Establishment of a double humanized lupus mouse model with human immune system and SLE patient microbiota 2284
Bibliographic record
Abstract
Abstract Description Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease influenced by complex interactions between the immune system and microbiota. To investigate these interactions, we established a Double humanized SLE (DhuSLE) mouse model by engrafting NSG mice with human CD34+ hematopoietic stem and progenitor cells (NSG-hu mice) and fecal microbiota transplantation from SLE patients (SLE-FMT). FMT transiently suppressed the development of human T cells in NSG-hu mice, indicating a modulatory effect of the transplanted microbiota on immune reconstitution. SLE-FMT alone did not induce most lupus clinical signs but promoted skin lesions, highlighting a microbiota-driven cutaneous response. Contrary to a previous report, pristane injection alone failed to drive lupus-like disease in NSG-hu mice. However, the combination of SLE-FMT and pristane induced lupus nephritis in DhuSLE mice. Interestingly, however, clinical signs observed in DhuSLE mice did not fully reflect those of the microbiota donors. Together, these results suggest that the combination of SLE-FMT and pristane induces lupus nephritis in NSG-hu mice. This indicates the successful establishment of a DhuSLE humanized lupus mouse model. However, limitations exist, and the model may benefit from estrogen conditioning and a different immunodeficient background, as shown in the recently published THX mouse. Upon further development, the DhuSLE mouse can be used to elucidate the pathogenetic mechanisms of human SLE. Funding Sources Supported by United States Department of Defense CDMRP program award number: HT9425-23-1-0343 Topic Categories Basic Autoimmunity (BA)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".