S3196 Dysbiosis in a Triplet With an Autism Spectrum Disorder: A Case Study
Bibliographic record
Abstract
INTRODUCTION: We present a case of triplets, one diagnosed with an Autism Spectrum Disorder (ASD). While patients with ASD are characterized by deficits in language and social interaction, these are often accompanied by gastrointestinal (GI) symptoms. Approximately half of a children with ASD have GI problems as well, and there appears to be a positive correlation in severity of GI symptoms and autistic severity. Trials of vancomycin and fecal microbiota transplant (FMT) have shown that altering the microbiome of the gut has success in improving both GI and ASD symptoms. The purpose of this study was to compare the microbiome of an autistic child with that of the child’s biological siblings and mother, in the hopes of elucidating perturbations possibly associated with ASD. METHODS: Next-generation sequencing was performed on fecal samples from a mother and her 3 siblings, two healthy and one with ASD (Sibling #3). Following stool collection, DNA was then extracted, quantitated, and normalized for downstream library fabrication utilizing shotgun methodology. Prepared and indexed libraries were subsequently pooled and sequenced on the Illumina NextSeq 550 System. Metagenomic readout data was analyzed for relative abundances of defined bacteria and overall microbiome diversity as measured by Shannon index. RESULTS: The ASD patient (Sibling #3) was found to possess lower Bifidobacteria and Prevotella to the patient’s healthy mother, and overall lower biodiversity as measured by the Shannon Index. Additionally, healthy Sibling #1 was found to have greater bacterial diversity than the mother, more Prevotella and Bifidobacter than Sibling #2. CONCLUSION: Our findings demonstrate the role of dysbiosis in ASD, and the utility of microbiome sequencing in order to draw conclusions regarding etiology and potential treatment modalities. Sequencing of the microbiome also presents potential insights in determining the optimal donor for a patient. As highlighted in Figure 1, Sibling #1 has greater microbial diversity and more Bifidobacterium than the mother, and more Prevotella than Sibling #2 and Sibling #3.Figure 1.: Comparative diversity of the gut microbiome of a patient with ASD (Sibling #3) and the patient’s biological siblings and mother.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".