The intestinal microbiome and metabolome discern disease severity in cytotoxic T-lymphocyte-associated protein 4 deficiency
Bibliographic record
Abstract
BACKGROUND: Cytotoxic T-lymphocyte-associated protein 4 deficiency (CTLA4-D) is an inborn error of immunity (IEI) caused by heterozygous mutations, and characterized by immune cell infiltration into the gut and other organs, leading to intestinal disease, immune dysregulation and autoimmunity. While regulatory T-cell dysfunction remains central to CTLA4-D immunopathogenesis, mechanisms driving disease severity and intestinal pathology are unknown but likely involve intestinal dysbiosis. We determined whether the intestinal microbiome and metabolome could distinguish individuals with severe CTLA4-D and identify biomarkers of disease severity. RESULTS: The genera Veillonella and Streptococcus emerged as biomarkers that distinguished CTLA4-D from healthy cohorts from both the National Institutes of Health (NIH) Clinical Center, USA (NIH; CTLA-D, n = 32; healthy controls, n = 16), and a geographically distinct cohort from the Center for Chronic Immunodeficiency (CCI) of the Medical Center - University of Freiburg, Germany (CCI; CTLA4-D, n = 25; healthy controls, n = 24). Since IEIs in general may be associated with perturbations of the microbiota, a disease control cohort of individuals with common variable immunodeficiency (CVID, n = 20) was included to evaluate for a CTLA4-D-specific microbial signature. Despite common IEI-associated microbiome changes, the two bacterial genera retained their specificity as biomarkers for CTLA4-D. We further identified intestinal microbiome and metabolomic signatures that distinguished patients with CTLA4-D having severe vs. mild disease. Microbiome changes were associated with distinct stool metabolomic profiles and predicted changes in metabolic pathways. These differences were impacted by the presence of gastrointestinal manifestations and were partially reversed by treatment with abatacept and/or sirolimus. CONCLUSIONS: Loss of intestinal microbial diversity and dysbiosis causing metabolomic changes was observed in CTLA4-D. Albeit some of these features were shared with CVID, the distinct changes associated with CTLA4-D highlight the fact that IEI-associated microbiome changes likely reflect the underlying immune dysregulation. Identified candidate intestinal microbial and metabolic biomarkers distinguishing individuals with CTLA4-D based on severity should be studied prospectively to determine their predictive value, and investigated as potential therapeutic ta. Video Abstract.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".