IDENTIFICATION OF FOXM1 AS A CANDIDATE DRIVER OF SLE AUTOIMMUNITY AND LUPUS NEPHRITIS
Bibliographic record
Abstract
PV135 / #415 Poster Topic: AS16 - Lupus Nephritis-Pathogenesis Background/Purpose Among the variable organ system manifestations experienced by patients with SLE, lupus nephritis (LN) is both common, affecting approximately 60% of patients, and severe. Patients with LN are characterized by enrichment in particular autoantibodies, including anti-double-stranded (ds)DNA and anti-Smith (Sm). We studied PBMC from our longitudinal SLE patient cohort and characterized gene transcripts that are correlated with levels of LN-associated autoantibodies. We focused on those transcripts that identify pathways and mechanisms that are particularly related to production of anti-dsDNA and/or anti-Sm autoantibodies compared to those associated with production of autoantibodies that are more generally characteristic of systemic autoimmunity, such as anti-Ro52. Understanding the mechanisms involved in development of pathogenic lupus autoantibodies could lead to identification of novel therapeutic targets. Methods Subjects included 80 SLE patients from our longitudinal SLE patient cohort at Hospital for Special Surgery. Samples were collected at 1 to 14 visits over a period of 3 (0-12) years, with an average of 4 time points per patient. Plasma levels of autoantibodies present in each patient sample were determined based on clinical assays and antigen array. RNA sequencing of patient PBMC was performed and the obtained data matrix of autoantibody levels and gene transcripts used to generate functionally annotated groups of co-expressed genes using the Weighted Gene Co-expression Analysis (WGCNA) algorithm. Comparison of autoantibody titers with gene expression was analyzed by linear mixed model, using either a per module or per gene approach. Transcripts associated with levels of pathogenic autoantibodies (anti-dsDNA and anti-Sm/RNP) were identified and their mechanisms of regulation assessed by literature review. Results Clusters of specific autoantibodies were identified based on degree of correlation between their titer and the level of expression of individual mRNA transcripts. Titers of LN-associated autoantibodies were highly associated with expression of cell cycle genes. Among those, the most significant correlations (p < 10^-5) were seen for TK1, AURKB, KIFC1, KIF15, FOXM1, GINS2, NGAPG, CDC45, CDCA5, CCNA1 , and CCNB1 . In contrast, the cluster of autoantibodies that are not associated with LN (eg, anti-Ro52) did not show an association with cell cycle transcripts. Based on literature review of the identified cell cycle-related genes, FOXM1 , encoding an important transcription factor, is itself a key regulator of many of the cell cycle transcripts identified in this analysis. Conclusions Our data indicate that cell cycle-related gene transcripts are associated with plasma levels of pathogenic autoantibodies implicated in LN (anti-dsDNA and anti-Sm) in contrast to anti-Ro52 autoantibodies that represent a more general measure of systemic autoimmunity. Of those cell cycle-related transcripts, FOXM1 is not only highly associated with elevated levels of pathogenic lupus autoantibodies, but is also identified as a critical regulator of many of the other cell cycle-related genes associated with high level pathogenic autoantibodies. FOXM1 is recognized as a critical regulator of malignant cells, is considered a therapeutic target in oncology, and pharmacologic inhibitors are in development for a number of malignancies. Characterization of the specific roles played by FOXM1 in the regulation of autoimmunity may provide the rationale for that transcription factor serving as a novel therapeutic target for LN.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".