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Enregistrement W2911507042 · doi:10.1182/blood-2018-99-118501

Cell Type-Specific Deregulation of Polypyrimidine Tract- Binding Proteins (PTBPs) Drive Aberrant Splicing in Multiple Myeloma (MM) and Acute Myeloid Leukemia (AML)

2018· article· en· W2911507042 sur OpenAlexaff
Sophia Adamia, Sigitas Verselis, Teru Hideshima, Michael P. Chu, Shruti Bhatt, David P. Steensma, Daniel J. DeAngelo, Richard M. Stone, Daniel G. Tenen, Linda M. Pilarski, James D. Griffin, Kenneth C. Anderson

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueRNA Research and Splicing
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésDownregulation and upregulationMyeloid leukemiaCancer researchMyeloidBiologyRNA splicingCell cultureMolecular biologyStromal cellIntronBone marrowPolypyrimidine tract-binding proteinTranscriptomeImmunologyGene expressionRNAGeneGenetics

Résumé

récupéré en direct d'OpenAlex

Abstract Genome-wide transcriptome profiling detected an increased splicing alterations in MM and AML. While these malignancies are derived from different cell linages, their tumor cells acquire similar aberrant splicing (AbSp), mostly intron retentions. To delineate AbSp mechanism in MM/AML, we focused on PTBPs (1/2/3) that play a critical role in intron excision. We have previously reported deregulated expression of splicing factors (SFs) in MM/AML patient and healthy donor (HD) bone marrow (BM). As MM progressed, PTBP1/2 progressively increased, and PTBP3 gradually decreased (ASH 2017). In AML, PTBP2/3 upregulation and PTBP1 downregulation were detected in patient samples in which increased intron retentions were identified by genome-wide splicing analysis (CCR 2015). Here, these findings were validated by TaqMan assays for PTBPs in 48 MM and 325 AML patient samples, 16 MM/AML cell lines, and in plasma cells (PCs) and CD34+cells from 14 HDBM. Results were consistent with differential expressions of PTBPs in MM/AML previously analyzed. Upregulation of PTBP1/2 and PTBP2/3 proteins were detected in MM and AML cell lines, respectively. PTBP1/2 upregulation was pronounced when MM cell lines were cocultured with BM stromal cells (MMBMSC) derived from MM patients' BM. Importantly, we detected increased proliferation and decreased apoptosis in MM/AML cell lines overexpressing PTBPs. These effects were most evident after coculturing MM cell lines with MMBMSC as compared to HDBMSC. To evaluate PTBP effects on AbSp, we knocked down and/or overexpressed PTBPs in MM/AML cell lines and assessed PDL1 splicing in MM, and NOTCH2 and FLT3 splicing in AML. PDL1 is spliced in ~ 30% of 90 MM patients; while NOTCH2/FLT3/CD13 are spliced in 78%/50% of 387 AML patients, respectively. After PTBP1/2 knockdown in MM cells we detected 4- to 11-fold downregulation of PDL1 splice variants, with proportional upregulation of wild-type PDL1 levels; concurrently, MM cell proliferation was decreased and apoptosis increased. To evaluate MM specific splicing alterations in the context of the MM BM microenvironment, PDL1 splicing, and PTBP1/2 mRNA/protein expressions, were monitored in MM cell lines cocultured with MMBMSC or HDBMSC. We observed time-dependent PDL1 splice variant upregulation, and higher levels of PTBP1/2 in MM cells. We also noted time-dependent PDL1 variant expression switching in association with PTBP1/2 deregulation in the BM microenvironment. RNA-seq analysis and western blotting showed that MMBMSC culture with tumor cells increased intron retention, and altered SF expressions, including PTBPs in BMSC. We next monitored PTBP effects on splicing in AML cells by evaluating NOTCH2 and FLT3 splicing in an TF1, an AML cell line that overexpresses PTBP2/3. RT-PCR analysis showed association between PTBP3 overexpression and NOTCH2 and FLT3 AbSp in TF1 cells. For further validation, we developed an ex vivo splicing assay composed of an FLT3/CD13 splicing cassette with a GFP reporter, which allows for evaluation of splicing events by flow cytometry (FACS) and microscopy. In this assay, cells overexpressing PTBP2/3 caused FLT3/CD13 minigene splicing similar to that detected in AML patients. Also, by RT-PCR, we showed that overexpression of PTBPs caused intron retention, that was confirmed by cloning and sequencing of PCR products and consistent with the FACS analysis and microscopy. Finally, we have tested effects of PTBPs using in vivo AML models (BMT and xenograft). In the BMT model, animal median survival was 48 days post-BMT for MLL-AF9/PTBP3 and 56 days in the control group. In the xenograft model, animal median survival was 66, 94, & 106 days after injection of TF1-PTBP3, TF1-PTBP2, and TF1 cells in mice, respectively (P>0.0001). These studies suggest that PTBP3 overexpression in partnership with the MLL-AF9 promotes occurrence of AML in mice. Tumor RNA samples harvested from these animals were subjected to RNA-seq analysis, which showed increased AbSp association with PTBP3 overexpression. Our studies indicate that deregulated PTBP1/2 expression in MM and of PTBP2/3 in AML drive time-dependent AbSp (intron retention) and splice variant switching, which in MM is induced by culture with BMSC; and conversely, that analogous changes are induced in BMSC cultured with tumor cells. They define role of deregulated expression of the PTBPs in MM/AML pathogenesis, and suggest novel targets for therapeutic intervention. Disclosures Stone: Pfizer: Consultancy; Jazz: Consultancy; Fujifilm: Consultancy; Merck: Consultancy; Cornerstone: Consultancy; Celgene: Consultancy, Other: Data and Safety Monitoring Board, Steering Committee; Novartis: Consultancy, Research Funding; Sumitomo: Consultancy; Ono: Consultancy; Orsenix: Consultancy; Otsuka: Consultancy; AbbVie: Consultancy; Agios: Consultancy, Research Funding; Amgen: Consultancy; Argenx: Other: Data and Safety Monitoring Board; Arog: Consultancy, Research Funding; Astellas: Consultancy. Griffin:Astellas Pharma: Consultancy; Novartis Pharma: Other: Grant, Patents & Royalties: Royalties ; Analysis Group: Consultancy; Sun Pharmaceuticals: Consultancy; RXi Pharmaceuticals: Consultancy; Lilly Pharmaceuticals: Other: Grant; Myeloproliferative Neoplasia Foundation: Other: Grant . Anderson:C4 Therapeutics: Equity Ownership, Other: Scientific founder; Celgene: Consultancy; OncoPep: Equity Ownership, Other: Scientific founder; Bristol Myers Squibb: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Millennium Takeda: Consultancy.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,243
Écart entre enseignants0,228 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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