Skip to main content
Advertisement
Browse Subject Areas
?

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

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Peripheral-blood b-cell subset disturbances in inflammatory joint diseases induced by Tropheryma whipplei

  • Maëlle Le Goff,

    Roles Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Victor Hugo Network, Brest Teaching Hospital, Brest, France

  • Divi Cornec,

    Roles Conceptualization, Investigation, Methodology, Validation, Visualization, Writing – review & editing

    Affiliations Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Victor Hugo Network, Brest Teaching Hospital, Brest, France, UMR1227, Lymphocytes B et Autoimmunité, Brest University, Inserm, Brest Teaching Hospital, LabEx IGO, Brest, France

  • Dewi Guellec,

    Roles Conceptualization, Investigation, Validation, Visualization, Writing – review & editing

    Affiliation Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Victor Hugo Network, Brest Teaching Hospital, Brest, France

  • Thierry Marhadour,

    Roles Conceptualization, Investigation, Methodology, Validation, Visualization, Writing – review & editing

    Affiliation Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Victor Hugo Network, Brest Teaching Hospital, Brest, France

  • Valérie Devauchelle-Pensec,

    Roles Formal analysis, Investigation, Validation, Visualization, Writing – review & editing

    Affiliations Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Victor Hugo Network, Brest Teaching Hospital, Brest, France, UMR1227, Lymphocytes B et Autoimmunité, Brest University, Inserm, Brest Teaching Hospital, LabEx IGO, Brest, France

  • Sandrine Jousse-Joulin,

    Roles Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – review & editing

    Affiliations Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Victor Hugo Network, Brest Teaching Hospital, Brest, France, UMR1227, Lymphocytes B et Autoimmunité, Brest University, Inserm, Brest Teaching Hospital, LabEx IGO, Brest, France

  • Marion Herbette,

    Roles Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing – review & editing

    Affiliation Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Victor Hugo Network, Brest Teaching Hospital, Brest, France

  • Jean Michel Cauvin,

    Roles Formal analysis, Methodology, Software, Validation, Visualization, Writing – review & editing

    Affiliation DIM and CDC, La Cavale Blanche Hospital, Brest, France

  • Clara Le Guillou,

    Roles Formal analysis, Methodology, Software, Validation, Visualization, Writing – review & editing

    Affiliation DIM and CDC, La Cavale Blanche Hospital, Brest, France

  • Yves Renaudineau,

    Roles Conceptualization, Investigation, Methodology, Validation, Writing – review & editing

    Affiliation UMR1227, Lymphocytes B et Autoimmunité, Brest University, Inserm, Brest Teaching Hospital, LabEx IGO, Brest, France

  • Christophe Jamin,

    Roles Investigation, Methodology, Software, Validation, Writing – review & editing

    Affiliation UMR1227, Lymphocytes B et Autoimmunité, Brest University, Inserm, Brest Teaching Hospital, LabEx IGO, Brest, France

  • Jacques Olivier Pers,

    Roles Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing – review & editing

    Affiliation UMR1227, Lymphocytes B et Autoimmunité, Brest University, Inserm, Brest Teaching Hospital, LabEx IGO, Brest, France

  • Alain Saraux

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    alain.saraux@chu-brest.fr

    Affiliations Rheumatology Unit, Centre National de Référence des Maladies Auto-Immunes Rares (CERAINO), Victor Hugo Network, Brest Teaching Hospital, Brest, France, UMR1227, Lymphocytes B et Autoimmunité, Brest University, Inserm, Brest Teaching Hospital, LabEx IGO, Brest, France

Abstract

Objective

To look for abnormalities in circulating B-cell subsets in patients with rheumatic symptoms of Whipple’s disease (WD).

Method

Consecutive patients seen between 2010 and 2016 for suspected inflammatory joint disease were identified retrospectively. Results of standardized immunological and serological tests and of peripheral-blood B-cell and T-cell subset analysis by flow cytometry were collected. Patients with criteria suggesting WD underwent PCR testing for Tropheryma whipplei, and those with diagnosis of WD (cases) were compared to those without diagnosis (controls). We used ROC curve analysis to evaluate the diagnostic value of flow cytometry findings for WD.

Results

Among 2917 patients seen for suspected inflammatory joint disease, 121 had suspected WD, including 9 (9/121, 7.4%) confirmed WD. Proportions of T cells and NK cells were similar between suspected and confirmed WD, whereas cases had a lower proportion of circulating memory B cells (IgD-CD38low, 18.0%±9.7% vs. 26.0%±14.2%, P = 0.041) and higher ratio of activated B cells over memory B cells (4.4±2.0 vs. 2.9±2.2, P = 0.023). Among peripheral-blood B-cells, the proportion of IgD+CD27- naive B cells was higher (66.2%±18.2% vs. 54.6%±18.4%, P = 0.047) and that of IgD-CD27+ switched memory B cells lower (13.3%±5.7% vs. 21.4%±11.9%, P = 0.023), in cases vs. controls. The criterion with the best diagnostic performance was a proportion of IgD+CD27- naive B cells above 70.5%, which had 73% sensitivity and 80% specificity.

Conclusion

Our study provides data on peripheral-blood B-cell disturbances that may have implications for the diagnosis and pathogenetic understanding of WD.

Introduction

Whipple’s disease (WD) is a rare, systemic, disease caused by the intracellular Gram-positive bacterium Tropheryma whipplei (TW). This ubiquitous commensal organism [1] is transmitted among humans via the oro-fecal route [2,3]. WD was first described in 1907. TW was identified by polymerase chain reaction (PCR) in small-bowel biopsies from patients with WD [47] in 1991 and later in various samples including stool, saliva, and joint fluid [8, 9]. T. whipplei is extraordinarily difficult and slow to grow in cultures. The prevalence of TW carriage is highest in adults, residents of rural areas, and exposed individuals such as homeless people and sewer workers [2, 10]. In apparently healthy individuals, the prevalence of carriers identified by PCR screening of stool and saliva was 1.5% to 7.0% and 0.2% to 1.5%, respectively [1113].

The clinical spectrum of TW infection [1418] includes classical WD, localized WD [19], acute infection [20], asymptomatic infection, WD influenced by immunosuppression [21], and T. whipplei-associated arthritis defined as chronic arthritis with a negative duodenal biopsy but positive PCR test at a non-articular site [22]. The non-specific clinical presentation and disease incidence that is too low to warrant routine PCR screening result in major diagnostic challenges. Thus, several years often elapse between symptom onset and the diagnosis [14] of this treatable disease [15]. The reference standard for diagnosing classical WD is duodenal biopsy testing by PCR and periodic acid-Schiff- (PAS) staining [23]. In many of the other forms, the diagnosis relies on PCR testing of saliva, stool, and/or joint fluid, which has a good positive predictive value [8, 11].

Chronic WD and the immune system are closely linked. A contributor to the pathogenesis of WD is the alternatively activated macrophage phenotype, which predominates in the duodenal mucosa and leads to persistent infection by making the macrophages unable to degrade TW [24, 25]. Impaired interleukin (IL)-12 production [26, 27] responsible for decreased IFN-γ production by NK and T cells has been reported in WD [28]. Regulatory T cells are involved in the pathogenesis of WD [29]. Deficiencies in specific peripheral and mucosal T helper cell type 1 (Th1) responses to TW have been reported in patients with WD [30]. The HLA DRB1*13 and DQB1*06 may confer susceptibility to WD [31]. Finally, immunosuppressive therapy may shorten the time from symptom onset to systemic chronic WD, and immunosuppressive therapy in patients with WD may increase the risk of immune reconstitution inflammatory syndrome [32].

To our knowledge, no studies have evaluated the potential role for B cells in WD. Technological advances have improved the phenotypic characterization of blood cells, and flow cytometry is now widely used in patients with hematological, infectious, and systemic auto-immune diseases [3334]. Abnormalities in the peripheral-blood B-cell subset profile were observed in systemic auto-immune diseases such as primary Sjögren’s syndrome in which the ratio of activated B cells over memory B cells ratio is increased [35] and might serve as a diagnostic aid. We noticed lymphocyte subset abnormalities similar to those seen in primary Sjögren’s syndrome in patients whose symptoms suggested ankylosing spondyloarthritis (inflammatory low back pain) or rheumatoid arthritis (chronic polyarthritis). We then observed the same abnormalities in patients with infectious rheumatic diseases due in particular to Bartonella (cat-scratch disease) or TW.

We therefore designed the present study with the aim of describing peripheral-blood lymphocyte subsets, with special attention to B cells, in patients with WD, with rheumatic symptoms. We aimed to assess whether any abnormalities found were sufficiently characteristic to help in diagnosing and monitoring WD.

Patients and methods

Participants

We retrospectively collected data on consecutive patients seen at our rheumatology department between April 2010 and December 2016 for suspected inflammatory joint disease. All patients underwent immunological and serological tests, and a peripheral-blood flow cytometry assessment of lymphocyte subsets (total T cells, NK cells, and CD19+ B cells) and B-cell subsets (CD19+IgD+CD38hi, transitional, CD19+IgD+CD27-, naive, CD19+IgD+CD27+, unswitched memory, and CD19+IgD-CD27+ switched memory B cells).

Ethics statement

This study was approved by the CPP Ouest IV ethics committee (2017. CE19). According to the ethics committee recommendations, all data were fully anonymized for analysis and rheumatologists signed a written document which confirmed that all patients received information and were not opposed to the use of their data for this study (non opposition form).

Identifications of patients with suspected (controls) and confirmed (cases) Whipple’s disease

Within the population, we identified the subgroup of patients (n = 121) who underwent PCR, systematically in stool and saliva, and depending of the symptoms in joint fluid, blood, duodenum, Cerebro Spinal Fluid (CSF), testing for TW. Within this subgroup, we compared the patients with definite diagnosis (cases) vs. no diagnosis (controls) of WD. All cases had at least one clinical criterion suggesting WD, at least one positive PCR test for TW, an antibiotic therapy response recorded by the physician as dramatic and including normalization of C reactive protein and a confirmation of the diagnosis based on all data (exclusion of differential diagnosis) and more than one year of follow up by an independent group of physicians. The cases were divided into three groups depending on whether they had classical WD, focal WD, or chronic TW-associated arthritis (CTWA). Classical WD was defined as a duodenal biopsy positive by PAS staining or TW immunohistochemistry, or as both stool and saliva positive by PCR plus a positive skin biopsy, or as blood positive by PCR. Focal WD was defined as joint fluid positive by PCR but duodenal biopsy negative by PAS staining and immunohistochemistry. CTWA was chronic arthritis plus duodenal biopsy, stool, or saliva positive by PCR but duodenal biopsy negative by PAS staining or immunohistochemistry and joint fluid negative by PCR (non-classical WD) [22].

Lymphocyte subset analyses

Flow cytometry was used to assess the distributions of CD4+ and CD8+ T cells, NK cells, and total CD19+ B cells(33). All antibodies were purchased from Beckman-Coulter (Hialeah, FL). Phycoerythrin (PE)-cyanine 7 (PC7)-conjugated anti-CD19 monoclonal antibody (mAb) (J4;119) was used to tag B cells; and fluorescein isothiocyanate-conjugated anti-IgD (IA6-2), PE-conjugated anti-CD27 (LS198), and PC5-conjugated anti-CD38 (LS198) mAbs to distinguish among B-cell subsets [36]. In a second B-cell panel, anti-CD19 and anti-CD38 mAbs were combined with PE-conjugated anti-CD24 (ALB9) mAb to identify CD19+CD38hiCD24hi transitional and CD19+CD24+CD38+ mature B cells. The cells were categorized on an Epics XL (Beckman-Coulter) fluorescence-activated cell-sorter (FACS). All details are summarized in the Fig 1 and Figure A in S1 File.

thumbnail
Fig 1. Gating strategy used for fluorescence-activated cell-sorter (FACS).

The % of CD3 (CD3+CD19-), the % of CD4 (CD3+CD4+), the % of CD8 (CD3+CD8+) and the % of CD19 (CD3-CD19+) subsets were determined within the total lymphocyte population (gate A = Lympho). For the B cell subsets, the % of CD24++CD38++ transitional B cells and the % of CD24+CD38+ naïve B cells were determined within the total lymphocyte population (gate A = Lympho). The % of IgD+CD27- naïve B cells, the % of IgD+CD27+ unswitched memory B cells and the % of IgD-CD27+ switched memory B cells, and the % of IgD+CD38+/hi activated B cells and the % of IgD-CD38+/low memory B cells were determined within the B cell subset (gate F = CD19+). For the NK cell subsets, the % of CD3-CD56+ NK lymphocytes and the % CD3+CD56+ NK-like lymphocytes were determined within the total lymphocyte population (gate A = Lympho). The % of CD3-CD16++CD56+ naïve cytotoxic NK lymphocytes and the % CD3-CD16+CD56++ active NK lymphocytes were determined within the NK lymphocyte population (gate O4 = CD3+CD56+).

https://doi.org/10.1371/journal.pone.0211536.g001

PCR tests and biopsy

The 121 patients with suspected WD had 214 visits and underwent at least one PCR test for TW on a variety of samples (stool; saliva; joint fluid; blood; cerebrospinal fluid; urine; and/or biopsies of lung, skin, and/or duodenal mucosa). Real-time quantitative PCR (qPCR) tests for repeated TW sequences were performed using specific oligonucleotide TaqMan probes(11) at the bacteriology laboratory of the Marseille teaching hospital [37]. Sequencing was performed when an amplified product was detected, followed by a confirmatory PCR test targeting a different TW sequence. Positive and negative controls were used routinely, and the quality of extracted DNA was checked by human actin gene detection [38].

Statistical analysis

The data were analyzed using the Statistical Package for the Social Sciences (SPSS 25.0, Chicago, IL). Absolute values were described as mean±SD (of number of cells by mm3 or percentages) of circulating lymphocyte subsets. Associations between lymphocyte subset distributions and WD were assessed by univariate analysis using Mann-Whitney test (comparison of continuous data) or Wilcoxon test (comparison of continuous data before and after treatment). Logistic regression was performed to identify the subset most strongly associated with WD. Subset distribution changes over time were then evaluated. p values smaller than 0.05 were considered significant. Receiver operating characteristic (ROC) curves were plotted for B-cell subsets at any time point to identify the cutoff associated with the best compromise between sensitivity and specificity.

Results

Patient population

Between April 2010 and December 2016, 2917 patients had 3515 visits to our rheumatology department for symptoms suggesting inflammatory rheumatism. Among them, 121 with suspected WD underwent 214 PCR tests for TW. There were 62 (51.2%) men and 59 (48.8%) women with a mean age of 52.5±15 years (range, 16–84 years). Of these 121 patients, 9 had positive PCR tests for TW and were diagnosed with WD: 1 (11.1%) had classical WD, 4 (44.4%) focal WD, and 4 (44.4%) non-classical WD. Of the 112 other patients, 58 received diagnoses of rheumatoid arthritis (n = 24), spondyloarthritis (n = 17), connective tissue disease (n = 4), vasculitis (n = 6), sarcoidosis (n = 3), or other diseases (Lyme disease, sarcoma, or polymyalgia rheumatica, n = 4) and 54 had no diagnosis.

Detailed features of the 9 patients diagnosed with Whipple’s disease (WD) (Figure B in S1 File)

There were 7 (77.8%) males and 2 (22.2%) females with a mean age of 60.3±11.4 years and a mean symptom duration of 8.5±7 years. Among them, 8 had previously received a diagnosis of rheumatoid arthritis (n = 4, 44.4%), leukocytoclastic vasculitis (n = 1, 11.1%), undifferentiated arthritis (n = 1, 11.1%), spondyloarthritis (n = 1, 11.1%), or calcium pyrophosphate dihydrate deposition disease (n = 1, 11.1%). In the patient with no previous diagnosis, time since symptom onset was only 2 years. In 5 (55.5%) patients, there was a history of treatment with synthetic disease-modifying antirheumatic drugs or TNFα antagonists. Mean serum C-reactive protein was 36.5±24.3 mg/L (range, 0.4–82.9 mg/L), 3 patients had anemia, and 5 patients had hypoalbuminemia.

All 9 patients received first-line hydroxychloroquine (400–600 mg/day) and doxycycline (200 mg/day) treatment for WD, which was consistently effective in resolving the clinical and laboratory abnormalities, with a response time of 10 days to 2 months. The doxycycline was switched to intravenous ceftriaxone after 1 month in 1 patient and to trimethoprim-sulfamethoxazole because of an allergic reaction in another.

Of the 9 patients, 2 are still receiving treatment at the time of writing.

Lymphocyte subsets in the 9 patients with Whipple’s disease and in the controls

Table 1 details the peripheral-blood lymphocyte subsets in each patient at baseline. Subset distributions were compared between the group of 9 patients with WD disease and the control group of 112 patients seen for suspected inflammatory joint disease and having a suspicion of WD but negative PCR tests for TW (Table 2). The proportions of total lymphocytes, CD4+ T cells, CD8 + T cells, and NK cells were not different between cases and controls. The percentage of circulating IgD-CD38-/low memory B cells was significantly lower, and the ratio of IgD+CD38+/hi activated B cells over IgD-CD38-/low memory B cells significantly higher in cases compared with controls (4.4±2.0 vs. 2.9±2.2, P = 0.023). Studying CD27 expression showed that the cases had a higher proportion of IgD+CD27- naive B cells (66.2%±18.2% vs. 54.6%±18.4%, P = 0.047) and, among memory B cells, a lower proportion of IgD-CD27+ switched memory cells (13.3%±5.7% vs. 21.4%±11.9%, P = 0.023) vs. controls.

thumbnail
Table 1. Peripheral-blood B-lymphocyte subsets at baseline of the 9 patients diagnosed with Whipple’s disease (WD).

https://doi.org/10.1371/journal.pone.0211536.t001

thumbnail
Table 2. Peripheral-blood B-cell subsets (mean±SD of the number by mm3 and %) in patients with Whipple’s disease, compared to controls with inflammatory diseases but no Whipple’s disease among patient who had a suspicion of Whipple’s disease (at least one PCR).

https://doi.org/10.1371/journal.pone.0211536.t002

We compared the 22 visits by the 9 patients with WD to the 3493 visits by the 2908 patients without PCR testing for TW or with negative PCR testing for TW (Table 3). The differences between the two groups were similar to those found at baseline. In addition, the lymphocyte counts were lower in the patients with WD than without a diagnosis of WD.

thumbnail
Table 3. Peripheral-blood B-cell subsets (mean±SD) in patients with Whipple’s disease compared to controls with inflammatory disease but negative PCR tests for Whipple’s disease, or without PCR tests for WD.

https://doi.org/10.1371/journal.pone.0211536.t003

Some patients with WD were receiving glucocorticoid (3 patients), methotrexate (5 patients) or TNFα antagonist (2 patients) therapy. These treatments may modify B-cell subset distribution. However, in the controls, patients were taking the same treatments (20 received glucocorticoids, 19 methotrexate, and 7 a biologic).

Changes in lymphocyte subset distribution in the 9 patients during treatment for Whipple’s disease (WD)

Flow cytometry was performed before the diagnosis (i.e., probably after disease onset, as symptoms were present), at the diagnosis of WD, and during treatment in the 9 patients with WD. No changes occurred in lymphocyte subset distribution (Table 4). Four patients had an evaluation at the end of the treatment and there was not changes (Figure C in S1 File: example on IgD-CD27+ switched memory B cells) despite the end of treatment by methotrexate (3 patients), TNF inhibitor (1 patient) and steroids (2 patients).

thumbnail
Table 4. Comparison of peripheral-blood B-cell subsets (mean of %) before and after starting treatment for Whipple’s disease in 9 patients.

https://doi.org/10.1371/journal.pone.0211536.t004

Relevance of the B-cell subset distribution to the diagnosis of Whipple’s disease (WD)

Full B-cell distribution was evaluated at 198 visits. ROC curve analysis showed that the proportion of IgD+CD27-naive B cells provided the best compromise between sensitivity and specificity for the diagnosis of WD. With a cutoff of 70.5%, the area under the curve was 0.83, sensitivity was 73.0%, and specificity was 80.0% (Fig 2). If we limit the controls to patients with autoimmune disease, results were quite similar (the area under the curve was 0.79).

thumbnail
Fig 2. ROC curves of the diagnostic performance of B-cell subset distribution for Whipple’s disease.

The best curve was obtained with IgD+CD27- naive B cells (%).

https://doi.org/10.1371/journal.pone.0211536.g002

Of 19 visits by patients with WD having all subpopulation evaluations, 14 (73.7%) were associated with a proportion of IgD+CD27-naive B cells at or above the cut-off, versus 35 (19.5%) of 179 visits by patients without WD (P<0.0001). Interestingly, the proportion of IgD+CD27-naive B cells was not elevated in patients with Lyme disease, reactive arthritis, or septic arthritis.

Discussion

In patients with WD and rheumatic symptoms, the distribution of peripheral-blood B-cell subsets differed from that in controls with inflammatory joint disease. No differences were found, in contrast, for total lymphocytes, T cells, or NK cells. The cases had lower proportions of circulating IgD-CD38-/low memory B cells and, most notably, of IgD-CD27+switched memory B cells. The ratio of IgD+CD38+/hi activated B cells over IgD-CD38-/low memory B cells was higher in the cases, because of a higher percentage of IgD+CD27- naive B cells. The best diagnostic performance was obtained for an IgD+CD27- naive B-cell proportion at or above 70.5%. The B-cell subset abnormalities documented in our study may provide diagnostic assistance, in combination with the medical history, physical findings, and standard laboratory tests. Furthermore, they may help to understand the pathophysiology of WD. In our population, patients with other infectious diseases did not have the B-cell subset abnormalities seen in the patients with WD.

WD is characterized by massive infiltration of TW in the duodenal mucosa, lack of duodenal inflammation, malfunction of antigen-presenting cells, and alternative activation of macrophages [24]. Dysregulation of T-cell functions are involved in the pathogenesis of WD. The proportion of CD4+ T cells in peripheral blood and the lamina propria is reduced, and both T-cell activation and the Th1 response are impaired, with diminished production of IL-2 and IFN-gamma [30]. These deficiencies allow the establishment of chronic TW infection in susceptible patients [31]. Regulatory T cells are abundant in the duodenal mucosa and exhibit enhanced activity in peripheral blood, leading to insufficient bacterial clearance [31]. However, a primary T cell defect does not appear to be the cause for chronic WD [29]. Our patients did not exhibit these previously described T-cell abnormalities; more specifically, they had no decrease in the proportion of CD4+ T cells [30].

B-cell abnormalities reported previously in WD include serological alterations and changes in duodenal mucosal B cells [3940]. Thus, WD is associated with the HLA-DRB1*13 and DQB1*06 genotypes [31], which may preferentially present antigenic epitopes to stimulate humoral responses instead of cellular immune responses. More interestingly, it was recently found that a single rare non-synonymous mutation with age-dependent incomplete penetrance leading to IRF4 deficiency which can underlie WD. As IRF4 help B-cell development, a genetic mechanism may explain why the lymphocyte subsets in our patients remained unchanged (despite antibiotic, infection by TW remains and induces altered kinds of B cells) [41]. Another hypothesis may be that the disease induced irreversible subset distribution abnormalities.

Our study has three main limitations. First, the number of patients was small. Second, at the time of flow cytometry, some patients were receiving or glucocorticoid (4 patients), methotrexate (4 patients) or TNFα antagonist (2 patients) therapy. These treatments are known to modify B-cell subset distribution [42, 43]. However, in the controls, patients were taking the same treatments (20 received glucocorticoids, 19 methotrexate, and 7 a biologic). Third, the controls were not healthy individuals but patients with inflammatory diseases such as Sjogren’s syndrome, which are known to be associated with alterations in peripheral-blood B-cell subset distribution [33, 35, 44].

To conclude, flow cytometry analysis of peripheral-blood lymphocytes in patients with rheumatic symptoms and a diagnosis of WD showed alterations in B-cell subset distribution compared to controls with inflammatory diseases, a clinical suspicion of WD, but negative PCR tests for WD. Treatment for WD consistently induced a clinical response but did not change the abnormalities in B-cell subset distribution. These abnormalities are not sufficiently characteristic to serve as a diagnostic tool when considered alone but may provide guidance when combined with other criteria. The B-cell subset abnormality associated with the best compromise between sensitivity and specificity for diagnosing WD was a proportion of IgD+CD27-naive B cells ≥70.5%. Our study provides the first data on peripheral-blood B-cell subset alterations in WD and may suggest hypotheses regarding the role for immunological abnormalities in this condition.

Supporting information

S1 File. Method for fluorescence-activated cell-sorter (FACS) (Figure A).

Features of the 9 patients diagnosed with Whipple’s disease (WD) (Figure B). Lack of changes of IgD-CD27+ switched memory B cells in 4 patients who had evaluation before treatment, under treatment and at the end of treatment (Figure C).

https://doi.org/10.1371/journal.pone.0211536.s001

(DOCX)

Acknowledgments

We thank the rheumatologists of the Victor Hugo Network.

References

  1. 1. La Scola B, Fenollar F, Fournier P-E, Altwegg M, Mallet M-N, Raoult D. Description of Tropheryma whipplei gen. nov., sp. nov., the Whipple’s disease bacillus. Int J Syst Evol Microbiol 2001; 51(4):1471–1479.
  2. 2. Keita AK, Raoult D, Fenollar F. Tropheryma whipplei as a commensal bacterium. Future Microbiol 2013; 8(1):57–71. pmid:23252493
  3. 3. Marth T, Moos V, Müller C, Biagi F, Schneider T. Tropheryma whipplei infection and Whipple’s disease. Lancet Infect Dis 2016; 16 (3): e13–22. pmid:26856775
  4. 4. Fenollar F, Raoult D. Molecular technique in Whipple’s disease. Expert Rev Mol Diagn 2001; 1(3):299–309. pmid:11901835
  5. 5. Relman David A., Schmidt Thomas M., MacDermott Richard P., Falkow Stanley. Identification of the uncultured bacillus of Whipple’s disease. N Engl J Med 1992; 327(5):293–301. pmid:1377787
  6. 6. Wilson KH, Frothingham R, Wilson JAP, Blitchington R. Phylogeny of the Whipple’s-disease-associated bacterium. Lancet 1991; 338(8765):474–475. pmid:1714530
  7. 7. Fenollar F, Fournier P-E, Raoult D, Gerolami R, Lepidi H, Poyart C. Quantitative Detection of Tropheryma whipplei DNA by Real-Time PCR. J Clin Microbiol 2002; 40(3):1119–20. pmid:11880458
  8. 8. Puéchal X, Fenollar F, Raoult D. Cultivation of Tropheryma whipplei from the synovial fluid in Whipple’s arthritis. Arthritis Rheum 2007; 56(5):1713–8. pmid:17469186
  9. 9. Qin S, Clausen E, Lucht L, Michael H, Beck JM, Curtis JL, et al. Presence of Tropheryma whipplei in different body sites in a cohort of healthy subjects. Am J Respir Crit Care Med 2016; 194(2):243–245. pmid:27420361
  10. 10. Fenollar F, Lagier J-C, Raoult D. Tropheryma whipplei and Whipple’s disease. J Infect 2014; 69(2):103–12. pmid:24877762
  11. 11. Fenollar F, Laouira S, Lepidi H, Rolain J, Raoult D. Value of Tropheryma whipplei quantitative polymerase chain reaction assay for the diagnosis of Whipple disease: usefulness of saliva and stool specimens for first-line screening. Clin Infect Dis 2008; 47(5):659–67. pmid:18662136
  12. 12. Fenollar F, Trani M, Davoust B, Salle B, Birg M, Rolain J, et al. Prevalence of asymptomatic Tropheryma whipplei carriage among humans and nonhuman primates. J Infect Dis 2008; 197(6):880–7. pmid:18419351
  13. 13. Amsler L, Bauernfeind P, Nigg C, Maibach RC, Steffen R, Altwegg M. Prevalence of Tropheryma whipplei DNA in patients with various gastrointestinal diseases and in healthy controls. Infection 2003; 31(2):81–5. pmid:12682812
  14. 14. Lagier J-C, Lepidi H, Raoult D, Fenollar F. Systemic Tropheryma whipplei: Clinical presentation of 142 patients with infections diagnosed or confirmed in a reference center. Medicine (Baltimore) 2010; 89(5):337–45.
  15. 15. El-Abassi R, Soliman MY, Williams F, England JD. Whipple’s disease. J Neurol Sci 2017; 377:197–206. pmid:28477696
  16. 16. Marth T. Whipple’s disease. Acta Clin Belg 2016; 71(6):373–8. pmid:27884091
  17. 17. Schneider T, Moos V, Loddenkemper C, Marth T, Fenollar F, Raoult D. Whipple’s disease: new aspects of pathogenesis and treatment. Lancet Infect Dis 2008; 8(3):179–190. pmid:18291339
  18. 18. Fenollar F, Puéchal X, Raoult D. Whipple’s Disease. N Engl J Med 2007; 356:55–66. pmid:17202456
  19. 19. Desmond O’Duffy J, Leroy Griffing W, Chin-Yang Li, Abdelmalek Manal F, Persing David H. Whipple’s arthritis: Direct detection of Tropheryma whippelii in synovial fluid and tissue. Arthritis Rheum. 1999; 42(4): 812–7. pmid:10211899
  20. 20. Lagier J-C, Fenollar F, Raoult D. Acute infections caused by Tropheryma whipplei. Future Microbiol. 2017; 12(3):247–54.
  21. 21. Marth T. Systematic review: Whipple’s disease (Tropheryma whipplei infection) and its unmasking by tumour necrosis factor inhibitors. Aliment Pharmacol Ther 2015; 41(8):709–24. pmid:25693648
  22. 22. Herbette M, Cren J, Joffres L, Lucas C, Ricard E, Salliot C, et al. Usefulness of polymerase chain reaction for diagnosing Whipple’s disease in rheumatology. PlosOne 2018; 13(7):
  23. 23. Günther U, Moos V, Offenmüller G, Oelkers G, Heise W, Moter A, et al. Gastrointestinal diagnosis of classical Whipple disease: Clinical, endoscopic, and histopathologic features in 191 patients. Medicine (Baltimore) 2015; 94(15): 714.
  24. 24. Moos V, Schmidt C, Geelhaar A, Kunkel D, Allers K, Schinnerling K, et al. Impaired immune functions of monocytes and macrophages in Whipple’s disease. Gastroenterology 2010;138(1):210–20. pmid:19664628
  25. 25. Desnues B, Lepidi H, Raoult D, Mege J-L. Whipple disease: intestinal infiltrating cells exhibit a transcriptional pattern of M2/alternatively activated macrophages. J Infect Dis 2005; 192(9):1642–1646. pmid:16206080
  26. 26. Kalt A, Schneider T, Ring S, Hoffmann J, Zeitz M, Stallmach A, et al. Decreased levels of interleukin-12p40 in the serum of patients with Whipple’s disease. Int J Colorectal Dis 2006;21(2):114–20. pmid:15875203
  27. 27. Marth T, Neurath M, Cuccherini BA, Strober W. Defects of monocyte interleukin 12 production and humoral immunity in Whipple’s disease. Gastroenterology 1997; 113(2):442–448. pmid:9247462
  28. 28. Marth T, Kleen N, Stallmach A, Ring S, Aziz S, Schmidt C, et al. Dysregulated peripheral and mucosal Th1/Th2 response in Whipple’s disease. Gastroenterology 2002;123(5):1468–77. pmid:12404221
  29. 29. Moos V, Schneider T. The role of T cells in the pathogenesis of classical Whipple’s disease. Expert Rev Anti Infect Ther 2012;10(3):253–5. pmid:22397556
  30. 30. Moos V, Kunkel D, Marth T, Feurle GE, LaScola B, Ignatius R, et al. Reduced peripheral and mucosal Tropheryma whipplei-specific Th1 response in patients with Whipple’s disease. J Immunol 2006;177(3):2015–22. pmid:16849516
  31. 31. Martinetti M, Biagi F, Badulli C, Feurle GE, Müller C, Moos V, et al. The HLA Alleles DRB1*13 and DQB1*06 are associated to Whipple’s disease. Gastroenterology 2009;136(7):2289–94. pmid:19208355
  32. 32. Feurle GE, Moos V, Schinnerling K, Geelhaar A, Allers K, Biagi F, et al. The immune reconstitution inflammatory syndrome in Whipple disease: A cohort study. Ann Intern Med 2010;153(11):710–717. pmid:21135294
  33. 33. Carvajal Alegria G, Gazeau P, Hillion S, Daïen CI, Cornec DYK. Could lymphocyte profiling be useful to diagnose systemic autoimmune diseases? Clin Rev Allergy Immunol. 2017; http://link.springer.com/10.1007/s12016-017-8608-5
  34. 34. Sack U, Boldt A, Mallouk N, Gruber R, Krenn V, Berger-Depincé A-E, et al. Cellular analyses in the monitoring of autoimmune diseases. Autoimmun Rev 2016;15(9):883–9. pmid:27392502
  35. 35. Binard A, Le Pottier L, Devauchelle-Pensec V, Saraux A, Youinou P, Pers J-O. Is the blood B-cell subset profile diagnostic for Sjogren syndrome? Ann Rheum Dis 2009;68(9):1447–52. pmid:18782791
  36. 36. Carvajal Alegria G, Devauchelle-Pensec V, Renaudineau Y, Saraux A, Pers J-O, Cornec D. Correction of abnormal B-cell subset distribution by interleukin-6 receptor blockade in polymyalgia rheumatica. Rheumatology 2017; 56(8):1401–6. pmid:28431111
  37. 37. Lagier J-C, Fenollar F, Lepidi H, Giorgi R, Million M, Raoult D. Treatment of classic Whipple’s disease: from in vitro results to clinical outcome. J Antimicrob Chemother 2014;69(1):219–27. pmid:23946319
  38. 38. Edouard S, Fenollar F, Raoult D. The rise of Tropheryma whipplei: a 12-year retrospective study of PCR diagnoses in our reference center. J Clin Microbiol 2012;50(12):3917–20. pmid:23015670
  39. 39. Bonhomme CJ, Renesto P, Desnues B, Ghigo E, Lepidi H, Fourquet P, et al. Tropheryma whipplei glycosylation in the pathophysiologic profile of Whipple’s disease. J Infect Dis 2009;199(7):1043–52. pmid:19222368
  40. 40. Bonhomme CJ, Renesto P, Nandi S, Lynn AM, Raoult D. Serological microarray for a paradoxical diagnostic of Whipple’s disease. Eur J Clin Microbiol Infect Dis 2008;27(10):959–68. pmid:18594884
  41. 41. Guérin A, Kerner G, Marr N, Markle JG, Fenollar F, Wong N, et al. IRF4 haploinsufficiency in a family with Whipple's disease. Elife. 2018 Mar 14;7
  42. 42. Anolik JH, Ravikumar R, Barnard J, Owen T, Almudevar A, Milner ECB, et al. Cutting edge: Anti-tumor necrosis factor therapy in rheumatoid arthritis inhibits memory B lymphocytes via effects on lymphoid germinal centers and follicular dendritic cell networks. J Immunol 2008;180(2):688–92. pmid:18178805
  43. 43. Glaesener S, Quách TD, Onken N, Weller-Heinemann F, Dressler F, Huppertz H-I, et al. Distinct effects of methotrexate and etanercept on the B cell compartment in patients with juvenile idiopathic arthritis: MTX and etanercept differentially affect B cells in JIA. Arthritis Rheumatol 2014;66(9):2590–600. pmid:24909567
  44. 44. Vitali C, Bombardieri S, Jonsson R, Moutsopoulos HM, Alexander EL, Carsons SE, et al. Classification criteria for Sjögren’s syndrome: a revised version of the European criteria proposed by the American-European Consensus Group. Ann Rheum Dis 2002; 61(6):554–558. pmid:12006334