Paraspeckle-independent co-transcriptional regulation of nuclear microRNA biogenesis by SFPQ.

MicroRNAs (miRNAs) play crucial roles in physiological functions and disease, but the regulation of their nuclear biogenesis remains poorly understood. Here, BioID on Drosha, the catalytic subunit of the microprocessor complex, reveals its proximity to splicing factor proline- and glutamine (Q)-rich (SFPQ), a multifunctional RNA-binding protein (RBP) involved in forming paraspeckle nuclear condensates. SFPQ depletion impacts both primary and mature miRNA expression, while other paraspeckle proteins (PSPs) or the paraspeckle scaffolding RNA NEAT1 do not, indicating a paraspeckle-independent role. Comprehensive transcriptomic analyses show that SFPQ loss broadly affects RNAs and miRNA host gene (HG) expression, influencing both their transcription and the stability of their products. Notably, SFPQ protects the oncogenic miR-17∼92 polycistron from degradation by the nuclear exosome targeting (NEXT)-exosome complex and is tightly linked with its overexpression across a broad variety of cancers. Our findings reveal a dual role for SFPQ in regulating miRNA HG transcription and stability, as well as its significance in cancers.

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data.

Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells.

The E3 ubiquitin ligase Nedd4L preserves skeletal muscle stem cell quiescence by inhibiting their activation.

Adult stem cells play a critical role in tissue repair and maintenance. In tissues with slow turnover, including skeletal muscle, these cells are maintained in a mitotically quiescent state yet remain poised to re-enter the cell cycle to replenish themselves and regenerate the tissue. Using a panomics approach we show that the PAX7/NEDD4L axis acts against muscle stem cell activation in homeostatic skeletal muscle. Our findings suggest that PAX7 transcriptionally activates the E3 ubiquitin ligase Nedd4L and that the conditional genetic deletion of Nedd4L impairs muscle stem cell quiescence, with an upregulation of cell cycle and myogenic differentiation genes. Loss of Nedd4L in muscle stem cells results in the expression of doublecortin (DCX), which is exclusively expressed during their in vivo activation. Together, these data establish that the ubiquitin proteasome system, mediated by Nedd4L, is a key contributor to the muscle stem cell quiescent state in adult mice.

Accurate isoform quantification by joint short- and long-read RNA-sequencing.

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals.

MOTIVATION: Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. RESULTS: We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. AVAILABILITY AND IMPLEMENTATION: SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

PHF6-mediated transcriptional control of NSC via Ephrin receptors is impaired in the intellectual disability syndrome BFLS.

The plant homeodomain zinc-finger protein, PHF6, is a transcriptional regulator, and PHF6 germline mutations cause the X-linked intellectual disability (XLID) Börjeson-Forssman-Lehmann syndrome (BFLS). The mechanisms by which PHF6 regulates transcription and how its mutations cause BFLS remain poorly characterized. Here, we show genome-wide binding of PHF6 in the developing cortex in the vicinity of genes involved in central nervous system development and neurogenesis. Characterization of BFLS mice harbouring PHF6 patient mutations reveals an increase in embryonic neural stem cell (eNSC) self-renewal and a reduction of neural progenitors. We identify a panel of Ephrin receptors (EphRs) as direct transcriptional targets of PHF6. Mechanistically, we show that PHF6 regulation of EphR is impaired in BFLS mice and in conditional Phf6 knock-out mice. Knockdown of EphR-A phenocopies the PHF6 loss-of-function defects in altering eNSCs, and its forced expression rescues defects of BFLS mice-derived eNSCs. Our data indicate that PHF6 directly promotes Ephrin receptor expression to control eNSC behaviour in the developing brain, and that this pathway is impaired in BFLS.

SparsePro: An efficient fine-mapping method integrating summary statistics and functional annotations.

Identifying causal variants from genome-wide association studies (GWAS) is challenging due to widespread linkage disequilibrium (LD) and the possible existence of multiple causal variants in the same genomic locus. Functional annotations of the genome may help to prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. Classical fine-mapping methods conducting an exhaustive search of variant-level causal configurations have a high computational cost, especially when the underlying genetic architecture and LD patterns are complex. SuSiE provided an iterative Bayesian stepwise selection algorithm for efficient fine-mapping. In this work, we build connections between SuSiE and a paired mean field variational inference algorithm through the implementation of a sparse projection, and propose effective strategies for estimating hyperparameters and summarizing posterior probabilities. Moreover, we incorporate functional annotations into fine-mapping by jointly estimating enrichment weights to derive functionally-informed priors. We evaluate the performance of SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved improved power for fine-mapping with reduced computation time. We demonstrate the utility of SparsePro through fine-mapping of five functional biomarkers of clinically relevant phenotypes. In summary, we have developed an efficient fine-mapping method for integrating summary statistics and functional annotations. Our method can have wide utility in understanding the genetics of complex traits and increasing the yield of functional follow-up studies of GWAS. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro.

Pyruvate Kinase M (PKM) binds ribosomes in a poly-ADP ribosylation dependent manner to induce translational stalling.

In light of the numerous studies identifying post-transcriptional regulators on the surface of the endoplasmic reticulum (ER), we asked whether there are factors that regulate compartment specific mRNA translation in human cells. Using a proteomic survey of spatially regulated polysome interacting proteins, we identified the glycolytic enzyme Pyruvate Kinase M (PKM) as a cytosolic (i.e. ER-excluded) polysome interactor and investigated how it influences mRNA translation. We discovered that the PKM-polysome interaction is directly regulated by ADP levels-providing a link between carbohydrate metabolism and mRNA translation. By performing enhanced crosslinking immunoprecipitation-sequencing (eCLIP-seq), we found that PKM crosslinks to mRNA sequences that are immediately downstream of regions that encode lysine- and glutamate-enriched tracts. Using ribosome footprint protection sequencing, we found that PKM binding to ribosomes causes translational stalling near lysine and glutamate encoding sequences. Lastly, we observed that PKM recruitment to polysomes is dependent on poly-ADP ribosylation activity (PARylation)-and may depend on co-translational PARylation of lysine and glutamate residues of nascent polypeptide chains. Overall, our study uncovers a novel role for PKM in post-transcriptional gene regulation, linking cellular metabolism and mRNA translation.

RNA-binding proteins that lack canonical RNA-binding domains are rarely sequence-specific.

Thousands of RNA-binding proteins (RBPs) crosslink to cellular mRNA. Among these are numerous unconventional RBPs (ucRBPs)-proteins that associate with RNA but lack known RNA-binding domains (RBDs). The vast majority of ucRBPs have uncharacterized RNA-binding specificities. We analyzed 492 human ucRBPs for intrinsic RNA-binding in vitro and identified 23 that bind specific RNA sequences. Most (17/23), including 8 ribosomal proteins, were previously associated with RNA-related function. We identified the RBDs responsible for sequence-specific RNA-binding for several of these 23 ucRBPs and surveyed whether corresponding domains from homologous proteins also display RNA sequence specificity. CCHC-zf domains from seven human proteins recognized specific RNA motifs, indicating that this is a major class of RBD. For Nudix, HABP4, TPR, RanBP2-zf, and L7Ae domains, however, only isolated members or closely related homologs yielded motifs, consistent with RNA-binding as a derived function. The lack of sequence specificity for most ucRBPs is striking, and we suggest that many may function analogously to chromatin factors, which often crosslink efficiently to cellular DNA, presumably via indirect recruitment. Finally, we show that ucRBPs tend to be highly abundant proteins and suggest their identification in RNA interactome capture studies could also result from weak nonspecific interactions with RNA.

Transcriptional reprogramming of skeletal muscle stem cells by the niche environment.

Adult stem cells are indispensable for tissue regeneration, but their function declines with age. The niche environment in which the stem cells reside plays a critical role in their function. However, quantification of the niche effect on stem cell function is lacking. Using muscle stem cells (MuSC) as a model, we show that aging leads to a significant transcriptomic shift in their subpopulations accompanied by locus-specific gain and loss of chromatin accessibility and DNA methylation. By combining in vivo MuSC transplantation and computational methods, we show that the expression of approximately half of all age-altered genes in MuSCs from aged male mice can be restored by exposure to a young niche environment. While there is a correlation between gene reversibility and epigenetic alterations, restoration of gene expression occurs primarily at the level of transcription. The stem cell niche environment therefore represents an important therapeutic target to enhance tissue regeneration in aging.

Bayesian threshold analysis of litter size in sheep

Background: Litter size at birth (LSB) is one of the most important economic traits in sheep and could be used in genetic improvement schemes for meat production. LSB is inherently a categorical trait and should be analysed with threshold models. Objective: Bayesian threshold models were used to analyze sheep LSB to estimate genetic parameters. Methods: Data was based on 7,901 LSB records from 14,968 dams and 682 sires collected from 1986 to 2012 at Makouie Sheep Breeding Station in Iran. Means of posterior distributions (MPDs) of LSB's genetic parameters were estimated, and the best-fitted models were selected using the deviance information criterion. Results: In the repeated measurement analysis, the estimated direct and maternal heritabilities, and permanent environmental effect (±SE), according to the best-fitted model (model 5), were 0.01 (0.010), 0.02 (0.014), and 0.01 (0.011), respectively. In the univariate analysis, the best estimates of direct and maternal heritabilities were 0.12 (0.064) and 0.08 (0.045), respectively. An increasing trend for direct and maternal heritabilities was observed in parity 2 (0.15 (0.082) and 0.25 (0.083), respectively). In the bivariate analysis, the best estimates of direct and maternal heritabilities for LSB were 0.03 (0.027) and 0.22 (0.041), respectively. The direct and maternal genetic correlations among parities were 0.25 (0.054) and 0.12 (0.021), respectively. Conclusions: The results showed a considerable influence of environmental factors on LSB in each parity of sheep; also, statistically different genetic parameters (p<0.05) were obtained from one parity to another, indicating the different and large influences of genetic and environmental factors for each parity.

Antecedentes: El tamaño de la camada al nacer (LSB) es inherentemente un rasgo categórico y debe analizarse con modelos de umbral. El LSB es uno de los rasgos de producción de carne más importantes en las ovejas y podría usarse en esquemas de mejora genética para la producción de carne. Objetivo: Se utilizaron modelos de umbral bayesiano para analizar el tamaño de la camada de ovejas al nacer (LSB) y estimar parámetros genéticos. Métodos: Los datos se basaron en 7.901 registros de LSB de 14.968 ovejas y 682 carneros recolectados de 1986 a 2012 en la estación de cría de ovejas Makouie en Irán. Se estimaron las medias de distribuciones posteriores (MPD) de los parámetros genéticos de LSB y se seleccionaron los modelos mejor ajustados utilizando el criterio de información de desviación. Resultados: En los análisis de medición repetida, la heredabilidad materna y directa estimada y el efecto ambiental permanente (±SE), según el modelo mejor ajustado (modelo 5), fueron 0,01 (0,010), 0,02 (0,014) y 0,01 (0,011), respectivamente. En el análisis univariado, las mejores estimaciones de heredabilidad directa y materna fueron 0,12 (0,064) y 0,08 (0,045), respectivamente. Se observó una tendencia creciente de heredabilidades directas y maternas en la paridad 2 (0,15 (0,082) y 0,25 (0,083), respectivamente). En el análisis bivariado, las mejores estimaciones de heredabilidad directa y materna para LSB fueron 0,03 (0,027) y 0,22 (0,041), respectivamente. Las correlaciones genéticas directas y maternas entre partos fueron 0,25 (0,054) y 0,12 (0,021), respectivamente. Conclusiones: Los resultados mostraron una influencia considerable de los factores ambientales sobre el LSB en cada parto de las ovejas; además, se obtuvieron parámetros genéticos estadísticamente diferentes (p<0.05) de un parto a otro, indicando las diferentes y grandes influencias de factores genéticos y ambientales para cada parto en ovejas. Los resultados de este estudio se pueden precisar aún más utilizando datos de SNP de todo el genoma sobre diferentes partes para manejar una amplia gama de problemas relacionados con la interacción del entorno genético del rasgo LSB.

Antecedentes: O tamanho da ninhada ao nascer (LSB) é inerentemente uma característica categórica e deve ser analisada com modelos de limiar. LSB é uma das características mais importantes de produção de carne em ovinos e pode ser usado em esquemas de melhoramento genético para a produção de carne. Objetivo: Modelos de limiar bayesiano foram usados para analisar o tamanho da ninhada de ovelhas ao nascer (LSB) para estimar parâmetros genéticos. Métodos: Os dados foram baseados em 7.901 registros LSB de 14.968 ovelhas e 682 carneiros coletados de 1986 a 2012 na Estação de Criação de Ovinos Makouie no Irã. Médias de distribuições posteriores (MPDs) dos parâmetros genéticos de LSB foram estimadas e os modelos mais bem ajustados foram selecionados usando o critério de informação de desvio. Resultados: Nas análises de medidas repetidas, as herdabilidades diretas e maternas estimadas e o efeito do ambiente permanente (±SE), de acordo com o modelo mais bem ajustado (modelo 5), foram 0,01 (0,010), 0,02 (0,014) e 0,01 (0,011), respectivamente. Na análise univariada, as melhores estimativas das herdabilidades direta e materna foram 0,12 (0,064) e 0,08 (0,045), respectivamente. Uma tendência crescente para as herdabilidades direta e materna foi observada na paridade 2 (0,15 (0,082) e 0,25 (0,083), respectivamente). Na análise bivariada, as melhores estimativas de herdabilidades direta e materna para LSB foram 0,03 (0,027) e 0,22 (0,041), respectivamente. As correlações genéticas diretas e maternas entre os partos foram 0,25 (0,054) e 0,12 (0,021), respectivamente. Conclusões: Os resultados mostraram uma influência considerável dos fatores ambientais na LSB em cada paridade de ovelhas; também, parâmetros genéticos estatisticamente diferentes (p<0,05) foram obtidos de uma paridade para outra, indicando as diferentes e grandes influências de fatores genéticos e ambientais para cada paridade em ovinos. Os resultados deste estudo podem ser ainda mais definidos usando dados SNPs de todo o genoma em diferentes partes para lidar com uma ampla gama de problemas relacionados à interação do ambiente genético do traço LSB.

An integrated model of acinar to ductal metaplasia-related N7-methyladenosine regulators predicts prognosis and immunotherapy in pancreatic carcinoma based on digital spatial profiling.

Acinar-to-ductal metaplasia (ADM) is a recently recognized, yet less well-studied, precursor lesion of pancreatic ductal adenocarcinoma (PDAC) developed in the setting of chronic pancreatitis. Through digital spatial mRNA profiling, we compared ADM and adjacent PDAC tissues from patient samples to unveil the bridging genes during the malignant transformation of pancreatitis. By comparing the bridging genes with the 7-methylguanosine (m7G)-seq dataset, we screened 19 m7G methylation genes for a subsequent large sample analysis. We constructed the "m7G score" model based on the RNA-seq data for pancreatic cancer in The Cancer Genome Atlas (TCGA) database and The Gene Expression Omnibus (GEO) database. Tumors with a high m7G score were characterized by increased immune cell infiltration, increased genomic instability, higher response rate to combined immune checkpoint inhibitors (ICIs), and overall poor survival. These findings indicate that the m7G score is associated with tumor invasiveness, immune cell infiltration, ICI treatment response, and overall patients' survival. We also identified FN1 and ITGB1 as core genes in the m7Gscore model, which affect immune cell infiltration and genomic instability not only in pancreatic cancer but also in pan-cancer. FN1 and ITGB1 can inhibit immune T cell activition by upregulation of macrophages and neutrophils, thereby leading to immune escape of pancreatic cancer cells and reducing the response rate of ICI treatment.

Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs.

Measuring mRNA decay in tumours is a prohibitive challenge, limiting our ability to map the post-transcriptional programs of cancer. Here, using a statistical framework to decouple transcriptional and post-transcriptional effects in RNA-seq data, we uncover the mRNA stability changes that accompany tumour development and progression. Analysis of 7760 samples across 18 cancer types suggests that mRNA stability changes are ~30% as frequent as transcriptional events, highlighting their widespread role in shaping the tumour transcriptome. Dysregulation of programs associated with >80 RNA-binding proteins (RBPs) and microRNAs (miRNAs) drive these changes, including multi-cancer inactivation of RBFOX and miR-29 families. Phenotypic activation or inhibition of RBFOX1 highlights its role in calcium signaling dysregulation, while modulation of miR-29 shows its impact on extracellular matrix organization and stemness genes. Overall, our study underlines the integral role of mRNA stability in shaping the cancer transcriptome, and provides a resource for systematic interrogation of cancer-associated stability pathways.

Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding.

BACKGROUND: While methylation of CpG dinucleotides is traditionally considered antagonistic to the DNA-binding activity of most transcription factors (TFs), recent in vitro studies have revealed a more complex picture, suggesting that over a third of TFs may preferentially bind to methylated sequences. Expanding these in vitro observations to in vivo TF binding preferences is challenging since the effect of methylation of individual CpG sites cannot be easily isolated from the confounding effects of DNA accessibility and regional DNA methylation. Thus, in vivo methylation preferences of most TFs remain uncharacterized. RESULTS: We introduce joint accessibility-methylation-sequence (JAMS) models, which connect the strength of the binding signal observed in ChIP-seq to the DNA accessibility of the binding site, regional methylation level, DNA sequence, and base-resolution cytosine methylation. We show that JAMS models quantitatively explain TF occupancy, recapitulate cell type-specific TF binding, and have high positive predictive value for identification of TFs affected by intra-motif methylation. Analysis of 2209 ChIP-seq experiments results in high-confidence JAMS models for 260 TFs, revealing a negative association between in vivo TF occupancy and intra-motif methylation for 45% of studied TFs, as well as 16 TFs that are predicted to bind to methylated sites, including 11 novel methyl-binding TFs mostly from the multi-zinc finger family. CONCLUSIONS: Our study substantially expands the repertoire of in vivo methyl-binding TFs, but also suggests that most TFs that prefer methylated CpGs in vitro present themselves as methylation agnostic in vivo, potentially due to the balancing effect of competition with other methyl-binding proteins.

Analysis of mRNA Dynamics Using RNA Sequencing Data.

The RNA abundance of each gene is determined by its rates of transcription and RNA decay. Biochemical experiments that measure these rates, including transcription inhibition and metabolic labelling, are challenging to perform and are largely limited to in vitro settings. Most transcriptomic studies have focused on analyzing changes in RNA abundances without attributing those changes to transcriptional or posttranscriptional regulation. Estimating differential transcription and decay rates of RNA molecules would enable the identification of regulatory factors, such as transcription factors, RNA binding proteins, and microRNAs, that govern large-scale shifts in RNA expression. Here, we describe a protocol for estimating differential stability of RNA molecules between conditions using standard RNA-sequencing data, without the need for transcription inhibition or metabolic labeling. We apply this protocol to in vivo RNA-seq data from individuals with Alzheimer's disease and demonstrate how estimates of differential stability can be leveraged to infer the regulatory factors underlying them.

Spatially mapping the immune landscape of melanoma using imaging mass cytometry.

Melanoma is an immunogenic cancer with a high response rate to immune checkpoint inhibitors (ICIs). It harbors a high mutation burden compared with other cancers and, as a result, has abundant tumor-infiltrating lymphocytes (TILs) within its microenvironment. However, understanding the complex interplay between the stroma, tumor cells, and distinct TIL subsets remains a substantial challenge in immune oncology. To properly study this interplay, quantifying spatial relationships of multiple cell types within the tumor microenvironment is crucial. To address this, we used cytometry time-of-flight (CyTOF) imaging mass cytometry (IMC) to simultaneously quantify the expression of 35 protein markers, characterizing the microenvironment of 5 benign nevi and 67 melanomas. We profiled more than 220,000 individual cells to identify melanoma, lymphocyte subsets, macrophage/monocyte, and stromal cell populations, allowing for in-depth spatial quantification of the melanoma microenvironment. We found that within pretreatment melanomas, the abundance of proliferating antigen-experienced cytotoxic T cells (CD8+CD45RO+Ki67+) and the proximity of antigen-experienced cytotoxic T cells to melanoma cells were associated with positive response to ICIs. Our study highlights the potential of multiplexed single-cell technology to quantify spatial cell-cell interactions within the tumor microenvironment to understand immune therapy responses.

Transcriptional control of brain tumor stem cells by a carbohydrate binding protein.

Brain tumor stem cells (BTSCs) and intratumoral heterogeneity represent major challenges in glioblastoma therapy. Here, we report that the LGALS1 gene, encoding the carbohydrate binding protein, galectin1, is a key regulator of BTSCs and glioblastoma resistance to therapy. Genetic deletion of LGALS1 alters BTSC gene expression profiles and results in downregulation of gene sets associated with the mesenchymal subtype of glioblastoma. Using a combination of pharmacological and genetic approaches, we establish that inhibition of LGALS1 signaling in BTSCs impairs self-renewal, suppresses tumorigenesis, prolongs lifespan, and improves glioblastoma response to ionizing radiation in preclinical animal models. Mechanistically, we show that LGALS1 is a direct transcriptional target of STAT3 with its expression robustly regulated by the ligand OSM. Importantly, we establish that galectin1 forms a complex with the transcription factor HOXA5 to reprogram the BTSC transcriptional landscape. Our data unravel an oncogenic signaling pathway by which the galectin1/HOXA5 complex maintains BTSCs and promotes glioblastoma.

Identification of sequence variation in the oocyte-derived bone morphogenetic protein 15 (BMP15) gene (BMP15) associated with litter size in New Zealand sheep (Ovis aries) breeds.

Advances in the study of reproductive traits indicate that functional variation in fertility genes may be useful for improving sheep fertility. The aim of this study was to search for variation in the bone morphogenetic protein 15 gene (BMP15) and ascertain any association with litter size in purebred Finnish Landrace sheep (n = 148), Finnish Landrace × Texel-cross sheep (n = 45), and composite sheep (of varying breed background; n = 58) from New Zealand (NZ). A 482 bp and 312 bp fragment of exon 1 and 2, respectively, of BMP15 were analysed using polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP). The additive and dominance effect of BMP15 variation on litter size were estimated using animal and sire models. Two variants (A and B) were detected in exon 1; no sequence variation was detected in exon 2. Variant A had the nucleotide sequence CTT between positions c.31 and c.33, while variant B had a deletion (c.31_33del). The observed frequency for variant A in the Finnish Landrace sheep, Finnish Landrace × Texel-cross sheep and the composite sheep, was 0.77, 0.92, and 0.68, respectively while the frequency of variant B (c.31_33del) was 0.23, 0.08, and 0.32, respectively. An association between litter size and c.31_33del (P < 0.001) was observed in composite sheep. Analysis of more sheep will be required to confirm these results. Litter size did not differ significantly between sheep breeds regardless of the presence/absence of c.31_33del. Results suggested that c.31_33del might be a genetic marker for improving fecundity in some NZ sheep.

A prometastatic splicing program regulated by SNRPA1 interactions with structured RNA elements.

Aberrant alternative splicing is a hallmark of cancer, yet the underlying regulatory programs that control this process remain largely unknown. Here, we report a systematic effort to decipher the RNA structural code that shapes pathological splicing during breast cancer metastasis. We discovered a previously unknown structural splicing enhancer that is enriched near cassette exons with increased inclusion in highly metastatic cells. We show that the spliceosomal protein small nuclear ribonucleoprotein polypeptide A' (SNRPA1) interacts with these enhancers to promote cassette exon inclusion. This interaction enhances metastatic lung colonization and cancer cell invasion, in part through SNRPA1-mediated regulation of PLEC alternative splicing, which can be counteracted by splicing modulating morpholinos. Our findings establish a noncanonical regulatory role for SNRPA1 as a prometastatic splicing enhancer in breast cancer.