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Commun Biol
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Nature Publishing Group UK London

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10.1038/s42003-024-06822-1
Article
A serum B-lymphocyte activation signature is a key distinguishing feature of the immune response in sarcoidosis compared to tuberculosis
http://orcid.org/0000-0002-9350-5133
Putera Ikhwanuliman 1234
Schrijver Benjamin 3
Kolijn P. Martijn 3
http://orcid.org/0000-0003-0698-4672
van Stigt Astrid C. 235
ten Berge Josianne C. E. M. 1
IJspeert Hanna 35
Nagtzaam Nicole M. A. 3
http://orcid.org/0000-0003-4339-8189
Swagemakers Sigrid M. A. 6
van Laar Jan A. M. 2
Agrawal Rupesh 7891011
Rombach Saskia M. 2
van Hagen P. Martin 23
La Distia Nora Rina 4
http://orcid.org/0000-0001-5235-3156
Dik Willem A. w.dik@erasmusmc.nl

3
1 https://ror.org/018906e22 grid.5645.2 0000 0004 0459 992X Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands
2 https://ror.org/018906e22 grid.5645.2 0000 0004 0459 992X Department of Internal Medicine Section Allergy & Clinical Immunology, Erasmus University Medical Center, Rotterdam, the Netherlands
3 https://ror.org/018906e22 grid.5645.2 0000 0004 0459 992X Laboratory Medical Immunology, Department of Immunology, Erasmus University Medical Center, Rotterdam, the Netherlands
4 https://ror.org/0116zj450 grid.9581.5 0000 0001 2019 1471 Department of Ophthalmology, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
5 https://ror.org/018906e22 grid.5645.2 0000 0004 0459 992X Academic Center for Rare Immunological Diseases (Rare Immunological Disease Center), Erasmus University Medical Center, Rotterdam, the Netherlands
6 https://ror.org/018906e22 grid.5645.2 0000 0004 0459 992X Department of Bioinformatics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
7 https://ror.org/032d59j24 grid.240988.f 0000 0001 0298 8161 National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore
8 https://ror.org/02e7b5302 grid.59025.3b 0000 0001 2224 0361 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
9 grid.428397.3 0000 0004 0385 0924 Ophthalmology and Visual Sciences Academic Clinical Program, Duke NUS University, Singapore, Singapore
10 https://ror.org/02crz6e12 grid.272555.2 0000 0001 0706 4670 Singapore Eye Research Institute, Singapore, Singapore
11 https://ror.org/03tb37539 grid.439257.e 0000 0000 8726 5837 Moorfields Eye Hospital, London, United Kingdom
10 9 2024
10 9 2024
2024
7 11146 4 2024
2 9 2024
© The Author(s) 2024
2024
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Sarcoidosis and tuberculosis (TB) are two granulomatous diseases that often share overlapping clinical features, including uveitis. We measured 368 inflammation-related proteins in serum in both diseases, with and without uveitis from two distinct geographically separated cohorts: sarcoidosis from the Netherlands and TB from Indonesia. A total of 192 and 102 differentially expressed proteins were found in sarcoidosis and active pulmonary TB compared to their geographical healthy controls, respectively. While substantial overlap exists in the immune-related pathways involved in both diseases, activation of B cell activating factor (BAFF) signaling and proliferation-inducing ligand (APRIL) mediated signaling pathways was specifically associated with sarcoidosis. We identified a B-lymphocyte activation signature consisting of BAFF, TNFRSF13B/TACI, TRAF2, IKBKG, MAPK9, NFATC1, and DAPP1 that was associated with sarcoidosis, regardless of the presence of uveitis. In summary, a difference in B-lymphocyte activation is a key discriminative immunological feature between sarcoidosis/ocular sarcoidosis (OS) and TB/ocular TB (OTB).

Sarcoidosis patients exhibit higher level of serum B cell activation signature compared to tuberculosis, regardless of uveitis manifestation.

Subject terms

Biomarkers
Immunological disorders
Tuberculosis
Uveal diseases
Adaptive immunity
Riset Inovatif Produktif - Lembaga Pengelola Dana Pendidikan (RISPRO-LPDP) [RISPRO/KI/B1/KOM/5/15219/4/2020].issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Sarcoidosis and tuberculosis (TB) are two multi-systemic granulomatous inflammatory diseases that most often affect the lungs, although multiple organs can be affected, including the eyes1–3. Sarcoidosis is a disorder of unknown etiology in which a complex interaction exists between host, genetics, and environmental triggers. This is thought to result in aberrant immune activation to unidentified antigens4–6. Moreover, sarcoidosis is associated with both autoinflammatory and autoimmune features2,5,7–11. TB, on the other hand, is an infectious disease caused by Mycobacterium tuberculosis (Mtb) infection7,8,12. The granulomas in sarcoidosis and TB display different histopathological characteristics; sarcoidosis is characterized by non-caseating granulomas with negative staining for infectious organisms and foreign material, while TB shows caseating granulomas with a positive acid-fast bacilli (AFB) smear or culture of Mtb growth1,2,7. Exposure to certain micro-organisms or their non-degradable remnants has been proposed as one of the triggering events for sarcoidosis4,6. Because of disease similarities, Mtb is among the most studied microbial aetiologies in sarcoidosis4,6. Detection of Mtb components in sarcoid tissues and enhanced peripheral T-lymphocyte responses to Mtb antigens, such as KatG and ESAT-6, have been observed6,13. In line with this, communalities in lungs, lymph nodes, and peripheral blood gene transcription signatures and microRNA profiles have been reported between TB and sarcoidosis, although disease-specific signatures were observed as well14–17.

Uveitis, an intraocular inflammation, may occur as an extra-pulmonary manifestation in both sarcoidosis and TB. Granulomatous uveitis, characterized by granulomatous anterior chamber inflammation and choroidal granuloma, as well as retinal vasculitis, are clinical features commonly observed in both ocular sarcoidosis (OS) and ocular TB (OTB)9,18. Globally, OS accounts for 2-17% (median prevalence estimate: 6.7%) of uveitis cases2, while OTB accounts for ~7-11% of all uveitis cases in TB endemic regions and for ~3% in non-TB-endemic regions19. Certain patients might be categorized as having a “gray zone” diagnosis, where distinguishing between OS and OTB becomes challenging, resulting in what is sometimes referred to as “tuberculous-sarcoid uveitis” or “sarcoid-tuberculous uveitis”7. Moreover, concomitant sarcoidosis and TB and the occurrence of OS preceding systemic TB in the same patients have been previously described20–22. These observations further suggest a potential association between these two granulomatous diseases7.

Precise and timely diagnosis of TB and sarcoidosis are crucial since the two conditions are managed in distinct ways. Sarcoidosis relies on immunosuppression, while TB relies on anti-tubercular treatment3,7,8. However, in many clinical scenarios, establishing the diagnosis of sarcoidosis and TB using the currently available diagnostic modalities might not be straightforward3,18. The diagnostic dilemma is especially encountered in patients manifesting solely with uveitis without the involvement of any other organ system9,23. Obtaining ocular samples for histopathological examination of aforementioned specific granuloma characteristics or to detect Mtb, for instance by polymerase chain reaction (PCR), is practically challenging12. In addition, PCR analysis for OTB diagnosis lacks diagnostic power and is not endorsed by the latest classification criteria for routine diagnosis support24. Meanwhile, diagnosis of OS often depends on positive findings from other diagnostic modalities, such as chest computed tomography (CT), serum angiotensin-converting enzyme (ACE), serum soluble interleukin-2 receptor (sIL-2R) and bronchoalveolar lavage (BAL), together with exclusion of systemic Mtb infection by a negative tuberculin skin test (TST) or interferon-gamma release assay (IGRA) test9,23,25. Nonetheless, in a high TB endemic setting, the presence of pulmonary parenchymal, pleural, and mediastinal lymph node abnormalities on chest radiographs without biopsy confirmation of non-caseating granulomas might also entangle the attending physician in the discrimination between true sarcoidosis and TB8. Thus, finding reliable disease-specific biomarkers that align with the underlying disease-specific pathways in sarcoidosis and TB, and that are applicable across the spectrum of patients with uveitis, is an urgent need and a topic of interest in current research12.

To date, it is still unknown why some patients with sarcoidosis or TB develop uveitis and whether common or disease-specific immune-related pathways are involved. The serum proteome, more easily accessible than the intraocular fluid proteome, provides an attractive route to unravel differences in pathobiological pathways that underlie disease entities26–28. While previously we identified candidate biomarkers through targeted vitreous proteomic analysis in OS and other uveitis29, the current approach of identifying a serum protein signature would avoid the potential risk of additional intraocular complications associated with diagnostic procedures attributed to intraocular sample collection30. Moreover, a proteomic approach may identify proteins that have the potential to serve as biomarker for diagnosis or monitoring of disease activity, or that may represent potential treatment targets28,31. By using a targeted proteomic approach to measure inflammation-related proteins in serum from sarcoidosis and TB patients with and without uveitis as well as healthy controls (HC), this study aimed: (1) to reveal and compare the biological pathways and upstream regulators associated with differentially expressed proteins (DEPs) observed in serum of each disease and (2) to explore proteins in the serum during clinically active uveitis in both diseases and further identify proteins with the potential to serve as diagnostic biomarker of sarcoidosis/OS and TB/OTB.

Results

Patient characteristics

In total, 90 sarcoidosis patients and 22 active pulmonary TB patients were analyzed in this study. While female patients were more common among those diagnosed with sarcoidosis, males constituted the majority of TB patients included (Pearson Chi-Square test p = 0.029). TB patients were younger than sarcoidosis patients (t test p = 0.001). Among S + OS and P + OTB patients, panuveitis and posterior uveitis were the two most commonly observed anatomical sites of uveitis (Table 1).Table 1 Characteristics of sarcoidosis and TB patients included in the analysis

	Sarcoidosis (the Netherlands)	TB (Indonesia)	
All Sarcoidosis N = 90 (%)	With uveitis (S + OS) N = 46 (%)	Without uveitis (SS) N = 44 (%)	All TB N = 22 (%)	With uveitis (P + OTB) N = 12 (%)	Without uveitis (PTB) N = 10 (%)	
Age (years)	53.3 ± 14.6	53.9 ± 16.0	52.8 ± 13.0	41.6 ± 16.2	42.0 ± 17.2	41.2 ± 15.7	
Sex	
 Male	38 (42.2%)	20 (43.5%)	18 (40.9%)	15 (68.2%)	8 (66.7%)	7 (70.0%)	
 Female	52 (57.8%)	26 (56.5%)	26 (59.1%)	7 (31.8%)	4 (33.3%)	3 (30.0%)	
Place of birth	
 Indonesia	0	0	0	22 (100%)	12 (100%)	10 (100%)	
 the Netherlands	64 (71.1%)	29 (63.0%)	35 (79.5%)	0	0	0	
 Europe other than the Netherlands	4 (4.4%)	3 (6.5%)	1 (2.3%)	0	0	0	
 Others	20 (22.2%)	12 (26.1%)	8 (18.2%)	0	0	0	
 N/A	2 (2.2%)	2 (4.3%)	0	0	0	0	
Anatomical site of uveitis	
 Anterior uveitis		0			1 (8.3%)		
 Intermediate uveitis		4 (8.7%)			0		
 Posterior uveitis		1 (2.2%)			6 (50.0%)		
 Panuveitis		39 (84.8%)			5 (41.7%)		
 Unknown		2 (4.3%)			0		
N/A data not available

Serum proteomic profile of sarcoidosis and TB

Heat maps generated from the entire set of measured proteins in each cohort revealed that sarcoidosis cases showed a distinct pattern of measured proteins compared to DHC. Likewise, TB cases displayed a different protein pattern than IHC. It is important to note that there was no clear pattern associated with the presence of uveitis in either sarcoidosis or TB compared to patients without uveitis (Fig. 1A and B and Supplementary Fig. 1). In addition, no DEPs were observed in relation to active uveitis in sarcoidosis (Supplementary Fig. 2A), while OTB was associated with a total of 13 downregulated DEPs (BANK1, NUB1, CCL26, SHMT1, MGMT, TBC1D5, FGF2, CCL7, TGFA, IL6, FCAR, CST7, and IL7; Supplementary Fig. 2B). In total, 192 DEPs were found in relation to sarcoidosis and 102 DEPs in relation to TB (Figs. 1C and 1D). Of these DEPs 113 were specific to sarcoidosis and 23 to TB, while 79 DEPS were shared between both conditions (Fig. 1E). The top 10 DEPs of total sarcoidosis groups versus DHC and total TB groups versus IHC are listed in Table 2.Fig. 1 Serum proteome analysis in sarcoidosis and TB.

A Supervised heat map of 368 proteins from the Olink® inflammation panel in sarcoidosis versus Dutch HC (DHC). B Supervised heat map of 368 proteins from the Olink® inflammation panel in active pulmonary TB versus Indonesian HC (IHC). C Volcano plot of DEPs between sarcoidosis versus DHC. D Volcano plots of DEPs between active pulmonary TB versus IHC. E Venn diagram depicting disease specific and overlapping DEPs between sarcoidosis vs DHC and TB vs IHC comparisons.

Table 2 Top 10 increased expression of proteins in sarcoidosis and active pulmonary TB compared to healthy controls

No	Gene name	Protein name	Fold change	B-H corrected p-value	
Dutch cohort: Sarcoidosis vs Dutch HC	
1	SHMT1	serine hydroxymethyltransferase 1	3.68	2.81E−07	
2	HSPA1A/HSPA1B	heat shock protein family A (Hsp70) member 1A	3.42	4.42E−09	
3	DAPP1	dual adaptor of phosphotyrosine and 3-phosphoinositides 1	3.30	1.11E−07	
4	FGF19	fibroblast growth factor 19	3.24	1.85E−04	
5	NCK2	NCK adaptor protein 2	3.22	2.16E−05	
6	CEACAM21	CEA cell adhesion molecule 21	3.18	5.99E−04	
7	TREM2	triggering receptor expressed on myeloid cells 2	3.17	8.67E−09	
8	LAMP3	lysosomal associated membrane protein 3	2.89	1.73E−06	
9	IL18	interleukin 18	2.86	1.04E−06	
10	LGALS9	Galectin 9	2.86	4.42E−09	
Indonesian cohort: Active pulmonary TB vs Indonesian HC	
1	IL18	interleukin 18	104.237	1.17E−03	
2	LILRB4	leukocyte immunoglobulin like receptor B4	98.04	7.37E−04	
3	HEXIM1	HEXIM P-TEFb complex subunit 1	80.444	2.27E−02	
4	BANK1	B cell scaffold protein with ankyrin repeats 1	45.762	4.79E−02	
5	CD79B	CD79b molecule	40.985	7.47E−03	
6	IL1B	interleukin 1 beta	28.11	7.58E−03	
7	CKAP4	cytoskeleton associated protein 4	22.966	3.35E−03	
8	LSP1	lymphocyte specific protein 1	14.637	8.65E−03	
9	PLAUR	plasminogen activator, urokinase receptor	13.6	3.05E−04	
10	ENAH	ENAH actin regulator	12.802	2.14E−02	
B–H Benjamini–Hochberg

Activated and inhibited pathways and upstream regulators in sarcoidosis and TB

We observed clear similarities between sarcoidosis and TB in terms of their activated and inhibited canonical pathways (Fig. 2A and Supplementary Table 1). Using the hierarchical clustering sorting method for activation z-score, we found that the most activated pathways in both sarcoidosis and TB are related to pathogen-induced cytokine storm signaling, wound healing signaling, macrophage classical activation signaling pathway, T-helper (Th)1 pathway, and crosstalk between dendritic cells and natural killer cells (Fig. 2A and Supplementary Table 1). Interestingly, the most strongly associated pathways with sarcoidosis were both B-cell activation pathways (Fig. 2A), namely B cell activating factor (BAFF) signaling (Fig. 2B) and a proliferation-inducing ligand (APRIL) mediated signaling (Fig. 2C). While BAFF itself is not included in the Olink® Explore Inflammation panel, we confirmed higher serum BAFF in sarcoidosis compared to TB and HCs using luminex-immunoassay (Fig. 2B). In addition, luminex-analysis revealed higher level of serum APRIL in sarcoidosis compared to DHC, in line with the observations from the Olink® panel, but was comparable to TB (Fig. 1C). Furthermore, IPA analysis identified the “IL-17F in allergic inflammatory airway disease” pathway to be specifically enhanced in TB compared to sarcoidosis, with a specific increase in CCL4. However, the other two pathways related to IL-17 signaling pathways (“IL-17 signaling” and “IL-17A signaling in fibroblasts”), were both activated in sarcoidosis and TB (Fig. 2A and Supplementary Table 1).Fig. 2 Ingenuity canonical pathways overrepresented by identified DEPs in sarcoidosis and TB.

A Comparison of canonical pathways inhibited or activated in sarcoidosis and TB using hierarchical clustering sorting method in IPA® showing pathways with z-score > 2 and < -2. B BAFF mediated signaling pathway and (C) APRIL mediated signaling pathway, as retrieved from IPA® and predicted activation to be more specifically related to sarcoidosis. The right bar graph depicts the corresponding level of measurement with Luminex assay. The height of the bar represents the median value, and the upper and lower horizontal bars represent 95% confidence interval. Symbols depict the proteins and the coloring indicates the following: Red proteins measured at increased level, green proteins measured at decreased level, orange proteins predicted to be activated, and blue proteins predicted to be inhibited. Line coloring indicates the following: orange line indicates activating effect, blue line indicates inhibitory effect, yellow line indicates inconsistency with what was actually measured for the respective protein, and gray line indicates an unpredictable effect. Line types indicate the following: solid line indicates a direct relationship and dashed line indicates indirect relationship. Two-sided p values: *p < 0.05, **p < 0.01, ***p < 0.001.

Given that age and sex differed significantly between sarcoidosis and active TB patients, an analysis with age and sex adjustment was also performed. The majority of DEPs in the initial unadjusted analysis for sarcoidosis (191 out of 192 DEPs) and nearly half of those for active TB (42 out of 102) were still observed using linear regression analysis for the protein NPX values. Comparison of analyses between unadjusted and adjusted-derived Ingenuity pathways showed no major differences. Importantly, BAFF signaling and APRIL-mediated signaling pathways were still significantly activated in sarcoidosis but not in active TB (Supplementary Fig. 3).

In line with the canonical pathways shared between sarcoidosis and TB, we also found that predicted upstream regulators strongly overlapped between both diseases. Lipopolysaccharide (LPS), Tumor Necrosis Factor (TNF), tetradecanoylphorbol acetate, and poly rI:rC-RNA are predicted as the most activated upstream regulators (Supplementary Table 2). The mechanistic networks of those aforementioned upstream regulators with other key regulators that are predicted to be involved in the identified protein expression profiles are depicted in Supplementary Fig. 4. Protein networks generated using IPA® from the identified DEPs in sarcoidosis are associated with “immunological disease,” “inflammatory response,” and “organismal injury and abnormalities.” In TB, the protein networks are associated with “hematological disease,” “immunological disease,” and “infectious disease” (Supplementary Fig. 5).

Contextualizing the findings of the proteomic assay with transcriptomic data

Next we explored whether our proteomic analysis demonstrated relevant consistency to publicly available blood transcriptomic profiles from sarcoidosis and TB. To achieve this, we conducted an overlay comparison between our identified Ingenuity canonical pathways and upstream regulators with publicly available transcriptomic datasets (GSE 83456). Several genes/proteins overlap between the transcriptomic and proteomic analyses in sarcoidosis and TB (Fig. 3A). We observed a number of similar patterns of activated and inhibited pathways in both sarcoidosis and TB between the transcriptome-proteome data. Specifically, even though we did not observe upregulation of BAFF and APRIL-mediated signaling pathways in the transcriptomic dataset (Fig. 3B), we noted downregulation of these pathways in TB (data not shown). This observation does support the overall notion that these pathways are less activated in TB than in sarcoidosis. Furthermore, the transcriptome-proteome analysis of upstream regulators, considered as drivers of the observed overrepresented genes and proteins, also revealed clear similarities between sarcoidosis and TB (Fig. 3C).Fig. 3 Comparative analysis of differentially expressed genes/proteins, pathways, and upstream regulators between transcriptomic and proteomic datasets.

A Genes overlap between transcriptomic (from GSE 83456) and current proteomic analysis in sarcoidosis and TB cohorts. B Overlay comparison of ingenuity canonical pathways generated from transcriptomic datasets (GSE 83456) with our identified proteome of sarcoidosis and TB. Pathways were hierarchically sorted by using a cut-off value of z-score > 3 and <−3. C Overlay comparison of upstream regulators generated from transcriptomic datasets (GSE83456) with our identified proteome of sarcoidosis and TB (hierarchically sorted by using a cut-off value of z-score > 5 and <−5).

In comparison to our previously generated RNA-sequencing data from Mtb-infected RPE cells32, we observed a comparable pattern of activated and inhibited pathways, as well as upstream regulators, when juxtaposed with the proteomic analysis of sarcoidosis and TB (Supplementary Fig. 6). Furthermore, analysis match feature in IPA® allowed us to also compare our results to other publicly available datasets, including those from in vitro studies. We identified two available transcriptomic datasets from Mtb-infected human monocyte-derived macrophages33,34 that demonstrated moderate to high similarity with our pulmonary TB dataset. Interestingly, the same level of similarity was also observed between the two Mtb-infected human monocyte-derived macrophage RNA sequencing datasets and our sarcoidosis serum proteome dataset. This analysis further confirmed the communalities in canonical pathways and upstream regulators we observed between TB and sarcoidosis (see Supplementary Table 3).

Identification of serum B-lymphocyte activation signature as key distinctive immunological feature in sarcoidosis compared to TB

As we observed the specific activation of the BAFF signaling and APRIL-mediated signaling pathways in sarcoidosis through IPA® analysis, and further verification using luminex assay confirmed a significantly elevated BAFF level in the sera of sarcoidosis groups compared to TB and HC groups (Fig. 2B), we here highlight increased DEPs that reflect B-lymphocyte activation in sarcoidosis. NPX values of five DEPs (TNFRSF13B/TACI, TRAF2, IKBKG, MAPK9, and NFATC1) involved in the BAFF signaling pathway (depicted in Fig. 2B), and one other protein (SLAMF7) were significantly elevated in sarcoidosis, irrespective of the presence of uveitis (Fig. 4A). We attempted to confirm this finding by measuring TNFRSF13B/TACI with another immunoassay (luminex assay: R&D Systems, Abingdon, UK), but values were below the detection limit of the assay (data not shown).Fig. 4 Identification of B-lymphocyte activation markers in sarcoidosis.

A NPX values from Olink® for TNFRSF13B/TACI, TRAF2, IKBKG, MAPK9, NFATC1, and SLAMF7 in sarcoidosis versus Dutch HC and TB versus Indonesian HC comparisons. B Representative hematoxylin & eosin and corresponding CD20 immunofluorescence microscopy images were analyzed for lymph node granulomas in sarcoidosis and TB. Annotated areas for CD20-stained cells quantification were represented by purple-bounded areas for granulomas (G) and red-bounded areas for the surroundings of granulomas (S). The bar graph represents CD20-positive cell counts normalized based on the total cells detected per annotated area from processed slides in sarcoidosis (SARC) and TB. post-hoc test B-H corrected two-sided *p <0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

To evaluate the influence of the unequal sample sizes on our initial analysis between the sarcoidosis and active pulmonary TB cohorts, we down sampled the sarcoidosis cohort. Using the DownSample function in the “caret” package in R, we randomly selected 20 samples from the sarcoidosis cohort (10 each with and without uveitis). An age- and sex-adjusted analysis of these 20 sarcoidosis patients versus DHC showed that, except for SLAMF7, the other five BAFF signaling-associated proteins remained significantly elevated, and subsequently, the BAFF signaling pathway also remained listed as a significantly activated pathway in the IPA® analysis, which again fits the significantly higher BAFF levels we observed in thirty randomly selected sarcoidosis patients (15 with and 15 without uveitis) compared to active pulmonary TB patients and HC (Fig. 2B).

Furthermore, when looking at the top 10 identified DEPs in the comparison of total sarcoidosis groups versus DHC (Table 2), we identified another protein related to B-lymphocyte activation but not included in the Ingenuity canonical BAFF signaling pathway, namely DAPP1/B-lymphocyte Adapter Protein Bam32. Of note, DAPP1 is not identified among the 102 DEPs in the comparison of total TB groups versus IHC. Next, to support our finding that B-lymphocyte involvement may be more prominent in sarcoidosis than TB we histo-pathologically analyzed granulomas obtained from both diseases for the presence of B-lymphocytes. This revealed that areas directly adjacent to sarcoid granulomas contain higher numbers of B-lymphocytes (CD20+ cells) than TB granulomas, while B-lymphocyte numbers within the granulomas did not differ between both diseases (Fig. 4B).

Identification of additional protein signatures for distinguishing ocular sarcoidosis from ocular tuberculosis

Due to our inability to perform direct analysis of sarcoidosis versus TB for the identification of DEPs between these two entities, we employed an alternative Venn diagram approach to search for potential biomarkers to distinguish sarcoidosis from TB, with a specific focus on patients exhibiting disease-specific uveitis. This approach enabled us to identify DEPs non-overlapping in the direct comparisons we made between sarcoidosis vs DHC and TB vs IHC. Moreover, it is important to note that our direct comparisons within each disease group between cases with and without uveitis did not reveal any elevated DEPs associated with the presence of uveitis. This result could be interpreted as either a lack of power to detect elevated DEPs with our limited sample size, or it may truly reflect absence of elevated DEPs in case of uveitis. Our Venn diagram approach identified 112 DEPs associated with the total sarcoidosis group and not TB (group ‘a’ proteins, supplementary fig. 8), with an additional 15 DEPs associated only with OS (group ‘b’ proteins, Supplementary Fig. 8). Likewise, 6 DEPs were specifically associated with the total TB group and not sarcoidosis (group ‘c’ proteins, Supplementary fig. 8), with an additional 1 DEP specifically associated only with OTB (group ‘d’ proteins. supplementary fig. 8). Subsequently, we selected eight proteins and their serum levels were determined with another immunoassay (Luminex: R&D Systems, Abingdon, UK). This analysis confirmed that serum levels of IL-16, TNF superfamily member 10 (TNFSF10)/TNF-related apoptosis-inducing ligand (TRAIL), FGF2, CCL11/eotaxin, and granzyme B (GZMB) were significantly elevated in sarcoidosis, especially in the S + OS group (Fig. 5). Importantly, among these proteins, IL-16, FGF2, and GZMB, which showed comparable levels between IHC and DHC, were significantly higher in the S + OS group than the P + OTB group. This suggests that the differences we observed in the abundance of these particular proteins are more associated with disease specificity rather than geographical differences. The FASLG level was elevated in the sarcoidosis group compared to the TB and HC groups, although not statistically significant after applying multiple testing corrections (possibly due to the limited number of samples in each group). Despite the lower IL-17C level in DHC than IHC comparable levels were observed between TB and sarcoidosis groups.Fig. 5 Identification of potential serum protein candidates in ocular sarcoidosis and tuberculosis.

Bar graphs showing the level of eight selected proteins in the verification step are presented. All measurements were acquired through Luminex assays, with the exception of AMBN, which was obtained through ELISA. The height of the bar represents the median value, and the upper and lower horizontal bars represent 95% confidence interval. Post-hoc test B-H corrected two-sided p-values *p < 0.05, **p < 0.01, ***p < 0.001. PTB = active pulmonary TB without uveitis, P + OTB = active pulmonary TB with uveitis, SS = Sarcoidosis without uveitis, S + OS = sarcoidosis with uveitis, IHC = Indonesian healthy controls, DHC = Dutch healthy controls.

Discussion

In this study, we conducted serum proteomics analysis that provided valuable insights into systemic proteins and biological pathways involved in sarcoidosis and TB and uncovered potential novel diagnostic biomarkers. To the best of our knowledge, our study is the first to compare the inflammatory serum proteome between sarcoidosis and TB, with specific attention to patients with uveitis (OS and OTB). Our findings highlight that, despite clear differences in serum DEPs between sarcoidosis and active TB, the two diseases exhibit significant overlap in the activation status of different biological pathways and upstream regulators. These findings support substantial pathophysiological communalities between the two diseases, which may explain, to a certain extent, the overlap in clinical manifestations between these diseases7,8. Yet, a number of disease-specific biological pathways were also identified. Importantly, we observed that activation of B-lymphocytes is more prominently associated with sarcoidosis. This was reflected by increased abundance of several B-lymphocyte signaling-related molecules, namely BAFF, TNFRSF13B/TACI, TRAF2, IKBKG, MAPK9, NFATC1, as well as DAPP1 in sarcoidosis.

APRIL and BAFF, both type II transmembrane proteins, play pivotal roles in B-lymphocyte maturation and survival35. These proteins share two receptors, the tumor necrosis factor receptor superfamily (TNFRSF)13B, also known as TACI and TNFRSF17, also known as BCMA35. Additionally, BAFF also exerts its effects through the BAFF receptor (TNFRSF13C, see Fig. 2B, C). Expression patterns of these three receptors varies across the different B-lymphocyte developmental stages35. With additional immunoassays, we could indeed confirm elevated BAFF and APRIL levels in serum from sarcoidosis. The observed activation of these crucial B-lymphocyte pathways in sarcoidosis further strengthens previous findings from our laboratory and others that B-lymphocytes are involved in sarcoidosis and may directly contribute to the local inflammatory process36–40. Importantly, the immunostaining we conducted on granulomas of sarcoidosis and TB revealed a notable higher B-lymphocyte accumulation in the direct surrounding of sarcoidosis granulomas. This observation further supports significantly higher B-lymphocyte involvement in sarcoidosis than TB. Furthermore, previous work from our laboratory, revealed that the peripheral B-lymphocyte compartment of sarcoidosis patients demonstrated a reduced population of natural effector B-lymphocytes, CD27+IgM+, CD27+IgG+, and CD27+IgA+ memory B-lymphocytes, as well as plasma cells40. This finding aligns with a study by Sassine et al. that demonstrated decreased circulating memory B-lymphocytes and an increase in serum BAFF levels in sarcoidsosis41. In further support of profound B-lymphocyte activation in sarcoidosis with uveitis are the elevated concentrations of BAFF and APRIL in aqueous humor from OS, that exceeded concentrations in other types of non-infectious uveitis (Behcet-associated uveitis, HLA-B27- associated uveitis and Vogt-Koyanagi Harada uveitis)42,43. Yet, the exact role of B-lymphocytes in the pathogenesis of sarcoidosis is a matter of debate. Whether it signifies direct pathogenic activity, compensatory mechanisms, or reflects the selective migration of B-lymphocyte specific subsets to the site of inflammation, remains elusive. In line with the B-lymphocyte activation signaling pathway, we also observed that DAPP1/Bam32 was highly abundant in sarcoidosis (Table 2). DAPP1/Bam32, a 32 kDa B-lymphocyte adapter molecule, has been recognized for its substantial role in B-lymphocyte activation44,45. Interestingly, although not the first line treatment for sarcoidosis, successful management of refractory sarcoidosis using rituximab (anti-B-lymphocyte therapy) has been reported in several small studies46,47. Larger studies are required to further assess the efficacy of rituximab or belimumab to obtain a more solid conclusion on the effect of anti-B-lymphocyte therapy in sarcoidosis.

In active TB, the role of B-lymphocytes is less well explored than that of T-lymphocytes. Nevertheless, B-lymphocytes are considered important for TB immunity48,49. Joosten et al. observed that active PTB was associated with impaired B-lymphocyte function (impaired proliferation, immunoglobulin and cytokine production), lower frequencies of total circulating B-lymphocytes, especially of naïve B-lymphocytes, and increased frequencies of atypical CD21-CD27- and IgD-CD27- memory B-lymphocytes50. Furthermore, they observed only minor B-lymphocyte infiltration in the outer part of granulomas in lung biopsy samples from patients deceased of TB50. These findings are in line with our current observation that active TB is associated with a different level of B-lymphocyte activation than sarcoidosis50.

Limited studies utilizing proteomics approaches, particularly using intraocular fluid samples, have been conducted to investigate the underlying biology and potential protein signatures in both OS and OTB51,52. Given the difficulties related to ocular fluid analysis (i.e., invasiveness and limited sample volume) for research and routine diagnostic purposes30, easily obtainable peripheral blood samples, as we performed, could also offer valuable insights applicable as a discriminative biomarker for OS and OTB. We hypothesized that these two systemic granulomatous diseases presenting with uveitis might exhibit distinct serum proteomic profiles from those without uveitis, with the expectation that certain proteins would be elevated and uveitis-specific. However, our initial direct comparison between disease cases with and without uveitis did not reveal any additional inflammation-associated proteins in the serum specifically linked to the presence of uveitis, either in sarcoidosis or TB. Possibly, the small volume of the eyes compared to that of the whole body and blood volume and the unique immune-privileged status of the eyes might very well limit the release and detection of uveitis-associated proteins in the blood53.

Clinical differentiation between OTB and OS can be troublesome, especially in the context of isolated granulomatous uveitis7. We propose that the serum proteins we identified in the context of B-lymphocyte activation in sarcoidosis are of potential interest to serve as biomarker candidates for discriminating between OS and OTB. This should, however, be confirmed by other studies. Using an alternative approach (depicted in supplementary fig. 8), we identified some additional promising serum biomarker candidates for differentiating OS from OTB. This includes IL-16, TNFSF10/ TRAIL, FGF2, eotaxin, GZMB as well as the previously identified CCL1729,54, which underscores the validity of our approach. These molecules may be linked to specific pathomechanisms known to occur in sarcoidosis. IL-16, along with CCL17, was also found elevated in cerebrospinal fluid of neurosarcoidosis patients55. IL-16 may be involved in the regulation of IL-2-dependent T-lymphocyte proliferation and contributes to B-lymphocyte differentiation56. In our analysis, AMBN (ameloblastin) emerged as the sole protein specific to OTB. While this finding highlights AMBN as a promising serum biomarker for OTB diagnostics, we could not confirm its elevation in OTB with a different immunoassay due to a lack of sample availability and most of the samples tested appeared below the assays limit of quantification. However, a previous serum proteomics study conducted by Penn-Nicholson et al. found AMBN as one of the proteins significantly linked to the progression of active pulmonary TB in previously healthy individuals, but the association with OTB was not explored in that particular study57. Although our observation and the study by Penn-Nicholson et al. support a role for AMBN in TB, its contribution to TB pathogenesis remains to be explored. AMBN is an enamel matrix protein mainly known for its role in dental pulp healing and bone repair, but was also found to contribute to LPS-induced IL-1β production58. Although this latter observations supports a role for AMBN in the regulation of infectious inflammation, future investigations on AMBN are required to delve into its role in TB/OTB.

Besides the specific sarcoidosis-associated B-lymphocyte activation pathways, we identified overlapping pathways and upstream regulators between sarcoidosis and TB, which further supports previous findings that these diseases share common disease pathways. The canonical pathway analysis conducted supports the role of macrophage activation and Th1 signaling pathways in both sarcoidosis and TB. Granuloma formation is well known to involve macrophages and a Th1-dominated immune response, with major contributions of IFNγ, IL-2, IL-12p40, and TNF-α59,60. It has been reported that the peripheral blood transcriptome of pulmonary sarcoidosis and active pulmonary TB display considerable overlap, as was also observed for peripheral blood microRNA expression profiles and selected serum cytokine levels17. It was also observed that IL-1β, IL-2, IL-4, IL-10, IL-13, and TNF-α levels were comparably elevated in BAL fluid from both diseases61. The shared peripheral blood transcriptional patterns comprised the upregulation of pro-inflammatory pathways and interferon signaling in both diseases17,62. In an analysis conducted on human lung tissues, Chai et al. found an overlap of 255 lung-derived gene expressions between sarcoidosis and TB, particularly highlighting shared genes related to immune response, extracellular matrix organization, and cell migration16. Thus, our findings, employing an omics platform distinct from the aforementioned studies, substantiate the concept of a shared disease mechanism between sarcoidosis and TB. It was suggested that insoluble mycobacterial antigens could contribute to the development of sarcoidosis, supported by observations across several studies that a significantly higher number of sarcoidosis patients showed positive T-lymphocyte responses to Mtb antigens than healthy controls63. This, along with our current data, strengthens a previous postulate that sarcoidosis and TB might represent opposite ends of the same disease spectrum, to which HLA associations also contribute (e.g. HLADRB1*03/07/05 predisposes to sarcoidosis but potentially confers protection to TB, while the opposite may be true for HLADRB1*04)3,7,64.

Our study poses several limitations. Since we provide a single-time snapshot of serum proteomic profiles, we were unable to evaluate how the chronicity or duration of the diseases may influence the observed DEPs and pathway analyses. Additionally, we pre-selected proteins from the inflammation panel of the Olink® platform. Given the potential extrapulmonary dissemination of Mtb and the involvement of various organs in sarcoidosis, including the heart and brain4, it is possible that proteins specifically linked to these organs - which are not included in the inflammation panel - are present in the serum. However, as discussed above, the currently used pre-set inflammation panel is considerably sufficient to capture important proteins involved in active sarcoidosis and PTB and to describe major biological inflammatory processes that occur in both diseases. It is also important to note that the sample sizes among the groups (sarcoidosis and active pulmonary TB) were unequal. We acknowledge that this is a limitation in the study design. To assess the impact of sample size on our results, we down sampled the sarcoidosis group to 20 randomly selected individuals using the “caret” R package. After down sampling, five out of six proteins belonging to the BAFF signaling pathway remained significantly elevated among sarcoidosis patients (supplementary fig. 7 and supplementary table 4). Moreover, using a bead-based immunoassay we demonstrated elevated serum BAFF level in another thirty randomly selected sarcoidosis, which strongly supports the putative biological relevance of the BAFF signaling pathway in sarcoidosis. While suggestive, our findings need validation in subsequent independent larger cohorts, preferably with a balanced study design, to strengthen the robustness of our findings. Ideally, further studies should include both sarcoidosis and TB patients from the same geographical region to better assess the accuracy of biomarker candidates. In our analysis, we were unable to directly compare the sarcoidosis cohort with the TB cohort due to the fact that the cohorts originated from different populations, and the Olink® proteomics analysis was conducted in a separate run for each disease with its respective healthy control group. Despite this limitation, we successfully validated our discovery analysis for several different molecules by the use of other immunoassays, which supports the validity and analytical approach we used for comparing the extensive serum proteomes between sarcoidosis and TB.

In conclusion, we conducted an extensive serum proteomic analysis of sarcoidosis and TB patients, with an additional focus on disease associated uveitis. Our data provide compelling evidence that, despite shared pathobiological pathways between sarcoidosis and TB, B-lymphocyte activation is more prominently associated with sarcoidosis. This is reflected by a serum B-lymphocyte activation signature (elevated BAFF, TNFRSF13B/TACI, TRAF2, IKBKG, MAPK9, NFATC1, and DAPP1/Bam32) in sarcoidosis as well as increased B-lymphocyte numbers surrounding sarcoid granulomas. Besides this B-lymphocyte activation signature, we identified additional serum proteins (i.e., CCL17, IL-16, FGF2, and GZMB) that may hold promise as potential serum biomarkers to discriminate OS from OTB, addressing a current challenge in daily clinical practice. Further studies unraveling the role of B-lymphocytes in sarcoidosis and TB as well as evaluating the diagnostic utility of here identified serum proteins to discriminate between sarcoidosis/OS and PTB/OTB are warranted.

Methods

Ethical considerations

This study was performed according to the Helsinki guidelines and was approved by the local medical ethics committees from Erasmus University Medical Center, Rotterdam, the Netherlands (MEC-2014-476, MEC-2020-0193 and MEC-2021-0251), and Faculty of Medicine, University of Indonesia, Jakarta, Indonesia (268/H2.F1/ETIK/2014). All ethical regulations relevant to human research participants were followed. Informed consent was obtained from all included patients.

Patients recruitment

We included patients with established sarcoidosis diagnosis, with and without uveitis65,66, as well as patients with clinically active pulmonary TB with and without uveitis. To achieve this, we included two distinct cohorts from geographically separated regions. It is important to note that in our previous study with one-year recruitment period of newly diagnosed uveitis cases in Indonesia there were no biopsy-proven cases of OS. In that cohort, 12 patients (8% of all uveitis cases) were diagnosed as OTB, with the concomitant presence of uveitis with active pulmonary TB67. These 12 patients, along with another 10 active pulmonary TB patients without uveitis recruited during that study period, were further included in this study. On the other hand, a diagnosis of OTB in the Netherlands was mostly presumptive based on a positive IGRA results and exclusion of other potential etiologies in the context of active uveitis68. Of note, OS was prevalent, accounting for 13.7% of all uveitis cases in the Netherlands69. Therefore, reflecting the distinct prevalence of geographically pecific diseases, we recruited sarcoidosis patients from the Netherlands, while active TB patients were sourced from Indonesia. For comparison, healthy controls from the Netherlands and Indonesia were included.

Sarcoidosis patients

Serum samples from 90 sarcoidosis patients with systemic manifestations were included in this study, 44 without uveitis (systemic sarcoidosis: SS) and 46 with uveitis (systemic plus ocular sarcoidosis: S + OS). These were retrieved from the local biobank (IDRIA). Sarcoidosis patients were recruited between January 2017 and March 2019 at the outpatient clinic of the Department of Internal Medicine, section of Allergy & Clinical Immunology, Erasmus University Medical Center, Rotterdam, the Netherlands. The diagnosis of sarcoidosis was made by the attending clinical immunologist based on clinical presentation suggestive of sarcoidosis together with either (1) histopathological proof of non-caseating granuloma from biopsy specimen or (2) combination of clinical presentation and diagnostic-workup results (including chest radiograph/CT-scan, serum ACE and serum sIL-2R)65,66. Out of 90 sarcoidosis patients included in this study, the diagnosis of sarcoidosis was supported by a biopsy showing granulomatous inflammation suggestive for sarcoidosis in 75 patients (75/90, 83.3%). Only one sarcoidosis patient had positive IGRA, but the sarcoidosis diagnosis was made on histopathological features matching sarcoidosis rather than TB and supported by the absence of other signs of active or previous systemic TB, meeting the diagnostic criteria for definitive sarcoidosis65.

OS diagnosis was made with an agreement between the attending uveitis specialist and clinical immunologist. Depending on the anatomical site of uveitis, workup tests were performed, which included, but were not limited to, erythrocyte sedimentation rate, QuantiFERON‐TB Gold test (QFT), and syphilis screening tests. When indicated, ocular fluid analysis was performed to screen for infectious pathogens through PCR and Goldmann–Witmer coefficient (GWC). None of the included OS cases concomitantly had another infectious cause.

Active pulmonary TB patients

Serum samples from active pulmonary TB patients (n = 22) used for this study were from a previously described prospective cohort study from Cipto Mangunkusumo General Hospital, Jakarta, Indonesia67. Pulmonary TB diagnosis was made by a TB-expert pulmonologist based on the presence of either positive AFB sputum smear or active TB lesion from chest radiograph according to the Indonesian Society of Respirology TB guideline along with suggestive clinical symptoms67. In this study, 10 active pulmonary TB patients without uveitis (pulmonary tuberculosis: PTB) were AFB sputum smear positive. Uveitis workup included, but was not limited to, erythrocyte sedimentation rate, QFT, and syphilis screening tests. OTB diagnosis was made by a uveitis specialist based on the presence of active uveitis occurring concurrently with active pulmonary TB disease upon workup and consultation with an appointed pulmonologist. From the 12 active pulmonary TB patients with uveitis (pulmonary plus ocular tuberculosis: P + OTB), only two patients had positive AFB sputum smears. The diagnosis of active pulmonary TB in the other 10 cases with uveitis was established based on clinical presentation and radiological examinations showing active pulmonary TB lesions.

Patients’ workup

All patients underwent a systematic workup as part of the routine diagnostic process at baseline presentation. Complete blood counts, erythrocyte sedimentation rate, syphilis serology (TPHA and VDRL), chest X-ray, and QFT tests were performed for all patients. None of the included patients tested positive for syphilis serology or HIV. Among sarcoidosis uveitis patients, two underwent ocular fluid analysis for PCR testing for herpes simplex virus (HSV), Varicella Zoster Virus (VZV), Epstein-Barr virus, Rubella Virus, and Cytomegalovirus (CMV), along with Goldmann-Witmer coefficient calculation, and neither tested positive for these viruses. Among uveitis patients with active pulmonary TB, five underwent ocular fluid PCR for multiple pathogens (Mtb, CMV, VZV, HSV, Rubella, and Toxoplasma gondii). One patient tested positive for Mtb PCR, and none tested positive for the other pathogens. All patients were consulted by the Department of Internal Medicine (Internist-Immunologist and or pulmonologist) at each study site. None of the included patients had coexisting autoimmune or autoinflammatory diseases upon thorough clinical and laboratory examination.

Healthy controls

Since patient cohorts were from two geographical distinct regions, we included two separate geographically matched healthy controls (HC) cohorts for this study. The first HC cohort consisted of 23 individuals from the Netherlands (Dutch healthy controls; DHC). The second HC cohort consisted of 22 individuals from Indonesia (Indonesian healthy controls; IHC). HC from both cohorts had no history of uveitis, sarcoidosis, TB or other immunological disease and did not use any type of medication. Additionally, all IHC were QFT negative.

Serum samples processing

Preparation and storage

Serum samples from all individuals from both the Netherlands and Indonesia were isolated from peripheral venous blood within 3 h after blood draw. In brief, sera were obtained by centrifugation at 3000 ×g for 10 minutes, aliquoted (1 mL) into 1.5 mL microcentrifuge tubes and stored at −80 °C, until use. Serum samples from the Indonesian cohort were transported to and arrived in the Netherlands in frozen condition. Sample transfer adhered to the material transfer agreement between the two involved institutions.

Targeted proteomics assay: discovery step

Measurement of serum levels of 368 proteins related to inflammation was conducted by Olink (Boston, USA) with the Olink® Explore Inflammation panel (list of measured proteins within inflammation panel: https://olink.com/resources-support/document-download-center/olink-explore-3072-assay-list-2023-06-08/). The normalized protein expression (NPX) values provided by Olink® serve as surrogate marker for protein abundance and were used for further analyses. Proteins detected in more than 25% of all samples were included for further analyses, resulting in the inclusion of 357 out of 368 proteins from the Olink® inflammation panel. Data values below the limit of detection of included proteins were replaced by fixed value (zero) as suggested by the manufacturer. One SS patient failed quality control for all 368 proteins and was excluded from further analysis. The proteomic analysis between sarcoidosis versus DHC and active pulmonary TB versus IHC was performed independently as the measurement of all included serum samples were conducted in two separate runs. Hence, for the purpose of direct comparison analysis between disease (case) and control groups, the sarcoidosis cohorts were only directly compared to DHC. A similar approach was applied for TB, with TB cohorts being compared to IHC. Statistical comparisons between groups were made using a Mann-Whitney U test with multiple testing corrections using the Benjamini–Hochberg (B–H) procedure, also known as false discovery rate (FDR) correction. An FDR-adjusted p-value p < 0.05 was considered statistically significant. Statistical analysis was performed in R (v4.2.2, R Core Team 2021).

Luminex and ELISA assays: verification step

Proteins selected for further verification from the proteomic discovery experiments were measured in the included cohorts using a multiplex approach (Luminex assay; R&D Systems, Abingdon, UK) in a single run. This approach enabled a direct comparative measurement between the Dutch (sarcoidosis and DHC) and Indonesian (TB and IHC) cohorts in a single run, which was not applicable during the discovery phase of the proteomic analysis. Protein selection comprised disease-specific biomarker candidates found in the discovery step for which Luminex assays were commercially available. These included APRIL/TNFSF13, BAFF/TNFSF13B, CCL11/Eotaxin, FGF basic/FGF2/bFGF, Fas Ligand/TNFSF6, Granzyme B (GZMB), IL-16, IL-17C, TRAIL/TNFSF10, and TNFRSF13B/TACI. The protein AMBN was measured with enzyme-linked immunosorbent assay (ELISA; LSBio, USA). Due to the limited amount of remaining samples from the Indonesian cohort, only nine PTB and nine P + OTB could be analyzed with the Luminex assay. We also randomly selected 15 individuals from the SS, S + OS, and HC groups to balance the sample size of each cohort of this verification step by using a computerized randomization tool (https://www.random.org/lists/) for sarcoidosis cohorts (SS and S + OS). Furthermore, we proceeded with the remaining 13 TB samples (four PTB and nine P + OTB), for which we randomly selected 13 S + OS and 13 SS to compare for AMBN measurement. Assays were performed according to manufacturers’ instructions. Statistical comparisons between groups were made using a Kruskal-Wallis one-way test followed by post-hoc test with B-H corrected p-values for multiple comparison testing. All the graphs and statistical analyses were performed using GraphPad Prism software version 9.0.0 for Windows (GraphPad Software, San Diego, CA, USA).

Ingenuity pathway analysis (IPA)

QIAGEN’s Ingenuity® Pathway Analysis (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity) software was used for canonical pathway and upstream regulator analyses using the DEPs identified in our study. The B-H correction method was used to adjust both canonical pathway and upstream regulator p-values and 0.05 was set as threshold for significance. A comparison analysis in IPA between datasets: sarcoidosis (SS and S + OS) versus DHC and active pulmonary TB (PTB and P + OTB) versus IHC was also performed. Identified significant pathways with activation z-scores >2 or < −2 were then hierarchically sorted.

We also retrieved and compared our identified biological pathways and upstream regulators from our sarcoidosis and TB proteomic data with our previous RNA-seq dataset of Mtb-infected retinal pigment epithelial (RPE) cells (DESEQ2, adjusted p-value < 0.01, log2 fold change cut-off 1.5)32, as well with publicly available curated RNA-seq datasets in IPA® derived from Gene Expression Omnibus (GEO) repository (www.ncbi.nlm.nih.gov/geo/). GEO accession number GSE 83456 was used for comparison as this dataset contained whole blood transcriptomic analysis for sarcoidosis (n = 49) versus healthy controls and TB (extrapulmonary, n = 47, and pulmonary, n = 45) versus healthy controls from a single cohort of the UK population70. In addition, we also used analysis match feature in IPA® to provide additional bioinformatics validation of the canonical pathways and upstream regulators we identified. We highlighted a comparison between our analyzed sarcoidosis and TB datasets and relevant publicly available transcriptomic datasets sourced from in vitro experiments with overall level of agreement of more than 50% with our observations.

Immunofluorescence image analysis of sarcoidosis and TB granulomas

Tissue slides (4 μm) from granuloma containing biopsies from six individuals with sarcoidosis and six with TB were kindly provided by dr. J.H. von der Thüsen (Department of Pathology, Erasmus MC, Rotterdam, the Netherlands). Biopsies from from all sarcoidosis cases were from lymph nodes, while for TB, three biopsies were from the intestine, one from a lymph node, one from the skin, and one from pleural tissue. Biopsy sections were stained with FITC-labeled CD20 (mouse monoclonal anti-human CD20 clone L26; Ventana-Roche, Tuscon, USA) and DAPI. Granulomas and their adjacent surrounding structures were annotated. A total of 10 annotated areas per slide were further analyzed. Hematoxylin and eosin (HE) staining were used as a reference slide for accurate granuloma annotation. The ZEISS Axio Imager 2.0 fluorescence microscope with ×40 magnification was used to scan the processed slides. QuPath (Quantitative Pathology and Bioimage analysis software) version 0.4.3 (41) was used for image analysis. The specific region of interest (ROI) was marked by manual annotations. Nuclei identification via the DAPI signal was performed using the ‘Cell detection’ command, utilizing consistent settings across all slides and batches. For the identification and quantification of CD20 fluorescently labeled cells, QuPath’s simple threshold method was set and applied per tissue slide. A classifier was created concurrently incorporating DAPI and the CD20-immunofluorescence channel. This configuration was executed simultaneously prior to protein count data extraction. Positive cell counts were normalized based on the total cells detected per annotated area. Statistical analysis was performed using GraphPad Prism version 9.0.0.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Peer Review File

Supplementary Information

Description of Additional Supplementary File

Supplementary Data 1

Reporting Summary

Supplementary information

The online version contains supplementary material available at 10.1038/s42003-024-06822-1.

Acknowledgements

I.P. and R.L.D.N. are supported by Riset Inovatif Produktif—Lembaga Pengelola Dana Pendidikan (RISPRO-LPDP) [RISPRO/KI/B1/KOM/5/15219/4/2020]. The funding source had no involvement in the collection, analysis, interpretation, writing of the report, and the decision to submit the article for publication.

Author contributions

W.A.D. designed the study. I.P., B.S. and P.M.K. performed the initial analysis of serum proteomics. I.P. and W.A.D. wrote the initial draft of the manuscript. A.C.vS. performed microscopy slides analysis and interpretation. J.C.E.M.tB., S.M.R., J.A.M.vL., R.L.D.N. and P.M.vH. involved in patients characterization and recruitment. Protein verification was performed by I.P. and N.M.A.N. Primary statistical analysis was performed by I.P., P.M.K., and S.M.A.S. All authors, I.P., B.S., P.M.K., A.C.vS., J.C.E.M.tB., H.I., N.M.A.N., S.M.A.S., J.A.M.vL., R.A., S.M.R., P.M.vH., R.L.D.N., and W.A.D., contributed significantly in data analysis and interpretation, as well as review and editing process of the manuscript. All authors approved the final version of the submitted manuscript.

Peer review

Peer review information

Communications Biology thanks Darragh Duffy and Edward Chen for their contribution to the peer review of this work. Primary Handling Editor: Christina Karlsson Rosenthal. A peer review file is available.

Data availability

The authors declare that all generated data supporting the findings of this study are available within the paper as supplementary information files. Numerical source data can be found in Supplementary Data 1. Other RNA-seq data used for pathway analysis in IPA® are available from GSE 8345670 and supplementary file of the previous publication32. The serum proteomics data of all sarcoidosis and active pulmonary TB patients were deposited into the figshare repository, as part of this record: https://figshare.com/s/c8128b5610fed0023da7. Additional relevant data not mentioned here is available upon specific request.

Competing interests

The authors declare no competing interest.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Ikhwanuliman Putera, Benjamin Schrijver, P. Martijn Kolijn.
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