A Topological Framework for the Computation of the HOMFLY Polynomial and Its Application to Proteins

Polymers can be modeled as open polygonal paths and their closure generates knots. Knotted proteins detection is currently achieved via high-throughput methods based on a common framework insensitive to the handedness of knots. Here we propose a topological framework for the computation of the HOMFLY polynomial, an handedness-sensitive invariant. Our approach couples a multi-component reduction scheme with the polynomial computation. After validation on tabulated knots and links the framework was applied to the entire Protein Data Bank along with a set of selected topological checks that allowed to discard artificially entangled structures. This led to an up-to-date table of knotted proteins that also includes two newly detected right-handed trefoil knots in recently deposited protein structures. The application range of our framework is not limited to proteins and it can be extended to the topological analysis of biological and synthetic polymers and more generally to arbitrary polygonal paths.Comment: 20 pages, 7 figure

Rknots: topological analysis of knotted biopolymers with R

Motivation: Rknots is a flexible R package providing tools for the detection and characterization of topological knots in biological polymers. The package is well documented and provides a simple syntax for data import and preprocessing, structure reduction, topological analyses and 2D and 3D visualization. Remarkably, Rknots is not limited to protein knots and allows researchers from interdisciplinary fields to analyze different topological structures and to develop simple yet fully custom pipelines. Availability: Rknots is distributed under the GPL-2 license and is available from the CRAN (the Comprehensive R Archive network) at http://cran.r-project.org/web/packages/Rknots Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics onlin

Mixture models and wavelet transforms reveal high confidence RNA-protein interaction sites in MOV10 PAR-CLIP data

The Photo-Activatable Ribonucleoside-enhanced CrossLinking and ImmunoPrecipitation (PAR-CLIP) method was recently developed for global identification of RNAs interacting with proteins. The strength of this versatile method results from induction of specific T to C transitions at sites of interaction. However, current analytical tools do not distinguish between non-experimentally and experimentally induced transitions. Furthermore, geometric properties at potential binding sites are not taken into account. To surmount these shortcomings, we developed a two-step algorithm consisting of a non-parametric two-component mixture model and a wavelet-based peak calling procedure. Our algorithm can reduce the number of false positives up to 24% thereby identifying high confidence interaction sites. We successfully employed this approach in conjunction with a modified PAR-CLIP protocol to study the functional role of nuclear Moloney leukemia virus 10, a putative RNA helicase interacting with Argonaute2 and Polycomb. Our method, available as the R package wavClusteR, is generally applicable to any substitution-based inference problem in genomic

Selective inactivation of hypomethylating agents by SAMHD1 provides a rationale for therapeutic stratification in AML.

Hypomethylating agents decitabine and azacytidine are regarded as interchangeable in the treatment of acute myeloid leukemia (AML). However, their mechanisms of action remain incompletely understood, and predictive biomarkers for HMA efficacy are lacking. Here, we show that the bioactive metabolite decitabine triphosphate, but not azacytidine triphosphate, functions as activator and substrate of the triphosphohydrolase SAMHD1 and is subject to SAMHD1-mediated inactivation. Retrospective immunohistochemical analysis of bone marrow specimens from AML patients at diagnosis revealed that SAMHD1 expression in leukemic cells inversely correlates with clinical response to decitabine, but not to azacytidine. SAMHD1 ablation increases the antileukemic activity of decitabine in AML cell lines, primary leukemic blasts, and xenograft models. AML cells acquire resistance to decitabine partly by SAMHD1 up-regulation. Together, our data suggest that SAMHD1 is a biomarker for the stratified use of hypomethylating agents in AML patients and a potential target for the treatment of decitabine-resistant leukemia

SAMHD1 is a biomarker for cytarabine response and a therapeutic target in acute myeloid leukemia.

The nucleoside analog cytarabine (Ara-C) is an essential component of primary and salvage chemotherapy regimens for acute myeloid leukemia (AML). After cellular uptake, Ara-C is converted into its therapeutically active triphosphate metabolite, Ara-CTP, which exerts antileukemic effects, primarily by inhibiting DNA synthesis in proliferating cells. Currently, a substantial fraction of patients with AML fail to respond effectively to Ara-C therapy, and reliable biomarkers for predicting the therapeutic response to Ara-C are lacking. SAMHD1 is a deoxynucleoside triphosphate (dNTP) triphosphohydrolase that cleaves physiological dNTPs into deoxyribonucleosides and inorganic triphosphate. Although it has been postulated that SAMHD1 sensitizes cancer cells to nucleoside-analog derivatives through the depletion of competing dNTPs, we show here that SAMHD1 reduces Ara-C cytotoxicity in AML cells. Mechanistically, dGTP-activated SAMHD1 hydrolyzes Ara-CTP, which results in a drastic reduction of Ara-CTP in leukemic cells. Loss of SAMHD1 activity-through genetic depletion, mutational inactivation of its triphosphohydrolase activity or proteasomal degradation using specialized, virus-like particles-potentiates the cytotoxicity of Ara-C in AML cells. In mouse models of retroviral AML transplantation, as well as in retrospective analyses of adult patients with AML, the response to Ara-C-containing therapy was inversely correlated with SAMHD1 expression. These results identify SAMHD1 as a potential biomarker for the stratification of patients with AML who might best respond to Ara-C-based therapy and as a target for treating Ara-C-refractory AML

Rknots

R package version 1.2.

Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data

Background PAR-CLIP is a recently developed Next Generation Sequencing-based method enabling transcriptome-wide identification of interaction sites between RNA and RNA-binding proteins. The PAR-CLIP procedure induces specific base transitions that originate from sites of RNA-protein interactions and can therefore guide the identification of binding sites. However, additional sources of transitions, such as cell type-specific SNPs and sequencing errors, challenge the inference of binding sites and suitable statistical approaches are crucial to control false discovery rates. In addition, a highly resolved delineation of binding sites followed by an extensive downstream analysis is necessary for a comprehensive characterization of the protein binding preferences and the subsequent design of validation experiments. Results We present a statistical and computational framework for PAR-CLIP data analysis. We developed a sensitive transition-centered algorithm specifically designed to resolve protein binding sites at high resolution in PAR-CLIP data. Our method employes a Bayesian network approach to associate posterior log-odds with the observed transitions, providing an overall quantification of the confidence in RNA-protein interaction. We use published PAR-CLIP data to demonstrate the advantages of our approach, which compares favorably with alternative algorithms. Lastly, by integrating RNA-Seq data we compute conservative experimentally-based false discovery rates of our method and demonstrate the high precision of our strategy. Conclusions Our method is implemented in the R package wavClusteR 2.0. The package is distributed under the GPL-2 license and is available from BioConductor at http://www.bioconductor.org/packages/devel/bioc/html/wavClusteR.html.ISSN:1471-210