Upper respiratory tract bacterial microbiome features associated with Streptococcus pneumoniae colonisation, environmental and demographic factors among older children in a rural setting in The Gambia
Khan, D.; Kwambana-Adams, B.; Tientcheu, P. E.; Thomas, S. A.; Espinoza, J. L.; John, G.; Mamaghani, T.; Ndow, P. S.; Kinteh, M.-A.; Worwui, A.; Otchere, I. D.; Mohammed, N. I.; Nicol, M. P.; Dupont, C. L.; Antonio, M.
Show abstract
Abstract The upper respiratory tract (URT) microbiome plays a vital role in respiratory health, with older children (5-14 years old) serving as key reservoirs for the transmission of bacteria to younger individuals (< 5 years old). However, the URT microbiome in this age group remains poorly characterised, particularly in relation to colonisation by the pathobiont Streptococcus pneumoniae. To address this gap, we analysed URT samples collected over eight visits from 134 healthy children aged 5-14 years in The Gambia, using culture-dependent methods to detect S. pneumoniae and culture-independent 16S rRNA sequencing to characterise the microbiome in parallel. 16S rRNA sequencing identified Streptococcus and Prevotella as the dominant microbiome constituents. When integrating longitudinal sampling data, we observed temporal instability with frequent switches in microbiome profiles over time. URT samples colonised with S. pneumoniae were associated with increased abundance of Moraxella, Streptococcus and Haemophilus, whereas non-colonised URT samples were enriched with Granulicatella and Filifactor. S. pneumoniae colonisation was also associated with lower Shannon diversity compared with non-colonised samples. Age and season were the strongest determinants of microbiome composition; increasing age was associated with decreasing prevalence and abundance of taxa including Haemophilus and Moraxella, while gram-positive taxa were predominantly enriched in the dry season. These findings provide new insight into the URT microbiome of older children in The Gambia and highlight Granulicatella and Filifactor as potential contributors to colonisation resistance against S. pneumoniae.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Gut Microbiome Profiling of a Rural and Urban South African Cohort Reveals Biomarkers of a Population in Lifestyle Transition 94%
- Lifestyle associates with unique resistome and microbiome signatures in children 93%
- Analysis of an Indian colorectal cancer faecal microbiome collection demonstrates universal colorectal cancer-associated patterns, but closest correlation with other Indian cohorts. 91%
Similar papers in this journal
- Gut Microbiome Functional and Taxonomic Diversity within an Amazonian semi-nomadic hunter-gatherer group 94%
- Model selection reveals the butyrate-producing gut bacterium Coprococcus eutactus as predictor for language development in three-year-old rural Ugandan children 93%
- Nasopharyngeal microbial communities of patients infected with SARS-COV-2 that developed COVID-19. 93%
Similar papers in this journal
- Microbiota of the pregnant mouse: characterization of the bacterial communities in the oral cavity, lung, intestine, and vagina through culture and DNA sequencing 92%
- HIV positive patients on antiretroviral therapy have an altered mucosal intestinal but not oral microbiome 92%
- Statistical evaluation of metaproteomics and 16s rRNA amplicon sequencing techniques for the study of the gut microbiota establishment of infants with cystic fibrosis 91%
Similar papers in this journal
- Interactions between the gut microbiome and mucosal immunoglobulins A, M and G in the developing infant gut 92%
- Unique features of the gut microbiome characterized in animal models of Angelman Syndrome 92%
- Human milk bacteria assembled into functionally distinct synthetic communities in infant formula differently affect intestinal physiology and microbiota in neonatal mini-piglets 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.