DNA CIRCULOMICS IN MURINE DNASE KNOCKOUT MODELS OF SLE REVEALS ENRICHMENT OF CALCIUM SIGNALING PATHWAYS IN LIVER
Bibliographic record
Abstract
PV105 / #92 Poster Topic: AS12 - Genetics, Epigenetics, Transcriptomics Background/Purpose Liver function test abnormalities occur in nearly 60% of patients with SLE, compared with 1-4% in the general population. While the inositol/calcium signaling pathway has been intensely studied in immune cells in health and disease, how the dysregulation of calcium signaling may contribute to the pathogenesis of SLE in particular is not well understood. Deficiencies in 2 endonucleases, DNASE1 and DNASE1L3, have been shown to cause SLE. Previously, we analyzed the genic profiles of cf-eccDNA in the plasma of Dnase1 -/- and Dnase1l3 -/- compared to wild-type (WT) mice. Here, we performed circulomics analyses in liver of the same groups. Methods eccDNA libraries were generated from livers of 5 WT, Dnase1 -/- and Dnase1l3 -/- mice, respectively, based on short-read sequencing data amplified by rolling circle amplification.[1] We downloaded the dataset from the EGA (accession EGAS00001005873) and applied our computational pipeline and differential analysis method, DifCir, to identify eccDNA from the liver DNA circulomics data. Each eccDNA was defined by 2 split reads, and coupled to a fold change of 1 on a log 2 scale at a 0.05 significance level to filter out nonsignificant genic eccDNA. Gene Ontology (GO) enrichment analysis was applied on the statistically significant eccDNAs. Results For Dnase1l3 -/- vs WT, we identified 304 up- and 92 down-differentially produced per gene circles (DPpGCs). The top up-DPpGC originated from the RAS protein-specific guanine nucleotide-releasing factor 1, Rasgrf1 (p=2.53e -05 ). It has been suggested that Rasgrf1 plays a role in the differentiation of plasma cells from B cells when confronted with factors of T cell-derived humoral immune responses. The most enriched GO term of up-DPpGCs was “positive regulation of GTPase activity,” followed by “GTPase activator activity,” and “nucleoside triphosphatase regulator activity.” The highest ranked down-DPpGC derived from the calmodulin binding transcription activator 1, Camta1 (p=0.0010). The encoded protein is a transcription factor and tumor suppressor. Camta1 participated as a member of the top-ranked GO terms in down-DPpGCs, “calcineurin-mediated signaling” and “inositol phosphate mediated signaling.” For Dnase1 -/- vs WT, we identified 291 up- and 104 down- DPpGCs. The top up-DPpGC arised from the sodium/potassium transporting ATPase interacting 3, Nkain3 (p=0.00077). GO terms statistically enriched in up-DPpGCs included “ion homeostasis,” “calmodulin binding,” and “glutamate receptor activity.” The top-ranked down-DPpGC came from phospholipase C like-1 (inactive), Plcl1 (p=1.18e -05 ), with PLCL1 known as a suppressor of tumor progression in renal cell carcinoma. GO terms in down-DPpGCs included “cyclic gmp-amp transmembrane,” “response to interleukin 3,” “histone dephosphorylation,” and “calcineurin-mediated signaling.” Intra-organ comparison of eccDNA in the liver of Dnase1 -/- and Dnase1l3 -/- mutants revealed an enrichment of the GO term “calcineurin-mediated signaling” in both models. Eight genes, Asic2, Bnc2, Cacna2d2, Farp2, Osbpl10, Pcca, Prdm16 and Tg, were identified as up-excising eccDNA in both liver and plasma of Dnase1l3 -/- mice.[2] Remarkably, we found that oxysterol binding protein like-10, OSBPL10, predominantly expressed in B cells and plasma cells, is also a specific and up-producing cf-eccDNA in DNASE1L3-deficient SLE patients.[3] Conclusions We found a functional enrichment of inositol/calcium signaling pathways for the genes excising eccDNA in the liver of SLE mouse models. More targeted research is needed to elucidate the precise mechanisms linking inositol calcium signaling, lupus, and liver pathology. Circulomics research might reveal new genes and cascades that elucidate the pathology and provide candidates for treatment interference. References: [1.] Sin ST. JCI Insight 2022;7(8):e156070. [2.] Garovska D. Biomedicines 2023;12(1):80. [3.] Gerovska D. 2023;12:1061.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".