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. 2016 Aug 2;11(8):e0158288.
doi: 10.1371/journal.pone.0158288. eCollection 2016.

The Application of Magnetic Bead Selection to Investigate Interactions between the Oral Microbiota and Salivary Immunoglobulins

Affiliations

The Application of Magnetic Bead Selection to Investigate Interactions between the Oral Microbiota and Salivary Immunoglobulins

Tejal Madhwani et al. PLoS One. .

Abstract

The effect of humoral immunity on the composition of the oral microbiota is less intensively investigated than hygiene and diet, in part due to a lack of simple and robust systems for investigating interactions between salivary immunoglobulins and oral bacteria. Here we report the application of an ex situ method to investigate the specificity of salivary immunoglobulins for salivary bacteria. Saliva collected from six volunteers was separated into immunoglobulin and microbial fractions, and the microbial fractions were then directly exposed to salivary immunoglobulins of "self" and "non-self" origin. Antibody-selected bacteria were separated from their congeners using a magnetic bead system, selective for IgA or IgG isotypes. The positively selected fractions were then characterized using gel-based eubacterial-specific DNA profiling. The eubacterial profiles of positively selected fractions diverged significantly from profiles of whole salivary consortia based on volunteer (P≤ 0.001%) and immunoglobulin origin (P≤ 0.001%), but not immunoglobulin isotype (P = 0.2). DNA profiles of separated microbial fractions were significantly (p≤ 0.05) less diverse than whole salivary consortia and included oral and environmental bacteria. Consortia selected using self immunoglobulins were generally less diverse than those selected with immunoglobulins of non-self origin. Magnetic bead separation facilitated the testing of interactions between salivary antibodies and oral bacteria, showing that these interactions are specific and may reflect differences in recognition by self and non-self immunoglobulins. Further development of this system could improve understanding of the relationship between the oral microbiota and the host immune system and of mechanisms underlying the compositional stability of the oral microbiota.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Schematic representation of the procedure for separation of salivary immunoglobulin recognized salivary consortia.
(A) preparation of isotype specific magnetic beads; (B) coating of salivary consortia with immunoglobulins; (C) separation of immunoglobulin-recognized microbial consortia from complete oral consortium using magnetic bead separation; and (D) magnetic bead-adherent bacteria remaining after washing the non-adherent fraction.
Fig 2
Fig 2. Major phylotypes identified by 16S rRNA gene sequencing from complete salivary and.
Immunoglobulin-selected fractions. abased on EMBL database searches; bthe number of ambiguous bases is given in parenthesis;. cidentified in salivary microbiota profile; didentified in the immunoglobulin (Ig) recognized positively selected fraction; known oral bacterium; ¥multiple bands noted in same lane; identified by sequencing colonies from culture of pooled positively selected fractions on blood agar.
Fig 3
Fig 3
Eubacterial DNA profiles of saliva from six volunteers (indicated by red, green, pink, blue, grey and black colours) analysed by cluster analysis (a) and non-metric MDS (b). Closed circles, whole saliva, open circles, saliva eubacterial fractions selected by antibodies from volunteers as indicated by coloured centre spots. DNA profiles differed significantly between individuals for microbiota (P≤ 0.001%, R = 0.475); between microbiota selected by self vs non-self-antibodies (P≤ 0.001, R = 0.42); according to microbiota and immunoglobulin origin (P≤ 0.001, R = 0.63), but not immunoglobulin isotype (p = 0.2, R = 0.029). Robustness of clusters (p≤ 0.05) is indicted in red, based on the SIMPROF test in PRIMER v6. Contour lines on the MDS plot superimpose 55% resemblance levels derived from cluster analysis.

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