Selective IgA Deficiency in Spontaneously Hypertensive Rats With Gut Dysbiosis
Bibliographic record
Abstract
Background: The spontaneously hypertensive rat (SHR) is extensively used to study hypertension. Gut microbiota dysbiosis is a notable feature in SHR for reasons unknown. Immunoglobulin A (IgA) is a major host factor required for gut microbiota homeostasis. We hypothesized that inadequate IgA contributes to gut microbiota dysbiosis in SHR. methods: IgA was measured in feces, cecum, serum, liver, gut-associated lymphoid tissue, and milk from SHR and Wistar Kyoto rats. IgA regulatory factors like IgM, IgG, and pIgR (polymeric immunoglobulin receptor) were analyzed. IgA and IgG antibodies and blood pressure (BP) were measured before and after administrating a bacterial antigen (ie, flagellin). Results: Compared with Wistar Kyoto rats, SHR displayed remarkably near-deficient IgA levels accompanied by compensatory increases in serum IgM and IgG and gut-liver pIgR expression. Inadequate milk IgA in SHR emphasized this immune defect stemmed from the neonatal stage. Reduced IgA + B cells in circulation and Peyer patches indicated a possible reason for the lower IgA in SHR. Noteworthy, a genetic insufficiency was unlikely because administering flagellin to SHR induced anti-flagellin IgA antibodies. This immune response surprisingly accelerated hypertension development in SHR, suggesting IgA quiescence may help maintain lower BP. Conclusions: This study is the first to reveal IgA deficiency in SHR as one host factor associated with gut microbiota dysbiosis and invigorates future research to determine the pathophysiological role of IgA in hypertension.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".