Phenotypic characterization of Peripheral B cells in Mycobacterium tuberculosis infection and disease in Addis Ababa, Ethiopia

Abstract

Background:

Mortality and morbidity from tuberculosis (TB) remain one of the most important public health issues. Although cell-mediated immunity is the main immune response against Mycobacterium tuberculosis (MTB), the role of B-cells during MTB infection and disease is unclear.

Methods:

Peripheral blood mononuclear cells (PBMC) were isolated from treatment naïve Pulmonary TB patients (TB, n = 16), latent TB-infected participants (LTBI, n = 17), and healthy controls (HC, n = 19). PBMCs were stained with various fluorescently labeled antibodies to define B-cell subsets using multicolor flow cytometry.

Results:

Atypical memory B cells (CD19+CD27−CD21−) and circulating marginal zone B-cells (CD19+CD27+CD21+IgM+IgD+CD23−) were significantly higher in active TB when compared to LTBI and HC. CD5+ regulatory B cells (Breg, CD19+CD24hiCD38hiCD5+) and resting B-cells (CD19+CD27+CD21+) in Active TB patients were significantly lower compared to HC and LTBI. Overall, there were no differences in B cell percentages (CD19+), naïve B cells (CD19+CD27−CD21+), Breg (CD19+CD24hiCD38hi), and activated memory B cells (CD19+CD27+CD21−) among the three study groups.

Conclusions:

These results indicated that multiple subsets of B cells were associated with TB infection and disease. It will be useful to examine these cell populations for their potential use as biomarkers for TB disease and LTBI.

1. Introduction

The communicable disease, tuberculosis (TB) is among the leading causes of death and disease in the world. Globally, approximately 10 million people developed TB in 2020. Africa accounted for 25% of all TB cases in WHO regions. According to WHO 2021 report, Ethiopia is among the 30 high-burden countries listed for TB and TB/HIV [1].

Infection with Mycobacterium tuberculosis in a human host can take one of three forms: latent infection, reactivation, or primary active infection [2]. For maximum protection against infection and disease progression, a better knowledge of the interactions and complementing effects of the different arms of the immune system against tuberculosis, as well as the way M. tuberculosis manipulates these responses, is critical [3–5].

Researchers studied B cells in tuberculosis, such as (CD19+) B cells, plasma cells (CD138+), regulatory B cells (Breg) (CD1d+CD5+, CD25+CD71+, CD24hiCD27+, and CD24hiCD38hi), circulating marginal zone (CMZ) (CD19+IgM+CD23−CD27+), and marker of memory based on the combined expression patterns of CD21 and CD27, or the combined expression of IgD and CD27 [6–8]. B cells can conceivably shape anti-tuberculosis immunity through a variety of means including direct effects of antibodies upon the pathogen, cytokine production, as well as influencing the intracellular killing mechanisms of leukocytes [5]. However, protection against intracellular pathogens is often generalized as exclusively T cell-mediated, with B cells and antibodies playing a limited role than they are perceived to play during extracellular infections [4,5,8]; therefore, most studies done on TB focused on T cells. Despite this, recent studies indicate that B cells are crucial in the fight against M. tuberculosis [3–5,9] although some studies reported conflicting results on the role of B cells in TB [6,8,10,11].

Given the paucity of studies on B cells in human TB in general and the lack of any such studies in Ethiopia, the goal of the present investigation was to assess frequencies of B cells and their many subsets using flow cytometric approaches in TB patients in Ethiopia.

2. Methods

2.1. Study population

A cross-sectional study was conducted from February 2018 to April 2018. Study participants were recruited from five public health centers, in Addis Ababa, Ethiopia. Newly diagnosed smear positive and rifampicin-sensitive pulmonary TB patients were recruited before the initiation of chemotherapy. As controls, apparently healthy participants were recruited from Voluntary Counseling and Testing (VCT) clinics of the same study sites as the TB patients. Individuals with LTBI and HC were classified based on QuantiFERON-TB Gold assay results. All participants were between the age of 18 and 75 and HIV-negative. Individuals treated with immunosuppressive drugs and those with chronic diseases such as diabetes were excluded from the study. Blood from all participants and sputum from TB patients was collected at the health center and transported to the Armauer Hansen Research Institute (AHRI) for laboratory investigations.

2.2. QuantiFERON TB Gold Plus assay

The assay was performed according to the manufacturer’s instructions using the QFT-TB Gold Plus blood collection tubes composed of Nil, TB 1 antigen, TB 2 antigen, and Mitogen tubes and the QFT-TB Gold plus ELISA kit (Cellestis Limited, Carnegie, Victoria, Australia). Absorbance was read using a microplate reader with a 450 nm filter and with a 620 nm to 650 nm reference filter. The data was analyzed using the QFT-TB Gold plus Analysis Software [12].

2.3. PBMC isolation

Peripheral blood mononuclear cells (PBMC) were isolated from blood collected in heparinized tubes by density gradient centrifugation using Ficoll-Paque^™PLUS solution (GE Healthcare). Briefly, whole blood was layered onto Leucosep tubes (BD) containing 15 ml of Ficoll and centrifuged for 20 min at 1700 RPM. The fluid above the separation membrane of the Leucosep tube which contains the PBMC was transferred into a new tube and first washed with phosphate-buffered saline (PBS) and then washed with FACS buffer (PBS, Heat-inactivated FBS (Fatal bovine serum) and Na2 EDTA) for 10 min at 1700 RPM.

2.4. Flow cytometry

Surface staining was done by incubating fresh PBMC with a cocktail of antibodies containing 5 μl CD19 PerCP (Clone 4G7, BD, Catalog No. 345778), 3 μl CD21 PE-CY7 (Clone B-ly4, BD, Catalog No. 561374), 20 μl CD27 FITC (Clone Ll28, BD, Catalog No. 340424), 3 μl CD23 BV421 (Clone M-L233, BD, Catalog No. 562707), 10 μl sIgD PE(Clone IA6–2, BD, Catalog No. 555779), 10 μl sIgM APC (Clone G20–127, BD, Catalog No. 551062) in panel one and 5 μl CD19 PerCP (Clone 4G7, BD, Catalog No. 345778), 3 μl CD21 PE-CY7 (Clone B-ly4, BD, Catalog No. 561374), 20 μl CD27 FITC (Clone Ll28, BD, Catalog No. 340424), 10 μl CD 5 PE(Clone Ll7F12, BD, Catalog No. 345782), 3 μl CD38 APC (Clone HB7, BD, Catalog No. 345807) and 3 μl CD24 BV421 (Clone ML5, BD, Catalog No. 562789) in panel two. Incubations were done for 20 min at room temperature in the dark. Then cells were washed and re-suspended in FACS (Fluorescence Activated Cell Sorting) buffer. Data were acquired on a BD FACSCanto II with Diva software and analyzed using FlowJo software (Version 9.6.2).

2.5. Gating strategy

The lymphocyte population was gated based on the forward scatter (FSC) and side scatter (SSC) position, followed by a singlets gate based on FSC height and area, then total B-cells were selected based on CD19 positivity. Memory B cell subsets were identified using CD27 and CD21 within a total B-cells gate. Switched and non-switched B cells were defined based on lack (IgM−IgD−) or presence (IgM+IgD+) of surface immunoglobulin, respectively. Marginal zone B cells were defined as non-switched CD27+CD19+CD21+CD23− B cells. Breg is classified by the combined expression of CD24 and CD38 with or without CD5. We used FMO (Fluorescence minus one) and unstained control to gate the target population.

2.6. Data quality assurance

Data quality was addressed by following standard operating procedures carefully for all laboratory tests. Reagents and PBMC were optimized for flow cytometry. The quality of the LJ medium was assured by sterility checking and inoculating of known M. tuberculosis isolate.

2.7. Data analysis and interpretation

Data was analyzed by JMP software. Differences in frequencies of B cell subsets across all groups were assessed using the non-parametric Kruskal-Wallis test, and comparisons of two groups were evaluated with the non-parametric Wilcoxon rank sum test. Data is presented as median (Interquartile range) and a P value of less than 0.05% was taken as a statistically significant difference.

2.8. Ethical consideration

Ethical approval was obtained from the Department of Medical Laboratory Sciences of Addis Ababa University, Addis Ababa City Administration Health Bureau, and Armauer Hansen Research Institute (AHRI)/All Africa Leprosy, Tuberculosis and Rehabilitation Training Center (ALERT) ethics review committee. All participants gave written informed consent before data and specimen collection.

3. Results

3.1. Characteristics of study participants

We recruited smear-positive Tuberculosis patients before the initiation of anti-TB treatment and performed additional tests at AHRI, including a culture of sputum and the SD BIOLINE TB Ag MPT64 rapid test. The TB cases (N = 16) all had positive culture results and identified as M. tuberculosis complex by SD BIOLINE TB Ag MPT64 immunochromatographic assay. The LTBI group (N = 17) had positive interferon-gamma release assay (IGRA) using QuantiFERON (QFN) blood tests, whereas the healthy control group (N = 19) had negative results. There were more males than females in each group. The median age was 26, 29, and 26 years for TB, LTBI, and healthy controls (HC) cases, respectively. Body Mass Index (BMI) was significantly lower in active TB than in LTBI and HCs (p = 0.0007) (Table 1).

Table 1.

Characterization of study participants, in selected health centers, Addis Ababa, Ethiopia, 2018.

Variables	TB (n = 16)	LTBI (n = 17)	HC (n = 19)	P-value
Sex				0.8727
Male	10	10	12
Female	6	7	7
Age (Years)
Median (IQR)	26(21–35)	29(26–33)	26(22–28)	0.2344
BMI (kg/m2)
Median (IQR)	18.75(17.1–20.5)	21.7(20–25.35)	22(20–24)	0.0007

TB, Tuberculosis; HC, Healthy control; LTBI, Latent tuberculosis infections; IQR, Interquartile Range; BMI, Body mass index.

3.2. Analysis of total B-cells frequencies during M.tuberculosis infection and disease

Total B-cells were defined as CD19+ cells within a lymphocyte population (Figs. 1 and 2). Hence, we assessed the differences in the total percentages of B-cells (CD19+) in PBMCs among active TB, LTBI, and HC. Differences in proportions of total B-cells (CD19+) were not statistically significant among the three groups (p = 0.2407) although frequencies of B-cells were lower in TB patients compared to the HC group (Table 2).

Fig. 1.

Typical example of gating strategy for memory B cells subsets (CD27+CD21−, CD27−CD21−, CD27+CD21+ and CD27−CD21+): (A) lymphocyte gate using forward scatter (FSC) and Side Scatter (SSC), (B) Gate on singlets based on FSC-height and area, (C) B-cells are identified using CD19, (D) Memory B cell subsets are identified using CD27 and CD21 within a total B-cell gate, (E) Class switched and non-class switched memory B cell gate on resting memory B-cell subsets, (F) Circulating Marginal zone B cells gate on non-class switched memory B cell.

Fig. 2.

Typical example of gating strategy for regulatory B cell:(A) Lymphocyte gate using FSC and SSC, (B) Gate on singlet based on FSC-height and area, (C) B-cells are identified using CD19, (D) Regulatory B cell subsets are identified using CD24 and CD38 within a total B cell gate, (E) Alternative subset of regulatory B cells gate on CD24 and CD 38(CD19+CD24hiCD38hiCD5+).

Table 2.

Percentage of B cells subsets [Median (IQR)] in the study participants, Addis Ababa, Ethiopia, 2018.

B cells subsets	HCs	LTBI	TB	Kruskal-wallis test P-value	P-values of Wilcoxon rank sum test
					HCs vs. LTBI	HCs vs. TB	LTBI vs. TB
Total B cells %	3(1.6–5.3)	1.9(1.0–3.3)	1.5(0.8–5.2)	0.2407	0.0962	0.0913	0.6656
Naïve B cells %	70.7(64–78)	64.7(56.6–71.9)	60.9(45–74.7)	0.1156	0.1095	0.0661	0.5167
aMBCs %	11.5 (6.4–16.2)	10.5(8.5–15.3)	20.8(13.6–34.3)	0.0024	0.7393	0.0036	0.0017
AMBCs %	5.3 (3.8–7.3)	6.9(4.8–11.3)	9.9(4.1–16.7)	0.1134	0.1406	0.0591	0.428
RMBCs %	10.9 (8.3–14)	15(11.2–19.5)	3.2(2.2–5.3)	<.0001	0.0573	<0.001	<0.0001
CSMBCs %	62.7 (54.9–68.7)	71.4(65.6–76.3)	70.4(66.3–84.4)	0.0065	0.0041	0.0118	0.4937
NCSMBCs %	27.6 (21.4–35)	19.5(14.1–23.2)	12.9(6.1–25.6)	0.0011	0.0027	<0.0021	0.1213
CMZ %	75.9(63.6–87.1)	84.6(70.55–88.8)	94.1(87.5–96.95)	0.0015	0.2165	0.0012	0.0059
Breg %	13(7.0–17.8)	7.64(3.6–14.2)	7.47(3.7–10.5)	0.1635	0.0842	0.1405	0.7458
CD5+ Breg %	49.3(41.367.6)	47.9(41.3–58.2)	34.5(25–42.5)	0.0043	0.5792	0.0044	0.0042

IQR: Interquartile Range; HC: Healthy Control; LTBI: latent TB infection; TB: tuberculosis; aMBCs: atypical memory B cells; AMBCs: Activated memory B cells; RMBCs: Resting memory B cells; CSMBCs: Class switched memory B cells; NCSMBCs: Non-class switched memory B cells; CMZ: Circulating marginal zone; Breg: Regulatory B cells.

3.3. Distribution of memory B-cell subsets in active TB, LTBI, and HC

In this study memory B-cells subsets were detected in PBMCs based on the combined expression patterns of CD27 and CD21 within a total B-cell population gate. Naive B-cells were identified as CD21+CD27−; resting B-cells as CD21+CD27+; atypical memory B-cells as CD21−CD27-and activated B-cells as CD21−CD27+. Likewise, class-switched, and non-class-switched memory B cell subsets were defined as IgM− IgD− and IgM+ IgD+, respectively (Table 3).

Table 3.

Definitions of B cells subsets.

Parameter	Subset
CD19+	B cell
CD19+CD27−CD21+	Naïve B cell
CD19+CD27−CD21−	Atypical Memory B cell
CD19+CD27+CD21−	Activated Memory B cell
CD19+CD27+CD21+	Resting Memory B cells
CD19+CD27+CD21+sIgM+sIgD+	Non-class switched Memory B cell
CD19+CD27+CD21+ sIgM−sIgD−	Class switched Memory B cell
CD19+CD27+CD21+sIgM+sIgD+CD23−	Circulating Marginal Zone
CD19+CD24hiCD38hi	Regulatory B cells
CD19+CD24hiCD38hiCD5+	CD5+ Regulatory B cells

Frequencies of naïve and activated memory B-cells did not differ among the evaluated study participants. (Table 2). Active TB groups showed a significant decrease in resting memory B cells percentage compared to both HCs (p = 0.001) and LTBI (p=<0.0001) groups. In active TB cases, however, atypical memory B cells increased compared to LTBI and HCs, (p = 0.0017 and p = 0.0036, respectively) (Table 2).

The relative fraction of B-cells with class-switched memory phenotype was higher in active TB compared to HC (p = 0.0118) (Table 2). In contrast, significantly decreased proportions of non-class switched memory B cells were observed in the TB group compared to the HC group (p = 0.0021). The percentage of class-switched memory B-cells (IgM− IgD−) in LTBI cases was significantly higher (p = 0.0041) than in HC whereas Non-class switched memory B cells (IgM+ IgD+) were significantly lower (p = 0.0027) (Table 2).

Additionally, the percentage of CMZ B cells CD19+CD27+CD21+sIgM+sIgD+CD23− within non-class switched memory B cells was higher in the TB group compared to the HCs (p = 0.0012) and LTBI (p = 0.0059). There was no statistically significant difference observed between LTBI and HC (Table 2).

3.4. The proportion of regulatory B-cells (Breg) subsets among study participants

Regulatory B cells in TB have been characterized as CD24hiCD38hi and CD1d+CD5+. Accordingly, we evaluated the frequency of Bregs in PBMCs between active TB, LTBI, and HC.

The proportions of Breg (CD19+CD24hiCD38hi) were similar among the three groups. We also assessed the alternative CD5 expressing subset (CD19+CD24hiCD38hiCD5+). Significantly lower Bregs among active TB patients were observed compared to LTBI (p = 0.0042) and HCs (p = 0.0044). There was no between HCs and LTBI (Table 2).

4. Discussion

B lymphocytes are identified by the expression of CD19 surface antigen, which is present at an early stage of bone marrow development and persists throughout the maturation process [13]. Different subsets of B cells can be distinguished in peripheral blood and other tissues. These are classified according to lineage and differentiation markers and include nave B-cells, immature B-cells, plasma cells, and memory B-cells. Memory B-cells can be further divided based on their expression of CD21 and CD27 or IgD and CD27. Class-switched human B cells are IgM−IgD− (double-negative) and non-class-switched human B cells are IgM+IgD+ (double-positive) [6,13,14]. B-cells have a regulatory phenotype as well, where they produce cytokines such as IL10 and TGF-alpha. Several phenotypes of B-cells with regulatory functions have been termed Bregs, which include, CD19+CD24hiCD38hi B-cells as well as CD5+ Breg cells [6,8].

Human B-cells have been understudied compared to the large number of studies assessing T-cell contributions in the control of M. tuberculosis infection. Studies of B-cells in TB suggest that these cells may contribute to disease pathology other than producing antibodies. Nevertheless, data are often contradictory [6–11]. Therefore, we studied B-cell phenotypes in patients with active TB, with LTBI, and in HC. We identified B-cells subsets based on ten markers.

Percentage and the total number of CD19+ B cells in previous studies of active untreated TB patients have been reported to be decreased relative to healthy controls in some [6,10] with other reports showing higher levels [11]. In our study, we observed decreased levels of B cells. Whether the frequency is low because of reduced total numbers in the body, or due to sequestration from the blood into diseased tissues is not clear. Variability in the dynamics of B cell tissue distribution, generation, and death may underly some of the discrepancies seen in the literature.

Similarly, levels of memory B-cells subsets were assessed in our study. We observed that active TB patients had higher frequencies of class-switched (IgM−IgD−) memory B lymphocytes, but not naïve B cells (CD21+CD27−) in their circulation than healthy controls. Class switching is associated with B cell memory, leading to more efficient clearance of infections than primary antibody responses [15]. Moreover, resting memory (CD21+CD27+) B-cell frequency was significantly decreased in active TB patients when compared to LTBI and HCs. In this investigation, proportions of activated memory B cells (CD21−CD27+) in active TB patients were higher than LTBI; consistent with findings in a previous study about active tuberculosis [6]; however, these differences did not achieve statistical significance in both studies. Furthermore, the study has shown that HIV-1 infection leads to increased numbers of activated memory B cells [16].

In the present study Atypical memory (CD21−CD27−) B cells were also investigated. Atypical memory B cells have been found in healthy individuals at low numbers, suggesting in disease settings these cells are an expansion of normally occurring cells. However, atypical memory B cells do not actively secrete antibodies or differentiate into antibody-secreting cells (ASCs) and also, exhibit reduced B cell receptor (BCR)-mediated signaling. Accordingly, they exhibit markedly impaired BCR-triggered Ca2+ mobilization, proliferation, and cytokine production [17,18]. In line with our report, phenotypic studies revealed that CD21−CD27− atypical memory B cells were similarly increased in the peripheral blood of patients with active TB than LTBI and HC, with levels normalizing after therapy [6]. Additionally, chronic infections such as hepatitis C virus [19], HIV-1 [16], and malaria [18,20] have been associated with atypical memory B cells. In contrast, decreased numbers of CD10−CD21−CD27− atypical memory B cells were found in patients with untreated erythema nodosum leprosum (ENL) reactions [15].

Marginal zone (MZ) B cells reside at the interface between the circulation and the white pulp of the spleen. They provide a first line of defense against infections by blood-borne viruses and encapsulated bacteria by rapidly producing IgM and class-switched IgG antibodies [21]. In peripheral blood, circulating marginal zone B cells have been described as IgM + IgD + CD27+ [22,23] and CD19+IgM+CD23−CD27+ [7]. In this study the number of circulating marginal zone B-cells (CD19+CD27+CD21+IgM+IgD+CD23−) in active TB cases was at a higher percentage than in LTBI; the contrary is reported by Du Plessis WJ et al. [7].

A consensus set of cell surface markers to specifically identify Breg has not been defined. Since numerous cell surface markers have been linked to Breg, a combination of these markers has been proposed as a method of better identifying Breg populations [24,25]. Therefore, in our study, we assessed CD19+CD24hiCD38hiBreg cells and an alternative CD19+CD24hiCD38hiCD5+ Breg marker combination, the latter of which are a main source of IL-10 that contribute to Breg suppressive function [6]. A higher level of IL-10 has been found in sputum and bronchoalveolar lavage fluid (BALF) [26]. Consistent with a previous report by Joosten and colleagues [6], we found in our study of human PBMC that CD19+CD24hiCD38hi Breg was not significantly different among the three study groups. However, in our study, the CD5+ subset was significantly lower in active TB patients when compared to LTBI and HCs. Contrary to our finding, Zhang M et al. reported significantly higher frequencies of CD19+CD5+CD1d+ B cells with stronger suppressive activity in TB patients than in healthy donors [8].

A limitation of our study was that we did not assess TB cases following treatment. However, we identified multiple subsets of B cells associated with TB infection and disease from a high TB burden country.

5. Conclusion

We observed significant differences in total B cells and B cell subsets in TB patients compared with healthy controls or those with latent TB, consistent with the involvement of B cells in the immuno-pathogenesis of TB. Based on changes in the profiles of B-cells during Mycobacterium tuberculosis infection and disease, these markers may be useful as biomarkers for TB disease and LTBI identification. These findings need to be confirmed and translated into clinically useful tests through large-scale longitudinal studies.

Abbreviations

AHRI

Armauer Hansen Research Institute

ASCs

antibody-secreting cells

BALF

Bronchoalveolar lavage fluid

BCR

B cell receptor

Bregs

Regulatory B cells

CMZ

Circulating Marginal Zone

DCs

Dendritic cells

EDTA Na2

Ethylenediaminetetraacetic acid disodium

ELISA

Enzyme-linked Immuno-Sorbent Assay

FACS

Fluorescence Activated Cell Sorting

FBS

Fetal bovine serum

FMO

Fluorescence minus one

FSC

Forward scatter

HCs

Healthy controls

HIV

Human Immunodeficiency Virus

IGRA

Interferon gamma release assay

LJ

Lowenstein Jensen Media

LTBI

Latent tuberculosis infection

MTB

Mycobacterium tuberculosis

PBMC

Peripheral blood mononuclear cell

PBS:

Phosphate buffered saline

PTB

Pulmonary tuberculosis

QFN:

QuantiFERON

sIg:

Surface immunoglobulin

SSC

Side scatter

TB

Tuberculosis

TGF:

Transforming growth factor

VCT

Voluntary Counseling and testing

WHO

World Health Organization