RNA ligands selected by cleavage stimulation factor contain distinct sequence motifs that function as downstream elements in 3'-end processing of pre-mRNA

Critical events in 3'-end processing of pre-mRNA are the recognition of the AAUAAA polyadenylation signal by cleavage and polyadenylation specificity factor (CPSF) and the binding of cleavage stimulation factor (CstF) via its 64-kDa subunit to the downstream element. The stability of this CPSF.CstF.RNA complex is thought to determine the efficiency of 3'-end processing. Since downstream elements reveal high sequence variability, in vitro selection experiments with highly purified CstF were performed to investigate the sequence requirements for CstF-RNA interaction. CstF was purified from calf thymus and from HeLa cells. Surprisingly, calf thymus CstF contained an additional, novel form of the 64-kDa subunit with a molecular mass of 70 kDa. RNA ligands selected by HeLa and calf thymus CstF contained three highly conserved sequence elements as follows: element 1 (AUGCGUUCCUCGUCC) and two closely related elements, element 2a (YGUGUYN0-4UUYAYUGYGU) and element 2b (UUGYUN0-4AUUUACU(U/G)N0-2YCU). All selected sequences tested functioned as downstream elements in 3'-end processing in vitro. A computer survey of the EMBL data library revealed significant homologies to all selected elements in naturally occurring 3'-untranslated regions. The majority of element 2a homologies was found downstream of coding sequences. Therefore, we postulate that this element represents a novel consensus sequence for downstream elements in 3'-end processing of pre-mRNA

Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape

Integrated pathway modules using time-course metabolic profiles and EST data from Milnesium tardigradum

<p>Abstract</p> <p>Background</p> <p>Tardigrades are multicellular organisms, resistant to extreme environmental changes such as heat, drought, radiation and freezing. They outlast these conditions in an inactive form (tun) to escape damage to cellular structures and cell death. Tardigrades are apparently able to prevent or repair such damage and are therefore a crucial model organism for stress tolerance. Cultures of the tardigrade <it>Milnesium tardigradum</it> were dehydrated by removing the surrounding water to induce tun formation. During this process and the subsequent rehydration, metabolites were measured in a time series by GC-MS. Additionally expressed sequence tags are available, especially libraries generated from the active and inactive state. The aim of this integrated analysis is to trace changes in tardigrade metabolism and identify pathways responsible for their extreme resistance against physical stress.</p> <p>Results</p> <p>In this study we propose a novel integrative approach for the analysis of metabolic networks to identify modules of joint shifts on the transcriptomic and metabolic levels. We derive a tardigrade-specific metabolic network represented as an undirected graph with 3,658 nodes (metabolites) and 4,378 edges (reactions). Time course metabolite profiles are used to score the network nodes showing a significant change over time. The edges are scored according to information on enzymes from the EST data. Using this combined information, we identify a key subnetwork (functional module) of concerted changes in metabolic pathways, specific for de- and rehydration. The module is enriched in reactions showing significant changes in metabolite levels and enzyme abundance during the transition. It resembles the cessation of a measurable metabolism (e.g. glycolysis and amino acid anabolism) during the tun formation, the production of storage metabolites and bioprotectants, such as DNA stabilizers, and the generation of amino acids and cellular components from monosaccharides as carbon and energy source during rehydration.</p> <p>Conclusions</p> <p>The functional module identifies relationships among changed metabolites (e.g. spermidine) and reactions and provides first insights into important altered metabolic pathways. With sparse and diverse data available, the presented integrated metabolite network approach is suitable to integrate all existing data and analyse it in a combined manner.</p

Identifying functional modules in protein–protein interaction networks: an integrated exact approach

Motivation: With the exponential growth of expression and protein–protein interaction (PPI) data, the frontier of research in systems biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sharing common cellular function beyond the scope of classical pathways, by means of detecting differentially expressed regions in PPI networks. This requires on the one hand an adequate scoring of the nodes in the network to be identified and on the other hand the availability of an effective algorithm to find the maximally scoring network regions. Various heuristic approaches have been proposed in the literature

‘Distancers’ and ‘non-distancers’? The potential social psychological impact of moralizing COVID-19 mitigating practices on sustained behaviour change

COVID‐19 mitigating practices such as ‘hand‐washing’, ‘social distancing’, or ‘social isolating’ are constructed as ‘moral imperatives’, required to avert harm to oneself and others. Adherence to COVID‐19 mitigating practices is presently high among the general public, and stringent lockdown measures supported by legal and policy intervention have facilitated this. In the coming months, however, as rules are being relaxed and individuals become less strict, and thus, the ambiguity in policy increases, the maintenance of recommended social distancing norms will rely on more informal social interactional processes. We argue that the moralization of these practices, twinned with relaxations of policy, may likely cause interactional tension between those individuals who do vs. those who do not uphold social distancing in the coming months: that is, derogation of those who adhere strictly to COVID‐19 mitigating practices and group polarization between ‘distancers’ and ‘non‐distancers’. In this paper, we explore how and why these processes might come to pass, their impact on an overall societal response to COVID‐19, and the need to factor such processes into decisions regarding how to lift restriction

Germinal Center B Cell-Like (GCB) and Activated B Cell-Like (ABC) Type of Diffuse Large B Cell Lymphoma (DLBCL): Analysis of Molecular Predictors, Signatures, Cell Cycle State and Patient Survival

Aiming to find key genes and events, we analyze a large data set on diffuse large B-cell lymphoma (DLBCL) gene-expression (248 patients, 12196 spots). Applying the loess normalization method on these raw data yields improved survival predictions, in particular for the clinical important group of patients with medium survival time. Furthermore, we identify a simplified prognosis predictor, which stratifies different risk groups similarly well as complex signatures

Global organization of metabolic fluxes in the bacterium, Escherichia coli

Cellular metabolism, the integrated interconversion of thousands of metabolic substrates through enzyme-catalyzed biochemical reactions, is the most investigated complex intercellular web of molecular interactions. While the topological organization of individual reactions into metabolic networks is increasingly well understood, the principles governing their global functional utilization under different growth conditions pose many open questions. We implement a flux balance analysis of the E. coli MG1655 metabolism, finding that the network utilization is highly uneven: while most metabolic reactions have small fluxes, the metabolism's activity is dominated by several reactions with very high fluxes. E. coli responds to changes in growth conditions by reorganizing the rates of selected fluxes predominantly within this high flux backbone. The identified behavior likely represents a universal feature of metabolic activity in all cells, with potential implications to metabolic engineering.Comment: 15 pages 4 figure