Fungal diversity in the gut microbiome of young South African children
Bibliographic record
Abstract
BACKGROUND: The fungal microbiome, or mycobiome, is a poorly described component of the gut ecosystem and little is known about its structure and development in children. In South Africa, there have been no culture-independent evaluations of the child gut mycobiota. This study aimed to characterise the gut mycobiota and explore the relationships between fungi and bacteria in the gut microbiome of children from Cape Town communities. METHODS: Stool samples were collected from children enrolled in the TB-CHAMP clinical trial. Internal transcribed spacer 1 (ITS1) gene sequencing was performed on a total of 115 stool samples using the Illumina MiSeq platform. Differences in fungal diversity and composition in relation to demographic, clinical, and environmental factors were investigated, and correlations between fungi and previously described bacterial populations in the same samples were described. RESULTS: Taxa from the genera Candida and Saccharomyces were detected in all participants. Differential abundance analysis showed that Candida spp. were significantly more abundant in children younger than 2 years compared to older children. The gut mycobiota was less diverse than the bacterial microbiota of the same participants, consistent with the findings of other human microbiome studies. The variation in richness and evenness of fungi was substantial, even between individuals of the same age. There was significant association between vitamin A supplementation and higher fungal alpha diversity (p = 0.047), and girls were shown to have lower fungal alpha diversity (p = 0.003). Co-occurrence between several bacterial taxa and Candida albicans was observed. CONCLUSIONS: The dominant fungal taxa in our study population were similar to those reported in other paediatric studies; however, it remains difficult to identify the true core gut mycobiota due to the challenges set by the low abundance of gut fungi and the lack of true gut colonising species. The connection between the microbiota, vitamin A supplementation, and growth and immunity warrants exploration, especially in populations at risk for micronutrient deficiencies. While we were able to provide insight into the gut mycobiota of young South African children, further functional studies are necessary to explain the role of the mycobiota and the correlations between bacteria and fungi in human health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".