Decoding the Epigenetic and Transcriptional Signatures of Pathogenic B-Cells in Patients at High Risk of Transformation to Lymphoma
Notice bibliographique
Résumé
Systemic Lupus Erythematosus (SLE) is a chronic, heterogenous autoimmune disorder characterized by diverse clinical manifestations and treatment responses due to various genetic, epigenetic, and immune-related factors (Kaul et al., 2016). SLE is characterized by the expansion of a rare B cell subset, double negative (DN), with the majority of these being DN2 cells (ZEB2hiITGAXhi, Jenks et al., 2018). These cells are known to be the main cause of SLE pathogenicity, and are precursors of pathogenic antibody secreting cells (Elsner & Shlomchik, 2020). SLE patients can experience several life threatening complications, including a 3-fold higher risk of developing hematological malignancies, particularly B cell lymphomas (Clarke et al., 2021). While recent studies have linked mutations involved in B cell clonal expansion to alterations in SLE pathogenesis-driving cells, there is limited direct clinical evidence on how this malignancy transformation begins (Pullabhatla et al., 2018; Singh et al., 2020; Venturutti et al., 2023). Therefore, studying DN2 cells in autoimmune disorders represents a unique opportunity to identify transcriptional, genetic and epigenetic alterations that might serve as pre-malignancy indicators in these patients. To characterize the somatic mutational landscape of active and inactive B-cells in SLE, we profiled a cohort of 14 SLE patients and 3 healthy controls (HC) using whole-exome sequencing. Three subsets of peripheral blood mononuclear cells were sorted: naïve B cells [CD19+IgD+], memory B cells [CD19+IgD-CD27+] and DN B cells [CD19+IgD-CD27-]. Using nf-core/sarek (Garcia et al., 2020), we identified several somatic alterations in the DN cell population that were previously found in lymphoid malignancies, such as EP300, a histone acetyltransferase (Huang et al., 2021), ITPKB, a kinase involved in the PI3K-AKT signaling (Tiacci et al., 2018), and DUSP22 tumor suppressor (Melart et al., 2016). These findings suggest the possibility of DN2 cells as precursors of lymphoid cancers, thus prompting the need to further characterize DN2 cellular state. To investigate the transcriptional and epigenetic alterations in DN2 cells, we conducted single-cell multiome sequencing on a pilot cohort of 4 SLE patients. In total we analyzed 34,472 cells from the SLE cohort including 2,664 DN2 cells, and 24,162 cells from the HC samples including 2,571 DN cells. Differential gene expression analysis revealed significant enrichment of Interferon (IFN) Alpha and Gamma in the SLE DN2 cells, and over-expression of CD86 compared to HC-DN cells, as previously reported (Jenks et al., 2018). However, by analyzing variations in transcriptomic profiles obtained from scRNA using consensus non-negative matrix factorization, we uncovered a unique and novel expression program in SLE DN2 cells that was absent in HC DN cells. This program was enriched in hematological malignancy-associated IFN/MHC class I and II pathways previously found in heme-related cancers (Gavish et al., 2023). This indicates an inherent bias in SLE DN2 cells towards a more malignant phenotype. Further, we discovered that highly accessible genes in SLE DN2 cells were enriched in a signature corresponding to activated B cell diffuse large B cell lymphoma (DLBCL) state 4 cells (Steen et al., 2021). These cells are identified as pre-plasmablast DLBCL cells and exhibit a more aggressive and unfavorable phenotype associated with poorer clinical outcomes. Motif enrichment analysis in open chromatin regions of SLE DN2 cells further revealed enrichment of the POU family of transcription factors (TFs), such as POU2F2 (encoding OCT2) and POU3F1 (encoding OCT-6) among others. OCT2 is known to be highly active in B cells, and a mutated form of this TF, has been found in various lymphomas with a different repertoire of targets (Hodson et al., 2016). Interestingly, the POU2F2 gene harbors a frameshift mutation in the Memory and DN subsets of an active SLE patient in our cohort. In summary, we identified distinctive transcriptional and epigenetic profiles in SLE DN2 cells, enriched in pathways associated with hematological malignancies and containing key oncogenic mutations. These findings suggest that the malignant phenotypic alterations in DN2 cells could serve as biomarkers of pre-malignancy, providing new insights into the pathogenesis and potential transformation of autoimmune diseases into malignancies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».