Poison Exon Splicing Regulates a Coordinated Network of SR Protein Expression during Differentiation and Tumorigenesis.

The RNA isoform repertoire is regulated by splicing factor (SF) expression, and alterations in SF levels are associated with disease. SFs contain ultraconserved poison exon (PE) sequences that exhibit greater identity across species than nearby coding exons, but their physiological role and molecular regulation is incompletely understood. We show that PEs in serine-arginine-rich (SR) proteins, a family of 14 essential SFs, are differentially spliced during induced pluripotent stem cell (iPSC) differentiation and in tumors versus normal tissues. We uncover an extensive cross-regulatory network of SR proteins controlling their expression via alternative splicing coupled to nonsense-mediated decay. We define sequences that regulate PE inclusion and protein expression of the oncogenic SF TRA2ß using an RNA-targeting CRISPR screen. We demonstrate location dependency of RS domain activity on regulation of TRA2ß-PE using CRISPR artificial SFs. Finally, we develop splice-switching antisense oligonucleotides to reverse the increased skipping of TRA2ß-PE detected in breast tumors, altering breast cancer cell viability, proliferation, and migration.

Fractionation iCLIP detects persistent SR protein binding to conserved, retained introns in chromatin, nucleoplasm and cytoplasm.

RNA binding proteins (RBPs) regulate the lives of all RNAs from transcription, processing, and function to decay. How RNA-protein interactions change over time and space to support these roles is poorly understood. Towards this end, we sought to determine how two SR proteins-SRSF3 and SRSF7, regulators of pre-mRNA splicing, nuclear export and translation-interact with RNA in different cellular compartments. To do so, we developed Fractionation iCLIP (Fr-iCLIP), in which chromatin, nucleoplasmic and cytoplasmic fractions are prepared from UV-crosslinked cells and then subjected to iCLIP. As expected, SRSF3 and SRSF7 targets were detected in all fractions, with intron, snoRNA and lncRNA interactions enriched in the nucleus. Cytoplasmically-bound mRNAs reflected distinct functional groupings, suggesting coordinated translation regulation. Surprisingly, hundreds of cytoplasmic intron targets were detected. These cytoplasmic introns were found to be highly conserved and introduced premature termination codons into coding regions. However, many intron-retained mRNAs were not substrates for nonsense-mediated decay (NMD), even though they were detected in polysomes. These findings suggest that intron-retained mRNAs in the cytoplasm have previously uncharacterized functions and/or escape surveillance. Hence, Fr-iCLIP detects the cellular location of RNA-protein interactions and provides insight into co-transcriptional, post-transcriptional and cytoplasmic RBP functions for coding and non-coding RNAs.

Systems biology approach to the dissection of the complexity of regulatory networks in the S. scrofa cardiocirculatory system.

Genome-wide experiments are routinely used to increase the understanding of the biological processes involved in the development and maintenance of a variety of pathologies. Although the technical feasibility of this type of experiment has improved in recent years, data analysis remains challenging. In this context, gene set analysis has emerged as a fundamental tool for the interpretation of the results. Here, we review strategies used in the gene set approach, and using datasets for the pig cardiocirculatory system as a case study, we demonstrate how the use of a combination of these strategies can enhance the interpretation of results. Gene set analyses are able to distinguish vessels from the heart and arteries from veins in a manner that is consistent with the different cellular composition of smooth muscle cells. By integrating microRNA elements in the regulatory circuits identified, we find that vessel specificity is maintained through specific miRNAs, such as miR-133a and miR-143, which show anti-correlated expression with their mRNA targets.

Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology.

Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on a manually annotated dataset of 3,862 cells from 36 organoids confocally imaged at 5 µm z-resolution. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivities. Cellos was able to accurately distinguish ratios of distinct fluorescently labelled cell populations in organoids, with ≤3% deviation from the seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent DAPI stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantifications by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclear morphology feature changes associated with treatment. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer cells beyond what arises from local cell division or organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos.

Three-dimensional (3D) organoid cultures are flexible systems to interrogate cellular growth, morphology, multicellular spatial architecture, and cellular interactions in response to treatment. However, computational methods for analysis of 3D organoids with sufficiently high-throughput and cellular resolution are needed. Here we report Cellos, an accurate, high-throughput pipeline for 3D organoid segmentation using classical algorithms and nuclear segmentation using a trained Stardist-3D convolutional neural network. To evaluate Cellos, we analyze ~100,000 organoids with ~2.35 million cells from multiple treatment experiments. Cellos segments dye-stained or fluorescently-labeled nuclei and accurately distinguishes distinct labeled cell populations within organoids. Cellos can recapitulate traditional luminescence-based drug response of cells with complex drug sensitivities, while also quantifying changes in organoid and nuclear morphologies caused by treatment as well as cell-cell spatial relationships that reflect ecological affinity. Cellos provides powerful tools to perform high-throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

MYC regulates a pan-cancer network of co-expressed oncogenic splicing factors.

MYC is dysregulated in >50% of cancers, but direct targeting of MYC has been clinically unsuccessful. Targeting downstream MYC effector pathways represents an attractive alternative. MYC regulates alternative mRNA splicing, but the mechanistic links between MYC and the splicing machinery in cancer remain underexplored. Here, we identify a network of co-expressed splicing factors (SF-modules) in MYC-active breast tumors. Of these, one is a pan-cancer SF-module correlating with MYC activity across 33 tumor types. In mammary cell models, MYC activation leads to co-upregulation of pan-cancer module SFs and to changes in >4,000 splicing events. In breast cancer organoids, co-overexpression of the pan-cancer SF-module induces MYC-regulated splicing events and increases organoid size and invasiveness, while knockdown decreases organoid size. Finally, we uncover a MYC-activity pan-cancer splicing signature correlating with survival across tumor types. Our findings provide insight into the mechanisms of MYC-regulated splicing and for the development of therapeutics for MYC-driven tumors.

Differential Functions of Splicing Factors in Mammary Transformation and Breast Cancer Metastasis.

Misregulation of alternative splicing is a hallmark of human tumors, yet to what extent and how it contributes to malignancy are only beginning to be unraveled. Here, we define which members of the splicing factor SR and SR-like families contribute to breast cancer and uncover differences and redundancies in their targets and biological functions. We identify splicing factors frequently altered in human breast tumors and assay their oncogenic functions using breast organoid models. We demonstrate that not all splicing factors affect mammary tumorigenesis in MCF-10A cells. Specifically, the upregulation of SRSF4, SRSF6, or TRA2ß disrupts acinar morphogenesis and promotes cell proliferation and invasion in MCF-10A cells. By characterizing the targets of these oncogenic splicing factors, we identify shared spliced isoforms associated with well-established cancer hallmarks. Finally, we demonstrate that TRA2ß is regulated by the MYC oncogene, plays a role in metastasis maintenance in vivo, and its levels correlate with breast cancer patient survival.

Tissue-specific expression and regulatory networks of pig microRNAome.

BACKGROUND: Despite the economic and medical importance of the pig, knowledge about its genome organization, gene expression regulation, and molecular mechanisms involved in physiological processes is far from that achieved for mouse and rat, the two most used model organisms in biomedical research. MicroRNAs (miRNAs) are a wide class of molecules that exert a recognized role in gene expression modulation, but only 280 miRNAs in pig have been characterized to date. RESULTS: We applied a novel computational approach to predict species-specific and conserved miRNAs in the pig genome, which were then subjected to experimental validation. We experimentally identified candidate miRNAs sequences grouped in high-confidence (424) and medium-confidence (353) miRNAs according to RNA-seq results. A group of miRNAs was also validated by PCR experiments. We established the subtle variability in expression of isomiRs and miRNA-miRNA star couples supporting a biological function for these molecules. Finally, miRNA and mRNA expression profiles produced from the same sample of 20 different tissue of the animal were combined, using a correlation threshold to filter miRNA-target predictions, to identify tissue-specific regulatory networks. CONCLUSIONS: Our data represent a significant progress in the current understanding of miRNAome in pig. The identification of miRNAs, their target mRNAs, and the construction of regulatory circuits will provide new insights into the complex biological networks in several tissues of this important animal model.

Counting on co-transcriptional splicing.

Splicing is the removal of intron sequences from pre-mRNA by the spliceosome. Researchers working in multiple model organisms - notably yeast, insects and mammalian cells - have shown that pre-mRNA can be spliced during the process of transcription (i.e. co-transcriptionally), as well as after transcription termination (i.e. post-transcriptionally). Co-transcriptional splicing does not assume that transcription and splicing machineries are mechanistically coupled, yet it raises this possibility. Early studies were based on a limited number of genes, which were often chosen because of their experimental accessibility. Since 2010, eight studies have used global datasets as counting tools, in order to quantify co-transcriptional intron removal. The consensus view, based on four organisms, is that the majority of splicing events take place co-transcriptionally in most cells and tissues. Here, we discuss the nature of the various global datasets and how bioinformatic analyses were conducted. Considering the broad differences in experimental approach and analysis, the level of agreement on the prevalence of co-transcriptional splicing is remarkable.