 (Hong Zhao, Koichi Nakajima, and Yoshiki Murakami) and 20K12067,
20H04848, and 19H05270 (Y-h. Taguchi).
Availability of data and materials
All data used in the manuscript will be provided upon request to thecorresponding author.
Ethics approval and consent to participate
Not applicable.

14.

15.

16.
17.

Consent for publication
All the authors agree for the publication.
Competing interests
The authors have no competing interests to declare.
Author details
1
Osaka Habikino Medical Center, Habikino, Osaka 583-8588, Japan.
2
Department of Immunology, Osaka City University Graduate School of
Medicine, Osaka 545-8585, Japan. 3Department of Pathology and Cell
Regulation, Kyoto Prefectural University of Medicine, Kyoto 602-0841, Japan.
4
Department of Physics, Chuo University, Tokyo 112-8551, Japan.
5
Department of Molecular Pathology, Tokyo Medical University, 6-1-1
Shinjuku, Shinjuku-ku, Tokyo 160-8402, Japan.

18.

19.
20.

21.

tuberculosis: systematic review and meta-analysis. BMC Infect Dis. 2017;17(1):200.
Redelman-Sidi G, Sepkowitz KA. IFN-gamma release assays in the diagnosisof latent tuberculosis infection among immunocompromised adults. Am J
Respir Crit Care Med. 2013;188(4):422–31.
Chee CB, KhinMar KW, Gan SH, Barkham TM, Koh CK, Shen L, Wang YT.
Tuberculosis treatment effect on T-cell interferon-gamma responses tomycobacterium tuberculosis-specific antigens. Eur Respir J. 2010;36(2):355–
61.
Adetifa IM, Ota MO, Jeffries DJ, Lugos MD, Hammond AS, Battersby NJ,
Owiafe PK, Donkor SD, Antonio M, Ibanga HB, et al. Interferon-gamma ELISPOT as a biomarker of treatment efficacy in latent tuberculosis infection: aclinical trial. Am J Respir Crit Care Med. 2013;187(4):439–45.
Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the
management of cancer and other diseases. Nat Rev Drug Discov. 2017;16(3):203–22.
Pos O, Biro O, Szemes T, Nagy B. Circulating cell-free nucleic acids:
characteristics and applications. Eur J Hum Genet. 2018;26(7):937–45.
Alipoor SD, Mortaz E, Tabarsi P, Farnia P, Mirsaeidi M, Garssen J, Movassaghi
M, Adcock IM. Bovis Bacillus Calmette-Guerin (BCG) infection induces
exosomal miRNA release by human macrophages. J Transl Med. 2017;15(1):105.
Alipoor SD, Tabarsi P, Varahram M, Movassaghi M, Dizaji MK, Folkerts G,
Garssen J, Adcock IM, Mortaz E. Serum Exosomal miRNAs are associatedwith active pulmonary tuberculosis. Dis Markers. 2019;2019:1907426.
Lyu L, Zhang X, Li C, Yang T, Wang J, Pan L, Jia H, Li Z, Sun Q, Yue L, et al.
Small RNA profiles of serum exosomes derived from individuals with latent
and active tuberculosis. Front Microbiol. 2019;10:1174.
Y-h T. Unsupervised feature extraction applied to bioinformatics: a PCA
based and TD based approach. Switzerland: Springer international; 2020.
de Rie D, Abugessaisa I, Alam T, Arner E, Arner P, Ashoor H, Astrom G,
Babina M, Bertin N, Burroughs AM, et al. An integrated expression atlas ofmiRNAs and their promoters in human and mouse. Nat Biotechnol. 2017;35(9):872–8.
McMullan GS, Lewis JH. Tuberculosis of the liver, biliary tract, and pancreas.
Microbiol Spectr. 2017;5(1). https://doi.org/10.1128/microbiolspec.TNMI70025-2016.
Stagg HR, Lipman MC, McHugh TD, Jenkins HE. Isoniazid-resistanttuberculosis: a cause for concern? Int J Tuberc Lung Dis. 2017;21(2):129–39.
Hu X, Liao S, Bai H, Wu L, Wang M, Wu Q, Zhou J, Jiao L, Chen X, Zhou Y,
et al. Integrating exosomal microRNAs and electronic health data improved
tuberculosis diagnosis. EBioMedicine. 2019;40:564–73.
Wang Y, Xu YM, Zou YQ, Lin J, Huang B, Liu J, Li J, Zhang J, Yang WM, Min
QH, et al. Identification of differential expressed PE exosomal miRNA in lungadenocarcinoma, tuberculosis, and other benign lesions. Medicine(Baltimore). 2017;96(44):e8361.

Received: 23 November 2020 Accepted: 25 November 2020

Publisher’s Note
References
1. Houben RM, Dodd PJ. The global burden of latent tuberculosis infection: are-estimation using mathematical modelling. PLoS Med. 2016;13(10):e1002152.
2. Cadena AM, Fortune SM, Flynn JL. Heterogeneity in tuberculosis. Nat Rev
Immunol. 2017;17(11):691–702.
3. Diel R, Loddenkemper R, Nienhaus A. Predictive value of interferon-gamma
release assays and tuberculin skin testing for progression from latent TB
infection to disease state: a meta-analysis. Chest. 2012;142(1):63–75.
4. Kasprowicz VO, Churchyard G, Lawn SD, Squire SB, Lalvani A. Diagnosing
latent tuberculosis in high-risk individuals: rising to the challenge in highburden areas. J Infect Dis. 2011;204(Suppl 4):S1168–78.
5. Santin M, Munoz L, Rigau D. Interferon-gamma release assays for the
diagnosis of tuberculosis and tuberculosis infection in HIV-infected adults: asystematic review and meta-analysis. PLoS One. 2012;7(3):e32482.
6. Chkhartishvili N, Kempker RR, Dvali N, Abashidze L, Sharavdze L, Gabunia P,
Blumberg HM, Del Rio C, Tsertsvadze T. Poor agreement between
interferon-gamma release assays and the tuberculin skin test among HIVinfected individuals in the country of Georgia. BMC Infect Dis. 2013;13:513.
7. Auguste P, Tsertsvadze A, Pink J, Court R, McCarthy N, Sutcliffe P, Clarke A.
Comparing interferon-gamma release assays with tuberculin skin test for
identifying latent tuberculosis infection that progresses to active

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