DNA CIRCULOMICS IN MURINE DNASE KNOCKOUT MODELS OF SLE REVEALS ENRICHMENT OF CALCIUM SIGNALING PATHWAYS IN LIVER
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».