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BMC Genetics

Open Access

Evaluation of host genetics on outcome of tuberculosis infection due to differences in killer immunoglobulin-like receptor gene frequencies and haplotypes

BMC Genetics201516:63

https://doi.org/10.1186/s12863-015-0224-x

Received: 13 February 2015

Accepted: 1 June 2015

Published: 16 June 2015

Abstract

Background

Outcome of Mycobacterium tuberculosis (Mtb) infection is affected by virulence of the infecting strain of Mtb, host environment, co-morbidities, and the genetic composition of the host, specifically the presence or absence of genes involved in immune responses/regulation. It is hypothesized that specific killer immunoglobulin-like receptor (KIR) genes may be associated with Mtb infection and clinical outcome. This cross-sectional study examined the KIR gene frequencies, profiles, and haplotypes of individuals with active tuberculosis, latent tuberculosis infection, compared to TB and HIV negative healthy controls.

Results

Analysis of KIR gene frequencies revealed differences among disease status groups, suggesting that enrichment or depletion of specific KIR genes may direct the disease outcome. Mtb infected individuals were more likely to have a centromeric-AA haplotype compared to controls.

Conclusion

The differences in KIR gene frequencies and haplotypes may result in differential cytokine expression, contributing to different disease outcomes, and suggest a genetic influence on Mtb susceptibility and pathogenesis.

Keywords

Killer immunoglobulin-like receptorKIR profilesKIR haplotypesTuberculosis

Background

Tuberculosis (TB) incidence in the world and in Canada overall are declining toward goals set by the World Health Organization [1], however, certain populations and/or regions within Canada continue to have rates of tuberculosis exceeding the national average. In 2012, the global burden of TB was estimated at 8.6 million incident cases (122/100,000 population) [1]. Canada reported 1686 new active cases of TB (4.8/100,000) in 2012 [2]. The incidence of TB in Manitoba was more than twice the national rate at 138 cases (10.9/100,000).

The immune response to an intracellular pathogen such as Mycobacterium tuberculosis (Mtb) involves natural killer (NK) cells to bridge the innate and adaptive immune response to infection [3]. NK cells are important in early Mtb infection, as they are capable of activating phagocytic cells at the site of infection [4], and are producers of INF-γ, which functions to activate macrophages [5]. The activity of NK cells is controlled by a balance of inhibitory and stimulatory signals generated when human leukocyte antigen (HLA) class I ligands bind to killer immunoglobulin-like receptors (KIRs) on the NK cell surface [6]. This highly specific recognition system is controlled by the integration of signals generated by a multitude of inhibitory and activating KIRs, which inhibit or activate, respectively, cytotoxicity and secretion of cytokines ultimately leading to death of the targeted cell [7]. Both inhibition and activation involve a number of signalling molecules, as previously described [8, 9].

There is extensive genomic diversity in KIR genes in humans. Currently, a database and online repository for immune gene frequencies in worldwide populations reports 517 different KIR genotypes [10, 11]. It is believed that this variation may affect resistance or susceptibility to a number of pathogens through ligand-receptor interactions and the downstream signalling and/or cytokine release that follows [12, 13]. Genetic susceptibility or resistance to infectious diseases, in conjunction with environmental and host risk factors, is thought to determine disease progression [1416].

Present literature indicates that the outcome of Mtb infection is affected not only by virulence of the infecting strain of M. tuberculosis [17], but also by host environment, disease co-morbidities, and the genetic composition of the host, specifically the presence or absence of genes that regulate the immune system [14, 16, 1820]. Following Mtb infection, approximately 10 % of individuals will develop active TB (ATB) during their lifetime, while the majority of individuals will exhibit latent TB infection (LTBI) [21, 22]. LTBI refers to the condition in which Mtb remains viable in the macrophage but retains a small amount of metabolic activity [23]. It is not currently known which genes and/or immune components regulate an individual’s disease outcome following exposure (ATB, LTBI, or exposed uninfected). Present literature captures only those studies focusing on genetic profiles among active tuberculosis vs. uninfected individuals. In the majority of these studies, the control group contains both individuals with LTBI infection as identified by a positive Tuberculin skin test (TST), and those with uninfected status.

The novel aspect of this study is to identify unique profiles among the LTBI population, diagnosed using the Interferon gamma release assay (IGRA)' as there is identify twice in this sentence. Differences in KIR profiles and haplotypes may be associated with Mtb infection status [2426] and play a role in altered TB disease progression and disease outcomes. In this cross-sectional study, we examined the enrichment or depletion of KIR genes in individuals from Manitoba with ATB infection, LTBI and controls, and further explored the association between Mtb infection status and KIR profiles and haplotypes.

Methods

Sample Populations

The 209 samples consisted of whole blood from individuals living in Manitoba. The sampling was performed at hospital and community TB clinics in Winnipeg, Manitoba, Canada between November 3, 2009 and March 29, 2011 and was cross-sectional in nature. The study was approved by the Health Research Ethics Board at the University of Manitoba (H2008:301). All study participants provided written informed consent following consultation with a study nurse. ATB infection (n = 59) was confirmed by mycobacterial culture. LTBI (n = 46) was identified using the interferon-gamma release assay (IGRA) (QuantiFERON®-TB-Gold, Qiagen). Healthy IGRA negative HIV negative individuals (n = 104) were used as controls and consisted largely of individuals participating in routine occupational health screening, and immigration screening. All individuals within the specified time period who consented to genetic testing were included in this study. Exclusion criteria included those individuals with HIV co-infection, and anyone who exhibited an indeterminate IGRA response. Participant demographics for ethnicity, age, and gender can be seen in Table 1.
Table 1

Sample demographics

Parameter

Value

Number

Percent

Gender

Male

91

43.5

 

Female

118

56.5

Age

≤19

0

0

 

20 - 39

88

42.1

 

40 - 59

105

50.2

 

≥60

16

7.7

Disease status

Control

104

49.8

 

LTBI

46

22

 

ATB

59

28.2

Ethnicity

Canadian-born

131

62.7

 

  Control

72

54.9

 

  LTBI

12

9.2

 

  ATB

47

35.9

 

Foreign-born

78

37.3

 

  Control

32

41.0

 

  LTBI

34

43.6

 

  ATB

12

15.4

LTBI Latent tuberculosis infection; ATB Active tuberculosis

DNA extraction and replication

Genomic DNA was extracted using Qiagen DNA Mini Kit as per manufacturer’s instructions (Qiagen, Louisville, KY). The samples were subjected to whole genome replication using the Qiagen Repli-G mini kit as per manufacturer’s instructions to increase DNA concentration of the testing sample.

KIR genotyping

The concentration of DNA was normalized to 100 μg/mL at 260 nm using the SmartSpec Plus spectrophotometer (Bio-Rad, Mississauga, ON). KIR genotyping was performed by sequence-specific primer polymerase chain reaction using the Miltenyi Biotec KIR Typing Kit (Auburn, CA) as previously described [9]. The amplicons were visualized with UV light (Bio-Rad Gel Doc EZ Imager, Mississauga, ON) following gel electrophoresis at 13 V/cm on a 2 % agarose gel containing ethidium bromide. The KIR typing kit allows for detection of all known human KIR genes and alleles [27, 28]. KIR2DL5A and KIR2DL5B are collectively referred to as KIR2DL5 for this paper.

Statistical analysis

Data for each individual was entered into BioNumerics software version 5.0 (Applied Maths, Belgium) as binary character data. All KIR genes were combined into a single KIR profile for each individual and clustered to identify prevalent profiles among specified groups using the categorical co-efficient and unweighted pair group method with arithmetic mean (UPGMA) [29]. KIR gene frequencies were tabulated by direct counts from the clustered profiles to determine frequency within a defined group. Differences between Mtb infection status groups were estimated using the two-tailed Fisher’s exact test (GraphPad Software, La Jolla, CA). A P-value ≤0.05 was considered statistically significant. Haplotype designation was determined as previously described [9].

Results

KIR gene frequencies

In order to determine the differences in KIR gene frequencies between different disease status groups (ATB, LTBI, and controls), the KIR gene frequency data obtained was analyzed and compared. All 209 samples consistently contained the framework genes KIR2DL4, KIR3DL2, KIR3DL3, and the pseudogenes KIR2DP1 and KIR3DP1.

Five KIR genes (KIR2DL2, KIR2DL5, KIR2DL5B, KIR2DS2, and KIR2DS3) differed significantly (P ≤ 0.05) in frequency between disease status groups (Table 2). Two genes differed between individuals with Mtb infection (LTBI and ATB) vs. controls, KIR2DL2 (33.33 % vs. 55.77 %, P = 0.0014) and KIR2DS2 (34.29 % vs. 54.81 %, P = 0.0035). However, the underlying differences can be exposed when analyzing LTBI and ATB separately. KIR2DL5 and KIR2DL5B (both 73.91 % vs. 51.92 %, P = 0.0125) were present in higher frequency in individuals within the LTBI group as compared to controls. KIR2DL2 (27.12 % vs. 55.77 %, P = 0.0005), KIR2DS2 (27.12 % vs. 54.82 %, P = 0.0010), and KIR2DS3 (8.47 % vs. 30.77 %, P = 0.0009) were present in a lower frequency in individuals with ATB compared to controls. Lastly, gene frequencies of KIR2DL5 (73.91 % vs. 49.15 %, P = 0.0156), KIR2DL5B (73.91 % vs. 49.15 %, P = 0.0156), and KIR2DS3 (39.13 % vs. 8.47 %, P = 0.0002) differed significantly between latently and actively infected individuals, respectively.
Table 2

Killer immunoglobulin-like receptor (KIR) gene frequencies by tuberculosis status

 

KIR; n (% f)

 

2DL1

2DL2

2DL3

2DL4

2DL5all

2DL5A

2DL5B

2DS1

2DS2

2DS3

1D

2DS4

2DS5

3DL1

3DL2

3DL3

3DS1

2DP1

3DP1

Mtb Infected

                   

All (n = 105)

103 (98.10)

35 (33.33)

100 (95.24)

105 (100.00)

63 (60.00)

48 (45.71)

63 (60.00)

50 (47.62)

36 (34.29)

23 (21.90)

73 (69.52)

105 (100.00)

41 (39.05)

99 (94.29)

105 (100.00)

105 (100.00)

55 (52.38)

105 (100.00)

105 (100.00)

LTBI (n = 46)

45 (97.82)

19 (41.30)

42 (91.30)

46 (100.00)

34 (73.91)

24 (52.17)

34 (73.91)

25 (54.35)

20 (43.48)

18 (39.13)

36 (78.26)

46 (100.00)

16 (34.78)

45 (97.82)

46 (100.00)

46 (100.00)

26 (56.52)

46 (100.00)

46 (100.00)

ATB (n = 59)

58 (98.31)

16 (27.12)

58 (98.31)

59 (100.00)

29 (49.15)

24 (40.68)

29 (49.15)

25 (42.37)

16 (27.12)

5 (8.47)

37 (62.71)

59 (100.00)

25 (42.37)

54 (91.53)

59 (100.00)

59 (100.00)

29 (49.15)

59 (100.00)

59 (100.00)

Control (n = 104)

102 (98.08)

58 (55.77)

95 (91.35)

104 (100.00)

54 (51.92)

43 (41.35)

54 (51.92)

44 (42.31)

57 (54.81)

32 (30.77)

79 (75.96)

103 (99.04)

32 (30.77)

97 (93.27)

104 (100.00)

104 (100.00)

43 (41.35)

104 (100.00)

104 (100.00)

P-value

                   

Mtb Infected vs. Control

1.0000

0.0014

0.2837

1.0000

0.2664

0.5776

0.2664

0.4879

0.0035

0.1599

0.3519

0.4976

0.2463

0.7832

1.0000

1.0000

0.1279

1.0000

1.0000

LTBI vs. Control

1.0000

0.1137

1.0000

1.0000

0.0125

0.2853

0.0125

0.2141

0.2190

0.3505

0.8363

1.0000

0.7050

0.4357

1.0000

1.0000

0.1099

1.0000

1.0000

ATB vs. Control

1.0000

0.0005

0.0957

1.0000

0.7472

1.0000

0.7472

1.0000

0.0010

0.0009

0.1047

1.0000

0.1716

0.7583

1.0000

1.0000

0.4121

1.0000

1.0000

ATB vs. LTBI

1.0000

0.1474

1.0000

1.0000

0.0156

0.3237

0.0156

0.2432

0.0988

0.0002

0.0933

1.0000

0.5457

0.2272

1.0000

1.0000

0.5552

1.0000

1.0000

Significant P-values (≤0.05) are bolded; Mtb Mycobacterium tuberculosis, LTBI Latent tuberculosis infection, ATB Active tuberculosis

KIR gene profiles

Forty-three KIR profiles (genotypes) were identified in this study (Fig. 1). These profiles ranged in their frequency of distribution from as high as 24.88 % (52/209) to as low as 0.48 % (1/209). Twenty-two of the 43 profiles identified were unique to a single individual. The most prominent genotypes were #7, (18/209, 8.2 %), #8 (52/209, 24.9 %), #12 (11/209, 5.3 %), #18 (12/209, 5.7 %), and #36 (25/209, 12.0 %). Eight KIR genotypes were shared between all three disease status groups (ATB, LTBI, controls; genotypes # 7, 8, 12, 17, 18, 30, 36, 39) (Fig. 2). Excluding those shared with individuals from the control group, two genotypes were shared between the LTBI and ATB groups (genotypes #11, 22). Nineteen genotypes were exclusive to the control group. Five genotypes were exclusive to those individuals with ATB (genotypes #1, 4 9, 23, 37), and represented 6/59 (10.2 %) active cases. Three genotypes were exclusive to those individuals with LTBI (genotypes # 13, 21, 28), and represented 3/46 (6.5 %) latent cases.
Fig. 1

Frequency of KIR genotypes in study group population. Forty-three distinct KIR types were seen in these 209 individuals that differ from each other by the presence of (shaded box) or absence (white box) of 19 KIR genes (KIR2DL5 broken down into 2DL5A, 2DL5B, and 2DL5 (both A and B); KIR2DS4 broken down into 1D and full length 2DS4). Frequency (%F) of each genotype is expressed as a percentage and is defined as the number of individuals having the genotype (N+) divided by the number of individuals (n) in the tuberculosis status group

Fig. 2

Distribution of KIR genotypes among active tuberculosis (ATB), latent tuberculosis infection (LTBI), and controls. Content of each genotype can be seen in Fig. 1

Those individuals with Mtb infection (LTBI or ATB) were found within 25 of the 43 genotypes, most predominantly in #8 (25/105, 23.8 %) and #36 (20/105, 19.0 %). Over 42 % of Mtb infected individuals were found in these two genotypes.

Haplotype analysis

In addition to gene frequency variation, there is haplotypic variation due to the different number and kinds of KIR genes [30]. Both LTBI (34.8 %, P = 0.0004) and ATB (32.3 %, P = 0.0005) infected individuals were significantly more likely to have AA-AB haplotypes than controls (9.6 %; Table 3). Additionally, those individuals with ATB were less likely to have an AB-AB haplotype compared to controls (8.5 % vs. 21.2 %, P = 0.0476). The majority of the Mtb infected cases are represented in the AA-AA (LTBI – 21.7 %, ATB – 35.6 %) and AA-AB (LTBI – 34.8 %, ATB – 32.3 %) haplotypes. Overall, 66.67 % of Mtb infected individuals had a centromeric-AA haplotype (LTBI – 58.7 %, ATB – 72.9 %), compared with only 44.2 % of controls (P = 0.0014).
Table 3

Frequency of centromeric and telomeric haplotypes by tuberculosis status

Haplotype

Tuberculosis Status; n (%F)

  

Latent

Active

Control

Centromeric

Telomeric

(n = 46)

(n = 59)

(n = 104)

AA

AA

10 (21.7)

21 (35.6)

33 (31.73)

AA

AB

16 (34.8)

19 (32.3)

10 (9.62)a,b

AB

AA

6 (13.0)

8 (13.6)

24 (23.08)

AB

AB

9 (19.6)

5 (8.5)

22 (21.15)b

AA

BB

1 (2.2)

3 (5.1)

3 (2.88)

AB

BB

 

2 (3.4)

3 (2.88)

BB

AA

4 (8.7)

1 (1.7)

4 (3.85)

BB

AB

  

4 (3.85)

BB

BB

  

1 (0.96)

aSignificant compared latent TB; bSignificant compared to active TB; p-value ≤ 0.05 considered significant

Discussion

This study was designed to investigate the KIR gene frequencies in individuals from Manitoba with ATB, LTBI, and a control group, as described in methods. Additionally, KIR profiles and haplotypes were analyzed. KIR genes may influence disease outcome (latent vs. active) which is controlled in part by an organized immune response.

When determining KIR gene frequencies, framework genes and pseudogenes were present in 100 % of the samples, as expected [31]. KIR2DL2, KIR2DL5, KIR2DL5B, KIR2DS2, and KIR2DS3 differed significantly between Mtb status groups.

Mahfouz et al. and Mendez et al. both found KIR2DL3 to be the only statistically significant KIR gene frequency to differ between ATB patients and controls (higher in ATB patients; P = 0.03 and P = 0.02, respectively) [24, 25]. In our study, KIR2DL3 occurred in only a slightly higher frequency of individuals with ATB (98.31 %) compared to controls (91.35 %, P = 0.0957; NS). Pydi et al. found gene frequencies of KIR2DS1, KIR2DS5, KIR3DL1, and KIR2DL3 to be higher in ATB patients compared to controls (P < 0.0001) [32]. Although KIR2DS5 and KIR2DL3 gene frequencies differed slightly between ATB and control groups in our study, none of the observed differences were statistically significant. Lastly, Lu et al. found gene frequencies of KIR2DS1, KIR2DS3, and KIR3DS1 to be significantly higher in ATB patients compared to controls (P < 0.05) [33]. In our study, frequencies of KIR2DS1 and KIR3DS1 were not statistically different between those with ATB compared to controls. However, when looking at KIR2DS3, our study showed a decreased frequency in individuals with ATB compared controls (8.47 % vs. 30.77 %, P = 0.0009). Observed differences in KIR frequencies between TB status groups may be due in large part to ethnicity, as in each of the above mentioned studies, a different country of origin was involved, and KIR are known to differ among ethnic groups [11]. Given that the Manitoba population is very heterogeneous consisting of many foreign born immigrant individuals, as well as indigenous and Canadian born populations, future studies matching TB status groups by ethnicity will help to more clearly define the role of KIR genes in TB pathogenicity. Additionally, group definitions may have played a role; the control groups in the above published studies contained either TST positive individuals or had no data on TB reactivity. These control group then reflect a mixture of both uninfected and LTBI infected participants. In contrast to this, with the use of the IGRA testing, we were able to clearly distinguish our LTBI and control groups.

With the exception of KIR2DL2, our study found an increased presence of inhibitory KIR (KIR2DL5, KIR2DL5B) in LTBI individuals and a decreased presence of activating KIR (KIR2DS2, KIR2DS3) in ATB infected individuals. This may suggest that the enrichment or depletion of specific KIR genes predisposes an individual to progressing to ATB disease by means of an inadequate cytotoxic response to the pathogen. LTBI refers to the condition in which Mtb remains viable in the macrophage but only retains a small amount of metabolic activity [23]. The inability of the immune system to maintain the infection in a latent state results in ATB infection.

Forty-three different gene profiles were identified in the 209 samples, of which, 25 profiles contained Mtb cases. The profiles containing the most Mtb cases were also prevalent in the control group, suggesting an unlikely correlation between profile/genotype and TB status. Many profiles were unique to individuals with Mtb, however there is little to be concluded from those profiles containing only a few individuals. Extrapolation of these findings via continued sampling is warranted to determine the importance of KIR profiles.

Those individuals with LTBI and ATB were more likely to have an AA-AB haplotype than controls. This was the haplotype that contained the most Mtb cases (35/105, 33.3 %). Two-thirds (66.7 %) of individuals with TB had a centromeric-AA halpotype, compared to only 44.2 % of controls (P = 0.0014). A centromeric-AA haplotype represents the haplotype with the fewest number of activating genes. It is hypothesized that this lack of activating genes may prevent the appropriate release of M. tuberculosis killing cytokines [8].

A limitation to this study is the lack of longitudinal data among our LTBI status group. As we do not know when an individual became LTBI (IGRA positive) it is possible that some of these individuals went on to develop primary or secondary TB, however we do not have access to this data. It is known that these were healthy individuals with low risk for the development of ATB. Another limitation is the unknown TB exposure of our control group (IGRA negative), we can make no conclusions in regards to the role KIR plays in TB susceptibility. This group is used as a reference comparison group for our LTBI and ATB populations.

Conclusions

In summary, major differences can be seen in KIR gene frequencies across Mtb disease status groups. KIR haplotype frequencies differ between these groups as well. The differences in KIR gene frequencies and/or haplotypes may result in differential cytokine expression, contributing to different disease outcomes, and suggest a genetic influence on Mtb susceptibility and pathogenesis. The skewed distribution of A-containing centromeric haplotypes (containing fewer activating genes), along with the increased presence of TB disease in these haplotypes, suggests a correlation. Further investigation is needed to characterize the subtleties of these differences by way of sequencing of specific KIR genes and/or KIR-HLA association studies taking into account different ethnic populations.

Availability of supporting data

The data set supporting the results of this article is included within the article.

Abbreviations

Mtb: 

Mycobacterium tuberculosis

KIR: 

Killer immunoglobulin-like receptor

TB: 

Tuberculosis

NK: 

Natural killer

HLA: 

Human leukocyte antigen

ATB: 

Active tuberculosis

LTBI: 

Latent tuberculosis infection

TST: 

Tuberculin skin test

UPGMA: 

Unweighted pair group method with arithmetic mean

Declarations

Acknowledgements

The authors wish to acknowledge the study nurse Ms. Carmen Lopez and research technician Ms. Christine Mesa. The authors would also like to thank Drs. T. Blake Ball, Linda Larcombe, and Pamela Orr for research support.

Authors’ Affiliations

(1)
Department of Medical Microbiology, University of Manitoba
(2)
National Reference Centre for Mycobacteriology, Public Health Agency of Canada

References

  1. World Health Organization. Global Tuberculosis Report 2013. Geneva, Switzerland: WHO Press; 2013.Google Scholar
  2. Tuberculosis in Canada 2012 - Pre-Release – Public Health Agency of Canada [http://www.phac-aspc.gc.ca/tbpc-latb/pubs/tbcan12pre/tab-eng.php#tab1]
  3. Martin MP, Carrington M. KIR locus polymorphisms: genotyping and disease association analysis. Methods Mol Biol. 2008;415:49–64.PubMedGoogle Scholar
  4. Raja A. Immunology of tuberculosis. Indian J Med Res. 2004;120:213–32.PubMedGoogle Scholar
  5. Flynn JL, Chan J. Immunology of tuberculosis. Annu Rev Immunol. 2001;19:93–129.PubMedView ArticleGoogle Scholar
  6. Vilches C, Gardiner CM, Parham P. Gene structure and promoter variation of expressed and nonexpressed variants of the KIR2DL5 gene. J Immunol. 2000;165:6416–21.PubMedView ArticleGoogle Scholar
  7. Rajalingam R. Overview of the killer cell immunoglobulin-like receptor system. Methods Mol Biol. 2012;882:391–414.PubMedGoogle Scholar
  8. Kelley J, Walter L, Trowsdale J. Comparative genomics of natural killer cell receptor gene clusters. PLoS Genet. 2005;1:129–39.PubMedView ArticleGoogle Scholar
  9. Braun K, Larcombe L, Orr P, Nickerson P, Wolfe J, Sharma M. Killer Immunoglobulin-Like Receptor (KIR) Centromeric-AA Haplotype Is Associated with Ethnicity and Tuberculosis Disease in a Canadian First Nations Cohort. PLoS ONE. 2013;8:e67842.PubMed CentralPubMedView ArticleGoogle Scholar
  10. The Allele Frequency Net Database [KIR Genotype Reference] [http://allelefrequencies.net/kir6001a.asp]
  11. Gonzalez-Galarza FF, Christmas S, Middleton D, Jones AR. Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations. Nucleic Acids Res. 2011;39(Database issue):D913–9.PubMed CentralPubMedView ArticleGoogle Scholar
  12. Bashirova AA, Martin MP, McVicar DW, Carrington M. The killer immunoglobulin-like receptor gene cluster: tuning the genome for defense. Annu Rev Genomics Hum Genet. 2006;7:277–300.PubMedView ArticleGoogle Scholar
  13. Carrington M, Norman P. The KIR Gene Cluster. Bethesda, MD: National Centre for Biotechnology Information; 2003.Google Scholar
  14. Delgado JC, Baena A, Thim S, Goldfeld AE. Ethnic‐Specific Genetic Associations with Pulmonary Tuberculosis. J Infect Dis. 2002;186:1463–8.PubMedView ArticleGoogle Scholar
  15. Middleton D, Gonzelez F. The extensive polymorphism of KIR genes. Immunology. 2010;129:8–19.PubMed CentralPubMedView ArticleGoogle Scholar
  16. Morris GAJ, Edwards DRV, Hill PC, Wejse C, Bisseye C, Olesen R, et al. Interleukin 12B (IL12B) genetic variation and pulmonary tuberculosis: a study of cohorts from The Gambia, Guinea-Bissau, United States and Argentina. PLoS ONE. 2011;6:e16656.PubMed CentralPubMedView ArticleGoogle Scholar
  17. Petrelli D, Kaushal Sharma M, Wolfe J, Al-Azem A, Hershfield E, Kabani A. Strain-related virulence of the dominant Mycobacterium tuberculosis strain in the Canadian province of Manitoba. Tuberculosis (Edinb). 2004;84:317–26.View ArticleGoogle Scholar
  18. Awomoyi AA, Marchant A, Howson JMM, McAdam KPWJ, Blackwell JM, Newport MJ. Interleukin-10, polymorphism in SLC11A1 (formerly NRAMP1), and susceptibility to tuberculosis. J Infect Dis. 2002;186:1808–14.PubMedView ArticleGoogle Scholar
  19. Lykouras D, Sampsonas F, Kaparianos A, Karkoulias K, Tsoukalas G, Spiropoulos K. Human genes in TB infection: their role in immune response. Monaldi Arch Chest Dis. 2008;69:24–31.PubMedGoogle Scholar
  20. Uciechowski P, Imhoff H, Lange C, Meyer CG, Browne EN, Kirsten DK, et al. Susceptibility to tuberculosis is associated with TLR1 polymorphisms resulting in a lack of TLR1 cell surface expression. J Leukoc Biol. 2011;90:377–88.PubMedView ArticleGoogle Scholar
  21. Public Health Agency of Canada. Canadian Lung Assocation/Canadian Thoracic Society: Canadian Tuberculosis Standards, 6th edition. Canada: Minister of Health; 2007.Google Scholar
  22. Versalovic J: Manual of Clinical Microbiology. 10th edition. Washington, DC: ASM Press; 2011.Google Scholar
  23. Vernon A. Treatment of latent tuberculosis infection. Semin Respir Crit Care Med. 2013;34:67–86.PubMedView ArticleGoogle Scholar
  24. Mahfouz R, Halas H, Hoteit R, Saadeh M, Shamseddeen W, Charafeddine K, et al. Study of KIR genes in Lebanese patients with tuberculosis. Int J Tuberc Lung Dis. 2011;15:1688–91.PubMedView ArticleGoogle Scholar
  25. Méndez A, Granda H, Meenagh A, Contreras S, Zavaleta R, Mendoza MF, et al. Study of KIR genes in tuberculosis patients. Tissue Antigens. 2006;68:386–9.PubMedView ArticleGoogle Scholar
  26. Shahsavar F, Mousavi T, Azargon A, Entezami K. Association of KIR3DS1 + HLA-B Bw4Ile80 Combination with Susceptibility to Tuberculosis in Lur Population of Iran. Iran J Immunol. 2012;9:39–47.PubMedGoogle Scholar
  27. KIRtyping_23June08.indd - IM0001493.ashx.Google Scholar
  28. KIR Typing Kits - Miltenyi Biotec [https://www.miltenyibiotec.com/en/products-and-services/macsmolecular/nucleic-acid-research/dna-research/kir-typing-kits.aspx]
  29. Clifford HT: An Introduction to Numerical Classification. New York, NY: Academic Press; 1975.Google Scholar
  30. Robinson J, Mistry K, McWilliam H, Lopez R, Marsh SGE. IPD--the Immuno Polymorphism Database. Nucleic Acids Res. 2010;38(Database):D863–9.PubMed CentralPubMedView ArticleGoogle Scholar
  31. Hsu KC, Liu X-R, Selvakumar A, Mickelson E, O’Reilly RJ, Dupont B. Killer Ig-like receptor haplotype analysis by gene content: evidence for genomic diversity with a minimum of six basic framework haplotypes, each with multiple subsets. J Immunol. 2002;169:5118–29.PubMedView ArticleGoogle Scholar
  32. Pydi SS, Sunder SR, Venkatasubramanian S, Kovvali S, Jonnalagada S, Valluri VL: Killer Cell Immunoglobulin like Receptor Gene Association with Tuberculosis. Hum Immunol 2012;74:85–92.Google Scholar
  33. Lu C, Bai XL, Shen YJ, Deng YF, Wang CY, Fan G, et al. Potential implication of activating killer cell immunoglobulin-like receptor and HLA in onset of pulmonary tuberculosis. Scand J Immunol. 2012;76:491–6.PubMedView ArticleGoogle Scholar

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© Braun et al. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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