Transcriptomic Signature of the Simulated Microgravity Response in Caenorhabditis elegans and Comparison to Spaceflight Experiments.

Given the growing interest in human exploration of space, it is crucial to identify the effects of space conditions on biological processes. Here, we analyze the transcriptomic response of Caenorhabditis elegans to simulated microgravity and observe the maintained transcriptomic response after returning to ground conditions for four, eight, and twelve days. We show that 75% of the simulated microgravity-induced changes on gene expression persist after returning to ground conditions for four days while most of these changes are reverted after twelve days. Our results from integrative RNA-seq and mass spectrometry analyses suggest that simulated microgravity affects longevity-regulating insulin/IGF-1 and sphingolipid signaling pathways. Finally, we identified 118 genes that are commonly differentially expressed in simulated microgravity- and space-exposed worms. Overall, this work provides insight into the effect of microgravity on biological systems during and after exposure.

Effects of liquid cultivation on gene expression and phenotype of C. elegans.

BACKGROUND: Liquid cultures have been commonly used in space, toxicology, and pharmacology studies of Caenorhabditis elegans. However, the knowledge about transcriptomic alterations caused by liquid cultivation remains limited. Moreover, the impact of different genotypes in rapid adaptive responses to environmental changes (e.g., liquid cultivation) is often overlooked. Here, we report the transcriptomic and phenotypic responses of laboratory N2 and the wild-isolate AB1 strains after culturing P0 worms on agar plates, F1 in liquid cultures, and F2 back on agar plates. RESULTS: Significant variations were found in the gene expressions between the N2 and AB1 strains in response to liquid cultivation. The results demonstrated that 8-34% of the environmental change-induced transcriptional responses are transmitted to the subsequent generation. By categorizing the gene expressions for genotype, environment, and genotype-environment interactions, we identified that the genotype has a substantial impact on the adaptive responses. Functional analysis of the transcriptome showed correlation with phenotypical changes. For example, the N2 strain exhibited alterations in both phenotype and gene expressions for germline and cuticle in axenic liquid cultivation. We found transcript evidence to approximately 21% of the computationally predicted genes in C. elegans by exposing the worms to environmental changes. CONCLUSIONS: The presented study reveals substantial differences between N2 and AB1 strains for transcriptomic and phenotypical responses to rapid environmental changes. Our data can provide standard controls for future studies for the liquid cultivation of C. elegans and enable the discovery of condition-specific genes.

ChIP and Chips: Introducing the WormPharm for correlative studies employing pharmacology and genome-wide analyses in C. elegans.

We present the WormPharm, an automated microfluidic platform that utilizes an axenic medium to culture C. elegans. The WormPharm is capable of sustaining C. elegans for extended periods, while recording worm development and growth with high temporal resolution ranging from seconds to minutes over several days to months. We demonstrate the utility of the device to monitor C. elegans growth in the presence of varying doses of nicotine and alcohol. Furthermore, we show that C. elegans cultured in the WormPharm are amendable for high-throughput genomic assays, i.e. chromatin-immunoprecipitation followed by next generation sequencing, and confirm that nematodes grown in monoxenic and axenic cultures exhibit genetic modifications that correlate with observed phenotypes. The WormPharm is a powerful tool for analyzing the effects of chemical, nutritional and environmental variations on organism level responses in conjunction with genome-wide changes in C. elegans.

Bioinformatics Knowledge Map for Analysis of Beta-Catenin Function in Cancer.

Given the wealth of bioinformatics resources and the growing complexity of biological information, it is valuable to integrate data from disparate sources to gain insight into the role of genes/proteins in health and disease. We have developed a bioinformatics framework that combines literature mining with information from biomedical ontologies and curated databases to create knowledge "maps" of genes/proteins of interest. We applied this approach to the study of beta-catenin, a cell adhesion molecule and transcriptional regulator implicated in cancer. The knowledge map includes post-translational modifications (PTMs), protein-protein interactions, disease-associated mutations, and transcription factors co-activated by beta-catenin and their targets and captures the major processes in which beta-catenin is known to participate. Using the map, we generated testable hypotheses about beta-catenin biology in normal and cancer cells. By focusing on proteins participating in multiple relation types, we identified proteins that may participate in feedback loops regulating beta-catenin transcriptional activity. By combining multiple network relations with PTM proteoform-specific functional information, we proposed a mechanism to explain the observation that the cyclin dependent kinase CDK5 positively regulates beta-catenin co-activator activity. Finally, by overlaying cancer-associated mutation data with sequence features, we observed mutation patterns in several beta-catenin PTM sites and PTM enzyme binding sites that varied by tissue type, suggesting multiple mechanisms by which beta-catenin mutations can contribute to cancer. The approach described, which captures rich information for molecular species from genes and proteins to PTM proteoforms, is extensible to other proteins and their involvement in disease.

RLIMS-P: an online text-mining tool for literature-based extraction of protein phosphorylation information.

Protein phosphorylation is central to the regulation of most aspects of cell function. Given its importance, it has been the subject of active research as well as the focus of curation in several biological databases. We have developed Rule-based Literature Mining System for protein Phosphorylation (RLIMS-P), an online text-mining tool to help curators identify biomedical research articles relevant to protein phosphorylation. The tool presents information on protein kinases, substrates and phosphorylation sites automatically extracted from the biomedical literature. The utility of the RLIMS-P Web site has been evaluated by curators from Phospho.ELM, PhosphoGRID/BioGrid and Protein Ontology as part of the BioCreative IV user interactive task (IAT). The system achieved F-scores of 0.76, 0.88 and 0.92 for the extraction of kinase, substrate and phosphorylation sites, respectively, and a precision of 0.88 in the retrieval of relevant phosphorylation literature. The system also received highly favorable feedback from the curators in a user survey. Based on the curators' suggestions, the Web site has been enhanced to improve its usability. In the RLIMS-P Web site, phosphorylation information can be retrieved by PubMed IDs or keywords, with an option for selecting targeted species. The result page displays a sortable table with phosphorylation information. The text evidence page displays the abstract with color-coded entity mentions and includes links to UniProtKB entries via normalization, i.e., the linking of entity mentions to database identifiers, facilitated by the GenNorm tool and by the links to the bibliography in UniProt. Log in and editing capabilities are offered to any user interested in contributing to the validation of RLIMS-P results. Retrieved phosphorylation information can also be downloaded in CSV format and the text evidence in the BioC format. RLIMS-P is freely available. DATABASE URL: http://www.proteininformationresource.org/rlimsp/

Protein Ontology: a controlled structured network of protein entities.

The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to UniProtKB in that PRO's organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO's representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments.