Highlight of 2023: Advances in mast cells.

In this article for the Highlights of 2023 Series, we consider the growing understanding of mast cell heterogeneity and interactions that has developed from single cell RNA sequencing studies. We also discuss novel concepts concerning mast cell interactions with the central nervous system and evidence for their role in host defense against SARS-CoV-2 infection.

Error modelled gene expression analysis (EMOGEA) provides a superior overview of time course RNA-seq measurements and low count gene expression.

Temporal RNA-sequencing (RNA-seq) studies of bulk samples provide an opportunity for improved understanding of gene regulation during dynamic phenomena such as development, tumor progression or response to an incremental dose of a pharmacotherapeutic. Moreover, single-cell RNA-seq (scRNA-seq) data implicitly exhibit temporal characteristics because gene expression values recapitulate dynamic processes such as cellular transitions. Unfortunately, temporal RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are often difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a framework for analyzing RNA-seq data that incorporates measurement uncertainty, while introducing a special formulation for those acquired to monitor dynamic phenomena. This method is specifically suited for RNA-seq studies in which low-count transcripts with small-fold changes lead to significant biological effects. Such transcripts include genes involved in signaling and non-coding RNAs that inherently exhibit low levels of expression. Using simulation studies, we show that this framework down-weights samples that exhibit extreme responses such as batch effects allowing them to be modeled with the rest of the samples and maintain the degrees of freedom originally envisioned for a study. Using temporal experimental data, we demonstrate the framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and an scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate of false negative discoveries compared to common approaches. Finally, we provide two packages in Python and R that are self-contained and easy to use, including test data.

M(R)apping RNA-Protein Interactions.

The systematic investigation of RNA-protein interactions is a key step towards a better understanding of the functions of RNA molecules.We developed an easy-to-use method to isolate and identify RNAs and proteins bound to long non-coding RNAs (lncRNAs ) in their native configuration. Similar to other methodologies, we utilize biotinylated antisense oligonucleotides (ASOs) to purify the lncRNA of interest and its associated proteins from different cellular compartments.

Integrator restrains paraspeckles assembly by promoting isoform switching of the lncRNA NEAT1.

RNA 3' end processing provides a source of transcriptome diversification which affects various (patho)-physiological processes. A prime example is the transcript isoform switch that leads to the read-through expression of the long non-coding RNA NEAT1_2, at the expense of the shorter polyadenylated transcript NEAT1_1. NEAT1_2 is required for assembly of paraspeckles (PS), nuclear bodies that protect cancer cells from oncogene-induced replication stress and chemotherapy. Searching for proteins that modulate this event, we identified factors involved in the 3' end processing of polyadenylated RNA and components of the Integrator complex. Perturbation experiments established that, by promoting the cleavage of NEAT1_2, Integrator forces NEAT1_2 to NEAT1_1 isoform switching and, thereby, restrains PS assembly. Consistently, low levels of Integrator subunits correlated with poorer prognosis of cancer patients exposed to chemotherapeutics. Our study establishes that Integrator regulates PS biogenesis and a link between Integrator, cancer biology, and chemosensitivity, which may be exploited therapeutically.

Stress-induced lncRNA LASTR fosters cancer cell fitness by regulating the activity of the U4/U6 recycling factor SART3.

Dysregulated splicing is a common event in cancer even in the absence of mutations in the core splicing machinery. The aberrant long non-coding transcriptome constitutes an uncharacterized level of regulation of post-transcriptional events in cancer. Here, we found that the stress-induced long non-coding RNA (lncRNA), LINC02657 or LASTR (lncRNA associated with SART3 regulation of splicing), is upregulated in hypoxic breast cancer and is essential for the growth of LASTR-positive triple-negative breast tumors. LASTR is upregulated in several types of epithelial cancers due to the activation of the stress-induced JNK/c-JUN pathway. Using a mass-spectrometry based approach, we identified the RNA-splicing factor SART3 as a LASTR-interacting partner. We found that LASTR promotes splicing efficiency by controlling SART3 association with the U4 and U6 small nuclear ribonucleoproteins (snRNP) during spliceosome recycling. Intron retention induced by LASTR depletion downregulates expression of essential genes, ultimately decreasing the fitness of cancer cells.

Probing Long Non-coding RNA-Protein Interactions.

Non-coding RNA sequences outnumber the protein-coding genes in the human genome, however our knowledge of their functions is still limited. RNA-binding proteins follow the transcripts, including non-coding RNAs, throughout their life, regulating not only maturation, nuclear export, stability and eventually translation, but also RNA functions. Therefore, development of sophisticated methods to study RNA-protein interactions are key to the systematic characterization of lncRNAs. Although mostly applicable to RNA-protein interactions in general, many approaches, especially the computational ones, need adjustment to be adapted to the length and complexity of lncRNA transcripts. Here we critically review all the wet lab and computational methods to study lncRNA-protein interactions and their potential to clarify the dark side of the genome.

Comparative oncogenomics identifies tyrosine kinase FES as a tumor suppressor in melanoma.

Identification and functional validation of oncogenic drivers are essential steps toward advancing cancer precision medicine. Here, we have presented a comprehensive analysis of the somatic genomic landscape of the widely used BRAFV600E- and NRASQ61K-driven mouse models of melanoma. By integrating the data with publically available genomic, epigenomic, and transcriptomic information from human clinical samples, we confirmed the importance of several genes and pathways previously implicated in human melanoma, including the tumor-suppressor genes phosphatase and tensin homolog (PTEN), cyclin dependent kinase inhibitor 2A (CDKN2A), LKB1, and others. Importantly, this approach also identified additional putative melanoma drivers with prognostic and therapeutic relevance. Surprisingly, one of these genes encodes the tyrosine kinase FES. Whereas FES is highly expressed in normal human melanocytes, FES expression is strongly decreased in over 30% of human melanomas. This downregulation correlates with poor overall survival. Correspondingly, engineered deletion of Fes accelerated tumor progression in a BRAFV600E-driven mouse model of melanoma. Together, these data implicate FES as a driver of melanoma progression and demonstrate the potential of cross-species oncogenomic approaches combined with mouse modeling to uncover impactful mutations and oncogenic driver alleles with clinical importance in the treatment of human cancer.

p53 induces formation of NEAT1 lncRNA-containing paraspeckles that modulate replication stress response and chemosensitivity.

In a search for mediators of the p53 tumor suppressor pathway, which induces pleiotropic and often antagonistic cellular responses, we identified the long noncoding RNA (lncRNA) NEAT1. NEAT1 is an essential architectural component of paraspeckle nuclear bodies, whose pathophysiological relevance remains unclear. Activation of p53, pharmacologically or by oncogene-induced replication stress, stimulated the formation of paraspeckles in mouse and human cells. Silencing Neat1 expression in mice, which prevents paraspeckle formation, sensitized preneoplastic cells to DNA-damage-induced cell death and impaired skin tumorigenesis. We provide mechanistic evidence that NEAT1 promotes ATR signaling in response to replication stress and is thereby engaged in a negative feedback loop that attenuates oncogene-dependent activation of p53. NEAT1 targeting in established human cancer cell lines induced synthetic lethality with genotoxic chemotherapeutics, including PARP inhibitors, and nongenotoxic activation of p53. This study establishes a key genetic link between NEAT1 paraspeckles, p53 biology and tumorigenesis and identifies NEAT1 as a promising target to enhance sensitivity of cancer cells to both chemotherapy and p53 reactivation therapy.