Evidence for Maternal Autoantibodies in the Pathogenesis of Preterm Birth
Bibliographic record
Abstract
SUMMARY Complications from preterm birth are the leading cause of global mortality in children under age five 1,2 . Spontaneous preterm labor is the most common cause of preterm delivery and is associated with a breakdown of maternal-fetal tolerance 3–5 . However, the current understanding of the role of autoantibodies in this process has been limited to a handful of examples of pathogenic antibodies that occur with pregnancy complications 6–14 . Here, we employ proteome-wide autoantibody profiling via phage display immunoprecipitation and sequencing (PhIP-seq) to identify autoreactivities associated with pregnancy as well as term or preterm delivery outcomes in maternal sera across eight cohorts of human pregnancy (n=2,194). We find that preterm pregnancies exhibit greater proteome-wide autoreactivity, validated by placental proteome immunoprecipitation mass spectrometry analysis using patient sera. Within the preterm birth associated autoreactive signature, we find enrichment for pathways known to be critical for normal pregnancy outcomes, including vascular development, reproductive hormones, and regulators of inflammation. Further analysis of autoreactive targets revealed involvement of the IL1β inflammatory cytokine cascade. IL1β is one of the few inflammatory cytokines sufficient to rapidly induce labor in animals 15–24 and it is also elevated in preterm human pregnancies 25–29 . Across these eight cohorts, antibodies to cytokine IL1 receptor antagonist (IL1RA), a natural antagonist to IL1β are significantly enriched in roughly 7% of preterm pregnancies. Sera from these patients functionally neutralize IL1RA activity in vitro and anti-IL1RA induces greater resorption, inflammation, and vascular malperfusion in timed-pregnant mice in vivo . These findings suggest utility for serologic diagnostics as one approach to stratify risk of spontaneous preterm delivery in addition to interventional strategies for restoring control of IL1β during pregnancy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".