Digital gene expression analysis of the zebra finch genome

Background: In order to understand patterns of adaptation and molecular evolution it is important to quantify both variation in gene expression and nucleotide sequence divergence. Gene expression profiling in non-model organisms has recently been facilitated by the advent of massively parallel sequencing technology. Here we investigate tissue specific gene expression patterns in the zebra finch (Taeniopygia guttata) with special emphasis on the genes of the major histocompatibility complex (MHC). Results: Almost 2 million 454-sequencing reads from cDNA of six different tissues were assembled and analysed. A total of 11,793 zebra finch transcripts were represented in this EST data, indicating a transcriptome coverage of about 65%. There was a positive correlation between the tissue specificity of gene expression and non-synonymous to synonymous nucleotide substitution ratio of genes, suggesting that genes with a specialised function are evolving at a higher rate (or with less constraint) than genes with a more general function. In line with this, there was also a negative correlation between overall expression levels and expression specificity of contigs. We found evidence for expression of 10 different genes related to the MHC. MHC genes showed relatively tissue specific expression levels and were in general primarily expressed in spleen. Several MHC genes, including MHC class I also showed expression in brain. Furthermore, for all genes with highest levels of expression in spleen there was an overrepresentation of several gene ontology terms related to immune function. Conclusions: Our study highlights the usefulness of next-generation sequence data for quantifying gene expression in the genome as a whole as well as in specific candidate genes. Overall, the data show predicted patterns of gene expression profiles and molecular evolution in the zebra finch genome. Expression of MHC genes in particular, corresponds well with expression patterns in other vertebrates

Marine Biotechnology: A New Vision and Strategy for Europe

Marine Board-ESF The Marine Board provides a pan-European platform for its member organisations to develop common priorities, to advance marine research, and to bridge the gap between science and policy in order to meet future marine science challenges and opportunities. The Marine Board was established in 1995 to facilitate enhanced cooperation between European marine science organisations (both research institutes and research funding agencies) towards the development of a common vision on the research priorities and strategies for marine science in Europe. In 2010, the Marine Board represents 30 Member Organisations from 19 countries. The Marine Board provides the essential components for transferring knowledge for leadership in marine research in Europe. Adopting a strategic role, the Marine Board serves its Member Organisations by providing a forum within which marine research policy advice to national agencies and to the European Commission is developed, with the objective of promoting the establishment of the European Marine Research Area

MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

Transcriptomics in ecotoxicology

The emergence of analytical tools for high-throughput screening of biomolecules has revolutionized the way in which toxicologists explore the impact of chemicals or other stressors on organisms. One of the most developed and routinely applied high-throughput analysis approaches is transcriptomics, also often referred to as gene expression profiling. The transcriptome represents all RNA molecules, including the messenger RNA (mRNA), which constitutes the building blocks for translating DNA into amino acids to form proteins. The entirety of mRNA is a mirror of the genes that are actively expressed in a cell or an organism at a given time. This in turn allows one to deduce how organisms respond to changes in the external environment. In this article we explore how transcriptomics is currently applied in ecotoxicology and highlight challenges and trends

How to Predict Molecular Interactions between Species?

Organisms constantly interact with other species through physical contact which leads to chan-ges on the molecular level, for example the transcriptome. These changes can be monitored forall genes, with the help of high-throughput experiments such as RNA-seq or microarrays. Theadaptation of the gene expression to environmental changes within cells is mediated throughcomplex gene regulatory networks. Often, our knowledge of these networks is incomplete. Netw-ork inference predicts gene regulatory interactions based on transcriptome data. An emergingapplication of high-throughput transcriptome studies are dual transcriptomics experiments. Here,the transcriptome of two or more interacting species is measured simultaneously. Based ona dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candidaalbicans, the software tool NetGenerator was applied to predict an inter-species gene regulatorynetwork. To promote further investigations of molecular inter-species interactions, we recentlydiscussed dual RNA-seq experiments for host-pathogen interactions and extended the appliedtool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use ofmeasurement variances in the algorithmic procedure and accepts gene expression time seriesdata with missing values. Additionally, we tested multiple modeling scenarios regarding the stimulifunctions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015)and put it into a broader context. We review various studies making use of the dual transcriptomicsapproach to investigate the molecular basis of interacting species. Besides the application tohost-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic andcommensalistic interactions. Furthermore, we give a short introduction into additional approachesfor the prediction of gene regulatory networks and discuss their application to dual transcriptomicsdata. We conclude that the application of network inference on dual-transcriptomics data is apromising approach to predict molecular inter-species interactions

Landscape transcriptomics as a tool for addressing global change effects across diverse species

Landscape transcriptomics is an emerging field studying how genome-wide expression patterns reflect dynamic landscape-scale environmental drivers, including habitat, weather, climate, and contaminants, and the subsequent effects on organismal function. This field is benefitting from advancing and increasingly accessible molecular technologies, which in turn are allowing the necessary characterization of transcriptomes from wild individuals distributed across natural landscapes. This research is especially important given the rapid pace of anthropogenic environmental change and potential impacts that span levels of biological organization. We discuss three major themes in landscape transcriptomic research: connecting transcriptome variation across landscapes to environmental variation, generating and testing hypotheses about the mechanisms and evolution of transcriptomic responses to the environment, and applying this knowledge to species conservation and management. We discuss challenges associated with this approach and suggest potential solutions. We conclude that landscape transcriptomics has great promise for addressing fundamental questions in organismal biology, ecology, and evolution, while providing tools needed for conservation and management of species

Integration of molecular functions at the ecosystemic level: breakthroughs and future goals of environmental genomics and post-genomics

Environmental genomics and genome-wide expression approaches deal with large-scale sequence-based information obtained from environmental samples, at organismal, population or community levels. To date, environmental genomics, transcriptomics and proteomics are arguably the most powerful approaches to discover completely novel ecological functions and to link organismal capabilities, organism–environment interactions, functional diversity, ecosystem processes, evolution and Earth history. Thus, environmental genomics is not merely a toolbox of new technologies but also a source of novel ecological concepts and hypotheses. By removing previous dichotomies between ecophysiology, population ecology, community ecology and ecosystem functioning, environmental genomics enables the integration of sequence-based information into higher ecological and evolutionary levels. However, environmental genomics, along with transcriptomics and proteomics, must involve pluridisciplinary research, such as new developments in bioinformatics, in order to integrate high-throughput molecular biology techniques into ecology. In this review, the validity of environmental genomics and post-genomics for studying ecosystem functioning is discussed in terms of major advances and expectations, as well as in terms of potential hurdles and limitations. Novel avenues for improving the use of these approaches to test theory-driven ecological hypotheses are also explored

Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes

The advent of genomic-, transcriptomic- and proteomic-based approaches has revolutionized our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New interdisciplinary approaches are needed to move from this descriptive level to improved quantitative, process-level understanding of the roles of marine microbes in biogeochemical cycles and of the impact of environmental change on the marine microbial ecosystem. Linking studies at levels from the genome to the organism, to ecological strategies and organism and ecosystem response, requires new modelling approaches. Key to this will be a fundamental shift in modelling scale that represents micro-organisms from the level of their macromolecular components. This will enable contact with omics data sets and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximization and evolutionary constraints. This way forward will build on ecological approaches that identify key organism traits and systems biology approaches that integrate traditional physiological measurements with new insights from omics. It will rely on developing an improved understanding of ecophysiology to understand quantitatively environmental controls on microbial growth strategies. It will also incorporate results from experimental evolution studies in the representation of adaptation. The resulting ecosystem-level models can then evaluate our level of understanding of controls on ecosystem structure and function, highlight major gaps in understanding and help prioritize areas for future research programs. Ultimately, this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers