Mud2 functions in transcription by recruiting the Prp19 and TREX complexes to transcribed genes

The different steps of gene expression are intimately linked to coordinate and regulate this complex process. During transcription, numerous RNA-binding proteins are already loaded onto the nascent mRNA and package the mRNA into a messenger ribonucleoprotein particle (mRNP). These RNA-binding proteins are often also involved in other steps of gene expression than mRNA packaging. For example, TREX functions in transcription, mRNP packaging and nuclear mRNA export. Previously, we showed that the Prp19 splicing complex (Prp19C) is needed for efficient transcription as well as TREX occupancy at transcribed genes. Here, we show that the splicing factor Mud2 interacts with Prp19C and is needed for Prp19C occupancy at transcribed genes in Saccharomyces cerevisiae. Interestingly, Mud2 is not only recruited to intron-containing but also to intronless genes indicating a role in transcription. Indeed, we show for the first time that Mud2 functions in transcription. Furthermore, these functions of Mud2 are likely evolutionarily conserved as Mud2 is also recruited to an intronless gene and interacts with Prp19C in Drosophila melanogaster. Taken together, we classify Mud2 as a novel transcription factor that is necessary for the recruitment of mRNA-binding proteins to the transcription machinery. Thus, Mud2 is a multifunctional protein important for transcription, splicing and most likely also mRNP packaging

Extraction of protein profiles from primary neurons using active contour models and wavelets

AbstractThe function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means.We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites.We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses

RNA Sequencing of Collecting Duct Renal Cell Carcinoma Suggests an Interaction between miRNA and Target Genes and a Predominance of Deregulated Solute Carrier Genes

Collecting duct carcinoma (CDC) is a rare renal cell carcinoma subtype with a very poor prognosis. There have been only a few studies on gene expression analysis in CDCs. We compared the gene expression profiles of two CDC cases with those of eight normal tissues of renal cell carcinoma patients. At a threshold of |log2fold-change| ≥1, 3349 genes were upregulated and 1947 genes were downregulated in CDCs compared to the normal samples. Pathway analysis of the deregulated genes revealed that cancer pathways and cell cycle pathways were most prominent in CDCs. The most upregulated gene was keratin 17, and the most downregulated gene was cubilin. Among the most downregulated genes were four solute carrier genes (SLC3A1, SLC9A3, SLC26A7, and SLC47A1). The strongest negative correlations between miRNAs and mRNAs were found between the downregulated miR-374b-5p and its upregulated target genes HIST1H3B, HK2, and SLC7A11 and between upregulated miR-26b-5p and its downregulated target genes PPARGC1A, ALDH6A1, and MARC2. An upregulation of HK2 and a downregulation of PPARGC1A, ALDH6A1, and MARC2 were observed at the protein level. Survival analysis of the cancer genome atlas (TCGA) dataset showed for the first time that low gene expression of MARC2, cubilin, and SLC47A1 and high gene expression of KRT17 are associated with poor overall survival in clear cell renal cell carcinoma patients. Altogether, we identified dysregulated protein-coding genes, potential miRNA-target interactions, and prognostic markers that could be associated with CDC

More Benefits of Semileptonic Rare B Decays at Low Recoil: CP Violation

We present a systematic analysis of the angular distribution of Bbar -> Kbar^\ast (-> Kbar pi) l^+ l^- decays with l = e, mu in the low recoil region (i.e. at high dilepton invariant masses of the order of the mass of the b-quark) to account model-independently for CP violation beyond the Standard Model, working to next-to-leading order QCD. From the employed heavy quark effective theory framework we identify the key CP observables with reduced hadronic uncertainties. Since some of the CP asymmetries are CP-odd they can be measured without B-flavour tagging. This is particularly beneficial for Bbar_s,B_s -> phi(-> K^+ K^-) l^+ l^- decays, which are not self-tagging, and we work out the corresponding time-integrated CP asymmetries. Presently available experimental constraints allow the proposed CP asymmetries to be sizeable, up to values of the order ~ 0.2, while the corresponding Standard Model values receive a strong parametric suppression at the level of O(10^-4). Furthermore, we work out the allowed ranges of the short-distance (Wilson) coefficients C_9,C_10 in the presence of CP violation beyond the Standard Model but no further Dirac structures. We find the Bbar_s -> mu^+ mu^- branching ratio to be below 9*10^-9 (at 95% CL). Possibilities to check the performance of the theoretical low recoil framework are pointed out.Comment: 18 pages, 3 fig.; 1 reference and comment on higher order effects added; EOS link fixed. Minor adjustments to Eqs 4.1-4.3 to match the (lower) q^2-cut as given in paper. Main results and conclusions unchanged; v3+v4: treatment of exp. uncert. in likelihood-function in EOS fixed and constraints from scan on C9,C10 updated (Fig 2,3 and Eqs 3.2,3.3). Main results and conclusions absolutely unchange

The Benefits of B ---> K* l+ l- Decays at Low Recoil

Using the heavy quark effective theory framework put forward by Grinstein and Pirjol we work out predictions for B -> K* l+ l-, l = (e, mu), decays for a softly recoiling K*, i.e., for large dilepton masses sqrt{q^2} of the order of the b-quark mass m_b. We work to lowest order in Lambda/Q, where Q = (m_b, sqrt{q^2}) and include the next-to-leading order corrections from the charm quark mass m_c and the strong coupling at O(m_c^2/Q^2, alpha_s). The leading Lambda/m_b corrections are parametrically suppressed. The improved Isgur-Wise form factor relations correlate the B -> K* l+ l- transversity amplitudes, which simplifies the description of the various decay observables and provides opportunities for the extraction of the electroweak short distance couplings. We propose new angular observables which have very small hadronic uncertainties. We exploit existing data on B -> K* l+ l- distributions and show that the low recoil region provides powerful additional information to the large recoil one. We find disjoint best-fit solutions, which include the Standard Model, but also beyond-the-Standard Model ones. This ambiguity can be accessed with future precision measurements.Comment: 31 pages, 8 figures; Instability near minimal recoil from numerics removed, Fig. 1 replaced and minor shifts in short distance uncertainties in SM predictions; typos corrected and references added; main results and conclusions unchange

Bayesian Fit of Exclusive b→sℓˉℓb \to s \bar\ell\ell Decays: The Standard Model Operator Basis

We perform a model-independent fit of the short-distance couplings $C_{7,9,10}$ within the Standard Model set of $b\to s\gamma$ and $b\to s\bar\ell\ell$ operators. Our analysis of $B \to K^* \gamma$, $B \to K^{(*)} \bar\ell\ell$ and $B_s \to \bar\mu\mu$ decays is the first to harness the full power of the Bayesian approach: all major sources of theory uncertainty explicitly enter as nuisance parameters. Exploiting the latest measurements, the fit reveals a flipped-sign solution in addition to a Standard-Model-like solution for the couplings $C_i$. Each solution contains about half of the posterior probability, and both have nearly equal goodness of fit. The Standard Model prediction is close to the best-fit point. No New Physics contributions are necessary to describe the current data. Benefitting from the improved posterior knowledge of the nuisance parameters, we predict ranges for currently unmeasured, optimized observables in the angular distributions of $B\to K^*(\to K\pi)\,\bar\ell\ell$.Comment: 42 pages, 8 figures; v2: Using new lattice input for f_Bs, considering Bs-mixing effects in BR[B_s->ll]. Main results and conclusion unchanged, matches journal versio

MiToBo - A Toolbox for Image Processing and Analysis

MiToBo is a toolbox and Java library for solving basic as well as advanced image processing and analysis tasks. It features a rich collection of fundamental, intermediate and high-level image processing operators and algorithms as well as a couple of sophisticated tools for specific biological and biomedical applications. These tools include operators for elucidating cellular morphology and locomotion as well as operators for the characterization of certain intracellular particles and structures. MiToBo builds upon and integrates into the widely-used image analysis software packages ImageJ and Fiji [11, 10], and all of its operators can easily be run in ImageJ and Fiji via a generic operator runner plugin. Alternatively MiToBo operators can directly be run from command line, and using its functionality as a library for developing own applications is also supported. Thanks to the Alida library [8] forming the base of MiToBo all operators share unified APIs fostering reusability, and graphical as well as command line user interfaces for operators are automatically generated. MiToBo is available from its website http://www.informatik.uni-halle.de/mitobo, on Github, via an Apache Archiva Maven repository server, and it can easily be activated in Fiji via its own update site

MiToBo - A Microscope Image Analysis Toolbox: v1.8

The Microscope Image Analysis Toolbox MiToBo is a Java toolbox and library of basic, intermediate and advanced image processing and analysis algorithms and tools. The toolbox for instance offers various filters (e.g., Gauss and Gabor filters, rank operators, vesselness filters), image enhancement approaches, image transformations (e.g., wavelets) and segmentation algorithms (e.g., thresholding, component labeling, active contours). In addition it ships with several advanced tools mainly dedicated to specific tasks in biomedical or biological image analysis, but not restricted to these fields. Examples are tools for DAPI stained nuclei detection, neurite segmentation, cell segmentation and tracking, and particle detection. MiToBo builds upon and extends the widely used image processing application ImageJ (http://imagej.nih.gov/ij/). All of its operators can be run from within ImageJ, but stand-alone usage or using MiToBo as a library for your own development is also possible. Finally, since MiToBo is based on Alida (http://www.informatik.uni-halle.de/alida), an advanced library for integrated development of data analysis applications, it offers a user- and programmer friendly framework for developing new image processing and analysis algorithms including automatic generation of user interfaces and a graphical workflow editor

IGF2BP1 Promotes Proliferation of Neuroendocrine Neoplasms by Post-Transcriptional Enhancement of EZH2

Neuroendocrine neoplasms (NENs) represent a heterogenous class of highly vascularized neoplasms that are increasing in prevalence and are predominantly diagnosed at a metastatic state. The molecular mechanisms leading to tumor initiation, metastasis, and chemoresistance are still under investigation. Hence, identification of novel therapeutic targets is of great interest. Here, we demonstrate that the RNA-binding Protein IGF2BP1 is a post-transcriptional regulator of components of the Polycomb repressive complex 2 (PRC2), an epigenic modifier affecting transcriptional regulation and proliferation: Comprehensive in silico analyses along with in vitro experiments showed that IGF2BP1 promotes neuroendocrine tumor cell proliferation by stabilizing the mRNA of Enhancer of Zeste 2 (EZH2), the catalytic subunit of PRC2, which represses gene expression by tri-methylation of histone H3 at lysine 27 (H3K27me3). The IGF2BP1-driven stabilization and protection of EZH2 mRNA is m6A-dependent and enhances EZH2 protein levels which stimulates cell cycle progression by silencing cell cycle arrest genes through enhanced H3K27 tri-methylation. Therapeutic inhibition of IGF2BP1 destabilizes EZH2 mRNA and results in a reduced cell proliferation, paralleled by an increase in G1 and sub-G1 phases. Combined targeting of IGF2BP1, EZH2, and Myc, a transcriptional activator of EZH2 and well-known target of IGF2BP1 cooperatively induces tumor cell apoptosis. Our data identify IGF2BP1 as an important driver of tumor progression in NEN, and indicate that disruption of the IGF2BP1-Myc-EZH2 axis represents a promising approach for targeted therapy of neuroendocrine neoplasms

MiRNA Deregulation Distinguishes Anaplastic Thyroid Carcinoma (ATC) and Supports Upregulation of Oncogene Expression

Anaplastic thyroid carcinoma (ATC) is the most fatal and rapidly evolving endocrine malignancy invading the head and neck region and accounts for up to 50% of thyroid cancer-associated deaths. Deregulation of the microRNA (miRNA) expression promotes thyroid carcinoma progression by modulating the reorganization of the ATC transcriptome. Here, we applied comparative miRNA–mRNA sequencing on a cohort of 28 thyroid carcinomas to unravel the association of deregulated miRNA and mRNA expression. This identified 85 miRNAs significantly deregulated in ATC. By establishing a new analysis pipeline, we unraveled 85 prime miRNA–mRNA interactions supporting the downregulation of candidate tumor suppressors and the upregulation of bona fide oncogenes such as survivin (BIRC5) in ATC. This miRNA-dependent reprogramming of the ATC transcriptome provided an mRNA signature comprising 65 genes sharply distinguishing ATC from other thyroid carcinomas. The validation of the deregulated protein expression in an independent thyroid carcinoma cohort demonstrates that miRNA-dependent oncogenes comprised in this signature, the transferrin receptor TFRC (CD71) and the E3-ubiquitin ligase DTL, are sharply upregulated in ATC. This upregulation is sufficient to distinguish ATC even from poorly differentiated thyroid carcinomas (PDTC). In sum, these findings provide new diagnostic tools and a robust resource to explore the key miRNA–mRNA regulation underlying the progression of thyroid carcinoma