Primate Monocytes - CD14, CD16 - Ziegler-Heitbrock

Differential expression and predictive value of monocyte scavenger receptor CD163 in populations with different tuberculosis infection statuses.

Abstract

BACKGROUND: Monocytes are the predominant innate immune cells at the early stage of Mycobacterium tuberculosis (M. tb) infection as the host defense against intracellular pathogens. Understanding the profile of different monocyte subpopulations and the dynamics of monocyte-related biomarkers may be useful for the diagnosis and prognosis of tuberculosis. METHODS: We enrolled 129 individuals comprising patients with pulmonary tuberculosis (PTB) (n = 39), tuberculous pleurisy (TBP) (n = 28), malignant pleural effusion (MPE) (n = 21), latent tuberculosis infection (LTBI) (n = 20), and healthy controls (HC) (n = 21). Surface expression of CD14, CD16, and CD163 on monocytes was detected using flow cytometry. In addition, soluble CD163 (sCD163) was determined by enzyme linked immunosorbent assay. RESULTS: Higher frequency of CD14+CD16+ (15.7% vs 7.8%, P < 0.0001) and CD14-CD16+ (5.3% vs 2.5%, P = 0.0011) monocytes and a decreased percentage of CD14+CD16- (51.0% vs 70.4%, P = 0.0110) cells was observed in PTB patients than in HCs. Moreover, PTB patients displayed a higher frequency of CD163+ cells in CD16+ monocytes than those in the HC group (40.4% vs 11.3%, P < 0.0001). The level of sCD163 was elevated in TBP patients and was higher in pleural effusion than in plasma (2116.0 ng/ml vs 1236.0 ng/ml, P < 0.0001). sCD163 levels in pleural effusion and plasma could be used to distinguish TBP from MPE patients (cut-off values: 1950.0 and 934.7 ng/ml, respectively; AUCs: 0.8418 and 0.8136, respectively). Importantly, plasma sCD163 levels in TBP patients decreased significantly after anti-TB treatment. CONCLUSIONS: Higher expression of membrane and soluble CD163 in active tuberculosis patients might provide insights regarding the pathogenesis of tuberculosis, and sCD163 may be a novel biomarker to distinguish TBP from MPE and to predict disease severity.