A Type I Interferon Transcriptional Signature Precedes Autoimmunity in Children Genetically at Risk for Type 1 Diabetes
Bibliographic record
Abstract
Diagnosis of the autoimmune disease type 1 diabetes (T1D) is preceded by the appearance of circulating autoantibodies to pancreatic islets. However, almost nothing is known about events leading to this islet autoimmunity. Previous epidemiological and genetic data have associated viral infections and antiviral type I interferon (IFN) immune response genes with T1D. Here, we first used DNA microarray analysis to identify IFN-β-inducible genes in vitro and then used this set of genes to define an IFN-inducible transcriptional signature in peripheral blood mononuclear cells from a group of active systemic lupus erythematosus patients (n = 25). Using this predefined set of 225 IFN signature genes, we investigated the expression of the signature in cohorts of healthy controls (n = 87), patients with T1D (n = 64), and a large longitudinal birth cohort of children genetically predisposed to T1D (n = 109; 454 microarrayed samples). Expression of the IFN signature was increased in genetically predisposed children before the development of autoantibodies (P = 0.0012) but not in patients with established T1D. Upregulation of IFN-inducible genes was transient, temporally associated with a recent history of upper respiratory tract infections (P = 0.0064), and marked by increased expression of SIGLEC-1 (CD169), a lectin-like receptor expressed on CD14(+) monocytes. DNA variation in IFN-inducible genes altered T1D risk (P = 0.007), as exemplified by IFIH1, one of the genes in our IFN signature for which increased expression is a known risk factor for disease. These findings identify transient increased expression of type I IFN genes in preclinical diabetes as a risk factor for autoimmunity in children with a genetic predisposition to T1D.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".