Alternative Splicing in Myeloid Malignancies

Correlations of Long Noncoding RNA HNF4A-AS1 Alternative Transcripts with Liver Diseases and Drug Metabolism

Hepatocyte nuclear factor 4 alpha antisense 1 (HNF4A-AS1) is a long noncoding RNA (lncRNA) gene physically located next to the transcription factor HNF4A gene in the human genome. Its transcription products have been reported to inhibit the progression of hepatocellular carcinoma (HCC) and negatively regulate the expression of cytochrome P450s (CYPs), including CYP1A2, 2B6, 2C9, 2C19, 2E1, and 3A4. By altering CYP expression, lncRNA HNF4A-AS1 also contributes to the susceptibility of drug-induced liver injury. Thus, HNF4A-AS1 lncRNA is a promising target for controlling HCC and modulating drug metabolism. However, HNF4A-AS1 has four annotated alternative transcripts in the human genome browsers, and it is unclear which transcripts the small interfering RNAs or small hairpin RNAs used in the previous studies are silenced and which transcripts should be used as the target. In this study, four annotated and two newly identified transcripts were confirmed. These six transcripts showed different expression levels in different liver disease conditions, including metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, and obesity. The expression patterns of all HNF4A-AS1 transcripts were further investigated in liver cell growth from human embryonic stem cells to matured hepatocyte-like cells, HepaRG differentiation, and exposure to rifampicin treatment. Several HNF4A-AS1 transcripts highly displayed correlations with these situations. In addition, some of the HNF4A-AS1 transcripts also showed a strong correlation with CYP3A4 during HepaRG maturation and rifampicin exposure. Our findings provide valuable insights into the specific roles of HNF4A-AS1 transcripts, paving the way for more targeted therapeutic strategies for liver diseases and drug metabolism. SIGNIFICANCE STATEMENT: This study explores the alternative transcripts of HNF4A-AS1, showing how their expression changes in different biological conditions, from various liver diseases to the growth and differentiation of hepatocytes and drug metabolism. The generated knowledge is essential for understanding the independent roles of different transcripts from the same lncRNA in different liver diseases and drug metabolism situations.

Targeting SNRNP200-induced splicing dysregulation offers an immunotherapy opportunity for glycolytic triple-negative breast cancer

Metabolic dysregulation is prominent in triple-negative breast cancer (TNBC), yet therapeutic strategies targeting cancer metabolism are limited. Here, utilizing multiomics data from our TNBC cohort (n = 465), we demonstrated widespread splicing deregulation and increased spliceosome abundance in the glycolytic TNBC subtype. We identified SNRNP200 as a crucial mediator of glucose-driven metabolic reprogramming. Mechanistically, glucose induces acetylation at SNRNP200 K1610, preventing its proteasomal degradation. Augmented SNRNP200 then facilitates splicing key metabolic enzyme-encoding genes (GAPDH, ALDOA, and GSS), leading to increased lactic acid and glutathione production. Targeting SNRNP200 with antisense oligonucleotide therapy impedes tumor metabolism and enhances the efficacy of anti-PD-1 therapy by activating intratumoral CD8+ T cells while suppressing regulatory T cells. Clinically, higher SNRNP200 levels indicate an inferior response to immunotherapy in glycolytic TNBCs. Overall, our study revealed the intricate interplay between RNA splicing and metabolic dysregulation, suggesting an innovative combination strategy for immunotherapy in glycolytic TNBCs.

The eukaryotic translation initiation factor eIF4E unexpectedly acts in splicing thereby coupling mRNA processing with translation: eIF4E induces widescale splicing reprogramming providing system-wide connectivity between splicing, nuclear mRNA export and translation

Recent findings position the eukaryotic translation initiation factor eIF4E as a novel modulator of mRNA splicing, a process that impacts the form and function of resultant proteins. eIF4E physically interacts with the spliceosome and with some intron-containing transcripts implying a direct role in some splicing events. Moreover, eIF4E drives the production of key components of the splicing machinery underpinning larger scale impacts on splicing. These drive eIF4E-dependent reprogramming of the splicing signature. This work completes a series of studies demonstrating eIF4E acts in all the major mRNA maturation steps whereby eIF4E drives production of the RNA processing machinery and escorts some transcripts through various maturation steps. In this way, eIF4E couples the mRNA processing-export-translation axis linking nuclear mRNA processing to cytoplasmic translation. eIF4E elevation is linked to worse outcomes in acute myeloid leukemia patients where these activities are dysregulated. Understanding these effects provides new insight into post-transcriptional control and eIF4E-driven cancers.

The physiology of alternative splicing

Alternative splicing is a substantial contributor to the high complexity of transcriptomes of multicellular eukaryotes. In this Review, we discuss the accumulated evidence that most of this complexity is reflected at the protein level and fundamentally shapes the physiology and pathology of organisms. This notion is supported not only by genome-wide analyses but, mainly, by detailed studies showing that global and gene-specific modulations of alternative splicing regulate highly diverse processes such as tissue-specific and species-specific cell differentiation, thermal regulation, neuron self-avoidance, infrared sensing, the Warburg effect, maintenance of telomere length, cancer and autism spectrum disorders (ASD). We also discuss how mastering the control of alternative splicing paved the way to clinically approved therapies for hereditary diseases.

Alternative splicing in ABA signaling during seed germination

Seed germination is an essential step in a plant's life cycle. It is controlled by complex physiological, biochemical, and molecular mechanisms and external factors. Alternative splicing (AS) is a co-transcriptional mechanism that regulates gene expression and produces multiple mRNA variants from a single gene to modulate transcriptome diversity. However, little is known about the effect of AS on the function of generated protein isoforms. The latest reports indicate that alternative splicing (AS), the relevant mechanism controlling gene expression, plays a significant role in abscisic acid (ABA) signaling. In this review, we present the current state of the art about the identified AS regulators and the ABA-related changes in AS during seed germination. We show how they are connected with the ABA signaling and the seed germination process. We also discuss changes in the structure of the generated AS isoforms and their impact on the functionality of the generated proteins. Also, we point out that the advances in sequencing technology allow for a better explanation of the role of AS in gene regulation by more accurate detection of AS events and identification of full-length splicing isoforms.

Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes

Myelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeutic implications. The current abundance of molecular information poses the challenges of precisely defining patients' molecular profiles and their incorporation in clinically established diagnostic and prognostic schemes. Perhaps the prognostic power of the current systems can be boosted by incorporating molecular features. Machine learning (ML) algorithms can be helpful in developing more precise prognostication models that integrate complex genomic interactions at a higher dimensional level. These techniques can potentially generate automated diagnostic and prognostic models and assist in advancing personalized therapies. This review highlights the current prognostication models used in MDS while shedding light on the latest achievements in ML-based research.