The palatine tonsil bacteriome, but not the mycobiome, is altered in HIV infection

Abstract

BackgroundMicrobial flora in several organs of HIV-infected individuals have been characterized; however, the palatine tonsil bacteriome and mycobiome and their relationship with each other remain unclear. Determining the palatine tonsil microbiome may provide a better understanding of the pathogenesis of oral and systemic complications in HIV-infected individuals. We conducted a cross-sectional study to characterize the palatine tonsil microbiome in HIV-infected individuals.ResultsPalatine tonsillar swabs were collected from 46 HIV-infected and 20 HIV-uninfected individuals. The bacteriome and mycobiome were analyzed by amplicon sequencing using Illumina MiSeq. The palatine tonsil bacteriome of the HIV-infected individuals differed from that of HIV-uninfected individuals in terms of the decreased relative abundances of the commensal genera Neisseria and Haemophilus. At the species level, the relative abundances and presence of Capnocytophaga ochracea, Neisseria cinerea, and Selenomonas noxia were higher in the HIV-infected group than those in the HIV-uninfected group. In contrast, fungal diversity and composition did not differ significantly between the two groups. Microbial intercorrelation analysis revealed that Candida and Neisseria were negatively correlated with each other in the HIV-infected group. HIV immune status did not influence the palatine tonsil microbiome in the HIV-infected individuals.ConclusionsHIV-infected individuals exhibit dysbiotic changes in their palatine tonsil bacteriome, independent of immunological status.Electronic supplementary materialThe online version of this article (10.1186/s12866-018-1274-9) contains supplementary material, which is available to authorized users.

The Human Oral Microbiome in Health and Disease: From Sequences to Ecosystems

“…Changes in the availability of oxygen, nutrients, and the pH-mediating effect of saliva [96] can promote the growth of different organisms, and conversely, these organisms can be involved in their own small niche construction [97] via biofilm formation and nutrient metabolism, which can produce effects both within the oral cavity (Table 1, Figure 2) and systemically (Table 2, Figure 2). Some researchers have chosen to study all these niches in parallel to compare them against each other [3,8,9], some have selected individual sites with particular focuses on localized dysbiosis in disease states [98][99][100][101][102][103][104][105][106], and others have used an oral rinse approach to capture an overall view of the oral cavity [10][11][12].…”

“…The tongue was also targeted as a potential segment in diagnostic tools that would perhaps incorporate the microbiomes of the full gastrointestinal tract to detect pancreatic cancer [105]. The palatine tonsils were explored in HIV-infected patients to better understand the oral and systemic complications of the disease, and it was shown that the bacteriome was indeed significantly altered in infected individuals, but the mycobiome was not [106].…”

JunB plays a crucial role in development of regulatory T cells by promoting IL-2 signaling

“…Deep sequencing and statistical analysis were performed as described previously. 52 Briefly, the library targeting the 16S rRNA V3 and V4 regions was prepared using the primer set consisting of 341 F (5′-CCTACGGGNGGCWG-CAG-3′) and 806 R (5′-GACTACHVGGGTATCTAATCC-3′) according to the manufacturer's protocol (http://jp.support.illumina.com/ content/dam/illumina-support/documents/documentation/ chemistry_documentation/16s/16s-metagenomic-library-prepguide-15044223-b.pdf) for sequencing on Illumina MiSeq (Illumina, San Diego, CA, USA). The library was sequenced with 2 × 300-bp paired-end reads using MiSeq v3 reagent kits (Illumina, San Diego, CA, USA), resulting in a total of 1,611,524 reads with a mean of 134,294 ± 12,918 sequences per sample.…”

Analysis of the gut microbiome of Japanese inflammatory bowel disease patients

“…A total of 48 families were detected in all three groups. The median number of observed families was lower in CD patients (23, interquartile range (IQR) 19.5-24.5) and UC patients (25, compared with healthy participants (28, (Fig. S3).…”

“…Sequencing data were processed using CLC Genomics Workbench 11.0.1 and CLC Microbial Genomics Module 3.5 (Qiagen, Redwood City, CA, USA) [25]. Overlapping paired-end reads were merged and trimmed, and chimeric reads were ltered using default parameters.…”