Prmt5 is a regulator of muscle stem cell expansion in adult mice.

Skeletal muscle stem cells (MuSC), also called satellite cells, are indispensable for maintenance and regeneration of adult skeletal muscles. Yet, a comprehensive picture of the regulatory events controlling the fate of MuSC is missing. Here, we determine the proteome of MuSC to design a loss-of-function screen, and identify 120 genes important for MuSC function including the arginine methyltransferase Prmt5. MuSC-specific inactivation of Prmt5 in adult mice prevents expansion of MuSC, abolishes long-term MuSC maintenance and abrogates skeletal muscle regeneration. Interestingly, Prmt5 is dispensable for proliferation and differentiation of Pax7(+) myogenic progenitor cells during mouse embryonic development, indicating significant differences between embryonic and adult myogenesis. Mechanistic studies reveal that Prmt5 controls proliferation of adult MuSC by direct epigenetic silencing of the cell cycle inhibitor p21. We reason that Prmt5 generates a poised state that keeps MuSC in a standby mode, thus allowing rapid MuSC amplification under disease conditions

PCA2GO: a new multivariate statistics based method to identify highly expressed GO-Terms

<p>Abstract</p> <p>Background</p> <p>Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is relatively complicated and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. Pie charts based on GO tree levels are, themselves, one-dimensional graphs, which cannot properly or efficiently represent the hierarchical specificity for the biological system being studied.</p> <p>Results</p> <p>Here we present a new method, which we name PCA2GO, capable of GO analysis using complex multidimensional experimental settings. We employed principal component analysis (PCA) and developed a new score, which takes into account the relative frequency of certain GO terms and their specificity (hierarchical position) within the GO graph. We evaluated the correlation between our representation score <it>R </it>and a standard measure of enrichment, namely <it>p</it>-values to convey the versatility of our approach to other methods and point out differences between our method and commonly used enrichment analyses. Although <it>p </it>values and the <it>R </it>score formally measure different quantities they should be correlated, because relative frequencies of GO terms occurrences within a dataset are an indirect measure of protein numbers related to this term. Therefore they are also related to enrichment. We showed that our score enables us to identify more specific GO-terms i.e. those positioned further down the GO-graph than other common tools used for this purpose. PCA2GO allows visualization and detection of multidimensional dependencies both within the acyclic graph (GO tree) and the experimental settings. Our method is intended for the analysis of several experimental sets, not for one set, like standard enrichment tools. To demonstrate the usefulness of our approach we performed a PCA2GO analysis of a fractionated cardiomyocyte protein dataset, which was identified by enhanced liquid chromatography-mass spectrometry (GeLC-MS). The analysis enabled us to detect distinct groups of proteins, which accurately reflect properties of biochemical cell fractions.</p> <p>Conclusions</p> <p>We conclude that PCA2GO is an alternative efficient GO analysis tool with unique features for detection and visualization of multidimensional dependencies within the dataset under study. PCA2GO reveals strongly correlated GO terms within the experimental setting (in this case different fractions) by PCA group formation and improves detection of more specific GO terms within experiment dependent GO term groups than standard <it>p </it>value calculations.</p

A Microarray Analysis of Gene Expression Patterns During Early Phases of Newt Lens Regeneration

Purpose: Notophthalmus viridescens, the red-spotted newt, possesses tremendous regenerative capabilities. Among the tissues and organs newts can regenerate, the lens is regenerated via transdifferentiation of the pigment epithelial cells of the dorsal iris, following complete removal (lentectomy). Under normal conditions, the same cells from the ventral iris are not capable of regenerating. This study aims to further understand the initial signals of lens regeneration

Newt-omics: a comprehensive repository for omics data from the newt Notophthalmus viridescens

Notophthalmus viridescens, a member of the salamander family is an excellent model organism to study regenerative processes due to its unique ability to replace lost appendages and to repair internal organs. Molecular insights into regenerative events have been severely hampered by the lack of genomic, transcriptomic and proteomic data, as well as an appropriate database to store such novel information. Here, we describe ‘Newt-omics’ (http://newt-omics.mpi-bn.mpg.de), a database, which enables researchers to locate, retrieve and store data sets dedicated to the molecular characterization of newts. Newt-omics is a transcript-centred database, based on an Expressed Sequence Tag (EST) data set from the newt, covering ∼50 000 Sanger sequenced transcripts and a set of high-density microarray data, generated from regenerating hearts. Newt-omics also contains a large set of peptides identified by mass spectrometry, which was used to validate 13 810 ESTs as true protein coding. Newt-omics is open to implement additional high-throughput data sets without changing the database structure. Via a user-friendly interface Newt-omics allows access to a huge set of molecular data without the need for prior bioinformatical expertise

On an Analytical Model for Long-Range Financial Planning (2)

The inhibitor of the nuclear factor-kappa B (I kappa B) kinase (IKK) complex is a key regulator of the canonical NF-kappa B signalling cascade and is crucial for fundamental cellular functions, including stress and immune responses. The majority of IKK complex functions are attributed to NF-kappa B activation; however, there is increasing evidence for NF-kappa B pathway-independent signalling. Here we combine quantitative mass spectrometry with random forest bioinformatics to dissect the TNF-alpha-IKK beta-induced phosphoproteome in MCF-7 breast cancer cells. In total, we identify over 20,000 phosphorylation sites, of which similar to 1% are regulated up on TNF-alpha stimulation. We identify various potential novel IKK beta substrates including kinases and regulators of cellular trafficking. Moreover, we show that one of the candidates, AEG-1/MTDH/LYRIC, is directly phosphorylated by IKK beta on serine 298. We provide evidence that IKK beta-mediated AEG-1 phosphorylation is essential for I kappa B alpha degradation as well as NF-kappa B-dependent gene expression and cell proliferation, which correlate with cancer patient survival in vivo

Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement.

Formation and segregation of cell lineages forming the heart have been studied extensively but the underlying gene regulatory networks and epigenetic changes driving cell fate transitions during early cardiogenesis are still only partially understood. Here, we comprehensively characterize mouse cardiac progenitor cells (CPCs) marked by Nkx2-5 and Isl1 expression from E7.5 to E9.5 using single-cell RNA sequencing and transposase-accessible chromatin profiling (ATAC-seq). By leveraging on cell-to-cell transcriptome and chromatin accessibility heterogeneity, we identify different previously unknown cardiac subpopulations. Reconstruction of developmental trajectories reveal that multipotent Isl1+ CPC pass through an attractor state before separating into different developmental branches, whereas extended expression of Nkx2-5 commits CPC to an unidirectional cardiomyocyte fate. Furthermore, we show that CPC fate transitions are associated with distinct open chromatin states critically depending on Isl1 and Nkx2-5. Our data provide a model of transcriptional and epigenetic regulations during cardiac progenitor cell fate decisions at single-cell resolution