DHX15 is involved in SUGP1-mediated RNA missplicing by mutant SF3B1 in cancer

Zhang, Jian; Huang, Jianing; Xu, Ke; Xing, Peiqi; Huang, Yue; Liu, Zhaoqi; Tong, Liang; Manley, James L.

doi:10.1073/pnas.2216712119

Cited by 23 publications

(21 citation statements)

References 54 publications

(107 reference statements)

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“…6 A; Supplemental Movie S1 ). Strikingly, as shown in Figure 6 B, the predicted DHX15–SUGP1 G-patch interface in this trimeric structure aligns nearly perfectly (0.58 Å RMSD) with that in the crystal structure of the DHX15–SUGP1 G-patch complex (PDB ID 8EJM) that we experimentally determined in our recent study ( Zhang et al 2022a ). Concurrently, the SF3B1–SUGP1 interface in the predicted trimer remains almost the same (0.73 Å RMSD) as in the predicted SF3B1–SUGP1 heterodimer that we biochemically characterized above ( Fig.…”

Section: Resultssupporting

confidence: 71%

“…Pan-cancer analyses of >10,000 samples in TCGA also revealed a number of SUGP1 cancer-associated mutations flanking the SUGP1 G-patch motif (G-patch) that partially recapitulate mutant SF3B1 missplicing, suggesting a mechanistic link between SF3B1 and SUGP1 ( Liu et al 2020 ; Alsafadi et al 2021 ). The G-patch is especially important in mediating SUGP1 function in accurate 3′ss selection ( Zhang et al 2019a ) and functions via an activating interaction with DEAH-box helicase 15 (DHX15) ( Zhang et al 2022a ; Beusch et al 2023 ; Feng et al 2023 ). However, how SF3B1 and SUGP1 interact is not known, leaving gaps in our understanding of both how branch sites and 3′ splice sites are correctly selected and how cancer mutations disrupt this process.…”

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confidence: 99%

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Characterization of the SF3B1–SUGP1 interface reveals how numerous cancer mutations cause mRNA missplicing

Zhang,

Xie,

Huang

et al. 2023

Genes Dev.

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The spliceosomal geneSF3B1is frequently mutated in cancer. While it is known thatSF3B1hotspot mutations lead to loss of splicing factor SUGP1 from spliceosomes, the cancer-relevant SF3B1–SUGP1 interaction has not been characterized. To address this issue, we show by structural modeling that two regions flanking the SUGP1 G-patch make numerous contacts with the region of SF3B1 harboring hotspot mutations. Experiments confirmed that all the cancer-associated mutations in these regions, as well as mutations affecting other residues in the SF3B1–SUGP1 interface, not only weaken or disrupt the interaction but also alter splicing similarly toSF3B1cancer mutations. Finally, structural modeling of a trimeric protein complex reveals that the SF3B1–SUGP1 interaction “loops out” the G-patch for interaction with the helicase DHX15. Our study thus provides an unprecedented molecular view of a protein complex essential for accurate splicing and also reveals that numerous cancer-associated mutations disrupt the critical SF3B1–SUGP1 interaction.

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Section: Resultssupporting

confidence: 71%

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confidence: 99%

Characterization of the SF3B1–SUGP1 interface reveals how numerous cancer mutations cause mRNA missplicing

Zhang,

Xie,

Huang

et al. 2023

Genes Dev.

Self Cite

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“…However, effective drugging of mutant SF3B1 remains elusive owing to the poor differential sensitivity between SF3B1 mutated and unmutated cancers and high level of cytotoxicities 53,54 . One possible explanation is presence or absence of additional cofactors that coordinate splicing with mutant spliceosome; For example, mutation on SF3B1 attenuates recruitment of SUGP1, a G-PATCH protein, that recruits DHX15 to surveil and quality control splicing under suboptimal conditions thereby potentiating cryptic splicing 55,56 . Advances in cryo-EM technologies have started to elucidate the dynamics of the early splicing where faithful branchpoint selection is made through several high-resolution structures of early spliceosome 57–60 .…”

Section: Discussionmentioning

confidence: 99%

Engineering oncogenic hotspot mutations onSF3B1via CRISPR-directed PRECIS mutagenesis

Fernandez,

Jia,

et al. 2024

Preprint

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SF3B1is the most recurrently mutated RNA splicing factor in cancer; However, its study has been hindered by a lack of disease-relevant cell line models. Here, we compared four genome engineering platforms to establishSF3B1mutant cell lines: CRISPR-Cas9 editing, AAV HDR editing, base editing (ABEmax, ABE8e), and prime editing (PE2, PE3, PE5Max). We showed that prime editing via PE5max achieved the most efficientSF3B1K700E editing across a wide range of cell lines. We further refined our approach by coupling prime editing with a with a fluorescent reporter that leverages aSF3B1mutation-responsive synthetic intron to mark prime edited cells. Calling this approach prime editing coupled intron-assisted selection (PRECIS), we then introduced the K700E hotspot mutation into two chronic lymphocytic leukemia (CLL) cell lines, HG-3 and MEC-1, and demonstrated that our PRECIS-engineered cells faithfully recapitulate the altered splicing and copy number variation (CNV) events frequently found in CLL patients withSF3B1mutation. Our results showcase PRECIS as an efficient and generalizable method for engineering genetically faithfulSF3B1mutant models, shed new light on the role ofSF3B1mutation in cancer biology, and enables generation of novelSF3B1mutant cell lines in any cellular context.

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“…Mutations in the SF3B1 splicing factor gene can disrupt the interaction between the splicing factor and SUGP1, leading to splicing errors that can cause cancer. DHX15, as an RNA helicase, is required for SUGP1 and participates in RNA error mediated by SF1B3 in cancer ( Zhang et al, 2022 ). DHX15 forms aggregates with NLRP6 inflammasomes and dsRNA, which participates in host defenses ( Shen C. et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of common genes and pathways between type 2 diabetes and COVID-19

Wang,

Li,

et al. 2024

Front. Genet.

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Background:Numerous studies have reported a high incidence and risk of severe illness due to coronavirus disease 2019 (COVID-19) in patients with type 2 diabetes (T2DM). COVID-19 patients may experience elevated or decreased blood sugar levels and may even develop diabetes. However, the molecular mechanisms linking these two diseases remain unclear. This study aimed to identify the common genes and pathways between T2DM and COVID-19.Methods:Two public datasets from the Gene Expression Omnibus (GEO) database (GSE95849 and GSE164805) were analyzed to identify differentially expressed genes (DEGs) in blood between people with and without T2DM and COVID-19. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the common DEGs. A protein-protein interaction (PPI) network was constructed to identify common genes, and their diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. Validation was performed on the GSE213313 and GSE15932 datasets. A gene co-expression network was constructed using the GeneMANIA database to explore interactions among core DEGs and their co-expressed genes. Finally, a microRNA (miRNA)-transcription factor (TF)-messenger RNA (mRNA) regulatory network was constructed based on the common feature genes.Results:In the GSE95849 and GSE164805 datasets, 81 upregulated genes and 140 downregulated genes were identified. GO and KEGG enrichment analyses revealed that these DEGs were closely related to the negative regulation of phosphate metabolic processes, the positive regulation of mitotic nuclear division, T-cell co-stimulation, and lymphocyte co-stimulation. Four upregulated common genes (DHX15, USP14, COPS3, TYK2) and one downregulated common feature gene (RIOK2) were identified and showed good diagnostic accuracy for T2DM and COVID-19. The AUC values of DHX15, USP14, COPS3, TYK2, and RIOK2 in T2DM diagnosis were 0.931, 0.917, 0.986, 0.903, and 0.917, respectively. In COVID-19 diagnosis, the AUC values were 0.960, 0.860, 1.0, 0.9, and 0.90, respectively. Validation in the GSE213313 and GSE15932 datasets confirmed these results. The miRNA-TF-mRNA regulatory network showed that TYH2 was targeted by PITX1, PITX2, CRX, NFYA, SREBF1, RELB, NR1L2, and CEBP, whereas miR-124-3p regulates THK2, RIOK2, and USP14.Conclusion:We identified five common feature genes (DHX15, USP14, COPS3, TYK2, and RIOK2) and their co-regulatory pathways between T2DM and COVID-19, which may provide new insights for further molecular mechanism studies.

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DHX15 is involved in SUGP1-mediated RNA missplicing by mutant SF3B1 in cancer

Cited by 23 publications

References 54 publications

Characterization of the SF3B1–SUGP1 interface reveals how numerous cancer mutations cause mRNA missplicing

Characterization of the SF3B1–SUGP1 interface reveals how numerous cancer mutations cause mRNA missplicing

Engineering oncogenic hotspot mutations onSF3B1via CRISPR-directed PRECIS mutagenesis

Identification of common genes and pathways between type 2 diabetes and COVID-19

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