Perturbations in <i>HNRNPH1</i> Splicing and Abundance Affect Global Splicing and Proliferation in Mantle Cell Lymphoma
Bibliographic record
Abstract
Objectives Mantle cell lymphoma (MCL) is an uncommon B-cell non-Hodgkin lymphoma that is incurable with standard therapies. The genetic drivers of this cancer have not been firmly established and the features known to contribute to differences in clinical course remain limited. We previously discovered non-coding and silent mutations in HNRNPH1 that affect its splicing and contribute to poor outcomes for patients with MCL. We sought to extend our understanding of the mechanisms by which HNRNPH1 contributes to MCL pathology using a combination of in vitro models and integrative analysis of RNA sequencing from MCL tumors. Methods We previously sequenced ribosomal RNA-depleted RNA from 130 MCL tumors. Based on our earlier identification of mutations in HNRNPH1 and altered splicing of this gene, we performed differential splicing analyses using rMATS and leafcutter. We investigated the functional and phenotypic effect of deregulated hnRNP H1 protein through siRNA knockdown. Results Our previous work identified that splicing of HNRNPH1, and not total mRNA expression, correlated with protein abundance in MCL tumors. As a result, our analysis of alternative splicing focused on events associated with altered splicing of HNRNPH1. We identified 155 unique alternative splicing events (ΔPSI > 0.1, FDR < 0.1). Gene ontology analysis identified various aspects of RNA processing which are significantly enriched within this gene list, including mRNA splicing, transport, and metabolic process. This nominates HNRNPH1 as part of the complex network controlling alternative splicing within MCL. Available CLIP-seq in HeLa cells provides evidence for direct interactions between hnRNP H1 and transcripts identified by our analysis (e.g. RBM25, EIF4A1, HNRNPA2B1). Of the 155 events we identified, more than half involved retained introns. Generally, retained introns result in non-productive RNA species, which indicates that this program of intron retention in MCL is a mechanism by which protein abundance can be regulated by hnRNP H1. For all cases with available Mantle Cell Lymphoma International Prognostic Indicator (MIPI) classification, we determined the splicing ratio for HNRNPH1 and observed a general association between high MIPI scores and a lower ratio of non-productive HNRNPH1 transcripts. This suggested that the increased hnRNP H1 abundance we observed in HNRNPH1-mutant tumors contributes to increased proliferation of MCL cells. We verified this in vitro with siRNA knockdown of HNRNPH1 in HEK cells, which resulted in a significant decrease in cell proliferation. Conclusions We have described a pattern of alternative splicing in MCL that is associated with alterations in HNRNPH1 splicing and related protein abundance. The prevalence of retained introns suggests that hnRNP H1 regulates the abundance of protein-coding transcripts via alternative splicing coupled to nonsense-mediated decay. We continue to explore targets of hnRNP H1, a novel oncoprotein in MCL. Disclosures Morin: Celgene: Consultancy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".