Characterization of nuclear polyadenylated RNA-binding proteins in Saccharomyces cerevisiae.

To study the functions of heterogeneous nuclear ribonucleoproteins (hnRNPs), we have characterized nuclear polyadenylated RNA-binding (Nab) proteins from Saccharomyces cerevisiae. Nab1p, Nab2p, and Nab3p were isolated by a method which uses UV light to cross-link proteins directly bound to poly(A)+ RNA in vivo. We have previously characterized Nab2p, and demonstrated that it is structurally related to human hnRNPs. Here we report that Nab1p is identical to the Np13p/Nop3p protein recently implicated in both nucleocytoplasmic protein shuttling and pre-rRNA processing, and characterize a new nuclear polyadenylated RNA-binding protein, Nab3p. The intranuclear distributions of the Nab proteins were analyzed by three-dimensional immunofluorescence optical microscopy. All three Nab proteins are predominantly localized within the nucleoplasm in a pattern similar to the distribution of hnRNPs in human cells. The NAB3 gene is essential for cell viability and encodes an acidic ribonucleoprotein. Loss of Nab3p by growth of a GAL::nab3 mutant strain in glucose results in a decrease in the amount of mature ACT1, CYH2, and TPI1 mRNAs, a concomitant accumulation of unspliced ACT1 pre-mRNA, and an increase in the ratio of unspliced CYH2 pre-mRNA to mRNA. These results suggest that the Nab proteins may be required for packaging pre-mRNAs into ribonucleoprotein structures amenable to efficient nuclear RNA processing

Association of p60c-src with endosomal membranes in mammalian fibroblasts.

We have examined the subcellular localization of p60c-src in mammalian fibroblasts. Analysis of indirect immunofluorescence by three-dimensional optical sectioning microscopy revealed a granular cytoplasmic staining that co-localized with the microtubule organizing center. Immunofluorescence experiments with antibodies against a number of membrane markers demonstrated a striking co-localization between p60c-src and the cation-dependent mannose-6-phosphate receptor (CI-MPR), a marker that identifies endosomes. Both p60c-src and the CI-MPR were found to cluster at the spindle poles throughout mitosis. In addition, treatment of interphase and mitotic cells with brefeldin A resulted in a clustering of p60c-src and CI-MPR at a peri-centriolar position. Biochemical fractionation of cellular membranes showed that a major proportion of p60c-src co-enriched with endocytic membranes. Treatment of membranes containing HRP to alter their apparent density also altered the density of p60c-src-containing membranes. Similar density shift experiments with total cellular membranes revealed that the majority of membrane-associated p60c-src in the cell is associated with endosomes, while very little is associated with plasma membranes. These results support a role for p60c-src in the regulation of endosomal membranes and protein trafficking

Bod1 regulates protein phosphatase 2A at mitotic kinetochores

Mitotic entry and progression require the activation of several mitotic kinases and the proper regulation and localization of several phosphatases. The activity and localization of each of these enzymes is tightly controlled through a series of specific activators, inhibitors and regulatory subunits. Two proteins, Ensa and Arpp-19, were recently identified as specific inhibitors of PP2A-B55 and are critical for allowing full activity of Cdk1/cyclin B and entry into mitosis. Here we show that Bod1, a protein required for proper chromosome alignment at mitosis, shares sequence similarity with Ensa and Arpp-19 and specifically inhibits the kinetochore-associated PP2A-B56 holoenzyme. PP2A-B56 regulates the stability of kinetochore-microtubule attachments by dephosphorylating several kinetochore proteins. Loss of Bod1 changes the balance of phosphorylation at kinetochores, causing defects in kinetochore function. Bod1, Ensa and Arpp-19 define a family of specific PP2A inhibitors that regulate specific PP2A holoenzymes at distinct locations and points in the cell cycle

Publishing and sharing multi-dimensional image data with OMERO

Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combination of binary pixel data and metadata that describe the acquisition process and any derived results. The OMERO image data management platform allows users to securely share image datasets according to specific permissions levels: data can be held privately, shared with a set of colleagues, or made available via a public URL. Users control access by assigning data to specific Groups with defined membership and access rights. OMERO’s Permission system supports simple data sharing in a lab, collaborative data analysis, and even teaching environments. OMERO software is open source and released by the OME Consortium at www.openmicroscopy.org

Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses

Background: The explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation. Results: Over 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from ≥88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections. Conclusion: To overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field. © 2006 Brahmachary et al; licensee BioMed Central Ltd

A novel application of motion analysis for detecting stress responses in embryos at different stages of development.

Motion analysis is one of the tools available to biologists to extract biologically relevant information from image datasets and has been applied to a diverse range of organisms. The application of motion analysis during early development presents a challenge, as embryos often exhibit complex, subtle and diverse movement patterns. A method of motion analysis able to holistically quantify complex embryonic movements could be a powerful tool for fields such as toxicology and developmental biology to investigate whole organism stress responses. Here we assessed whether motion analysis could be used to distinguish the effects of stressors on three early developmental stages of each of three species: (i) the zebrafish Danio rerio (stages 19 h, 21.5 h and 33 h exposed to 1.5% ethanol and a salinity of 5); (ii) the African clawed toad Xenopus laevis (stages 24, 32 and 34 exposed to a salinity of 20); and iii) the pond snail Radix balthica (stages E3, E4, E6, E9 and E11 exposed to salinities of 5, 10 and 15). Image sequences were analysed using Sparse Optic Flow and the resultant frame-to-frame motion parameters were analysed using Discrete Fourier Transform to quantify the distribution of energy at different frequencies. This spectral frequency dataset was then used to construct a Bray-Curtis similarity matrix and differences in movement patterns between embryos in this matrix were tested for using ANOSIM

Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3D segmentation algorithms.

Background: Image segmentation is a crucial step in quantitative microscopy that helps to define regions of tissues, cells or subcellular compartments. Depending on the degree of user interactions, segmentation methods can be divided into manual, automated or semi-automated approaches. 3D image stacks usually require automated methods due to their large number of optical sections. However, certain applications benefit from manual or semi-automated approaches. Scenarios include the quantification of 3D images with poor signal-to-noise ratios or the generation of so-called ground truth segmentations that are used to evaluate the accuracy of automated segmentation methods. Results: We have developed Gebiss; an ImageJ plugin for the interactive segmentation, visualisation and quantification of 3D microscopic image stacks. We integrated a variety of existing plugins for threshold-based segmentation and volume visualisation. Conclusions: We demonstrate the application of Gebiss to the segmentation of nuclei in live Drosophila embryos and the quantification of neurodegeneration in Drosophila larval brains. Gebiss was developed as a cross-platform ImageJ plugin and is freely available on the web at http://imaging.bii.a-star.edu.sg/projects/gebiss