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.

The lncRNA Neat1 is required for corpus luteum formation and the establishment of pregnancy in a subpopulation of mice.

Neat1 is a non-protein-coding RNA that serves as an architectural component of the nuclear bodies known as paraspeckles. Although cell-based studies indicate that Neat1 is a crucial regulator of gene expression, its physiological relevance remains unclear. Here, we find that Neat1 knockout (KO) mice stochastically fail to become pregnant despite normal ovulation. Unilateral transplantation of wild-type ovaries or the administration of progesterone partially rescued the phenotype, suggesting that corpus luteum dysfunction and concomitant low progesterone were the primary causes of the decreased fertility. In contrast to the faint expression observed in most of the adult tissues, Neat1 was highly expressed in the corpus luteum, and the formation of luteal tissue was severely impaired in nearly half of the Neat1 KO mice. These observations suggest that Neat1 is essential for the formation of the corpus luteum and for the subsequent establishment of pregnancy under a suboptimal condition that has not yet been identified.

The long noncoding RNA Neat1 is required for mammary gland development and lactation.

The lncRNA Neat1 is an essential architectural component of paraspeckle nuclear bodies. Although cell-based studies identified Neat1-paraspeckles as key regulators of gene expression through retention of hyperdited mRNAs and/or transcription factors, it is unclear under which specific physiological conditions paraspeckles are formed in vivo and whether they have any biological relevance. Herein, we show that paraspeckles are assembled in luminal epithelial cells during mammary gland development. Importantly, genetic ablation of Neat1 results in aberrant mammary gland morphogenesis and lactation defects. We provide evidence that the lactation defect is caused by a decreased ability of Neat1-mutant cells to sustain high rates of proliferation during lobular-alveolar development. This study is the first to assign an important biological function to the lncRNA Neat1 and to link it to the presence of paraspeckles nuclear bodies in vivo.

iRegulon: from a gene list to a gene regulatory network using large motif and track collections.

Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.