Residential bacteria and fungi identified by high-throughput sequencing and childhood respiratory health

• Published in: Environmental research 2022-03, Vol.204 (Pt D), p.112377-112377, Article 112377
• Main Authors: Cox, Jennie, Stone, Timothy, Ryan, Patrick, Burkle, Jeff, Jandarov, Roman, Mendell, Mark J., Niemeier-Walsh, Christine, Reponen, Tiina
• Format: Article
• Language: English
• Subjects: Air Pollution, Indoor - analysis; Asthma; Bacteria; Bacteria - genetics; Child; Dust - analysis; Fungi - genetics; High-Throughput Nucleotide Sequencing; Humans; Indoor air; Microbiota; Mold; Wheeze
• ISSN: 0013-9351; 1096-0953
• DOI: 10.1016/j.envres.2021.112377

The objective of this study was to examine and compare environmental microbiota from dust and children's respiratory health outcomes at ages seven and twelve. At age seven, in-home visits were conducted for children enrolled in the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Floor dust was collected and analyzed for bacterial (16 S rRNA gene) and fungal (internal transcribed spacer region) microbiota. Respiratory outcomes, including physician-diagnosed asthma, wheeze, rhinitis, and aeroallergen sensitivity were assessed by physical examination and caregiver-report at ages seven and twelve. The associations between dust microbiota and respiratory outcomes were evaluated using Permanova, DESeq, and weighted quantile sum (WQS) regression models. Four types of WQS regression models were run to identify mixtures of fungi or bacteria that were associated with the absence or presence of health outcomes. For alpha or beta diversity of fungi and bacteria, no significant associations were found with respiratory health outcomes. DESeq identified specific bacterial and fungal indicator taxa that were higher or lower with the presence of different health outcomes. Most individual indicator fungal species were lower with asthma and wheeze and higher with aeroallergen positivity and rhinitis, whereas bacterial data was less consistent. WQS regression models demonstrated that a combination of species might influence health outcomes. Several heavily weighted species had a strong influence on the models, and therefore, created a microbial community that was associated with the absence or presence of asthma, wheeze, rhinitis, and aeroallergen+. Weights for specific species within WQS regression models supported indicator taxa findings. Health outcomes might be more influenced by the composition of a complex mixture of bacterial and fungal species in the indoor environment than by the absence or presence of individual species. This study demonstrates that WQS is a useful tool in evaluating mixtures in relation to potential health effects. [Display omitted] •Indoor dust microbiota exposure could influence childhood respiratory outcomes.•Specific fungal indicator taxa were lower with the presence of asthma and wheeze.•Other fungal indicator taxa were higher for those with aeroallergen+ and rhinitis.•Models with species mixtures were positively and negatively associated with health.