Exploring the genomes: From Arabidopsis to crops

Model systems have played a crucial role for understanding biological processes at genetic, molecular and systems levels. Arabidopsis thaliana is one of the best studied model species for higher plants. Large genomic resources and mutant collections made Arabidopsis an excellent source for functional and comparative genomics. Rice and Brachypodium have a great potential to become model systems for grasses. Given the agronomic importance of grass crops, it is an attractive strategy to apply knowledge from Arabidopsis to grasses. Despite many efforts successful reports are sparse. Knowledge transfer should generally work best between orthologous genes that share functionality and a common ancestor. In higher plants, however, recent genome projects revealed an active and rapid evolution of genome structure, which challenges the concept of one-to-one orthologous mates between two species. In this study, we estimated on the example of protein families that are involved in redox related processes, the impact of gene expansions on the success rate for a knowledge transfer from Arabidopsis to the grass species rice, sorghum and Brachypodium. The sparse synteny between dicot and monocot plants due to frequent rearrangements, translocations and gene losses strongly impairs and reduces the number of orthologs detectable by positional conservation. To address the limitations of sparse synteny and expanded gene families, we applied for the detection of orthologs in this study orthoMCL, a sequence-based approach that allows to group closely related paralogs into one orthologous gene cluster. For a total of 49 out of 170 Arabidopsis genes we could identify conserved copy numbers between the dicot model and the grass annotations whereas approximately one third (34.7%, 59 genes) of the selected Arabidopsis genes lack an assignment to any of the grass genome annotations. The remaining 62 Arabidopsis genes represent groups that are considerably biased in their copy numbers between Arabidopsis and all or most of the three grass genomes.     

From genomes to morphology: a view from amphioxus

Holland, P.W.H. 2010. From genomes to morphology: a view from amphioxus. —Acta Zoologica (Stockholm) 91 : 81–86 As complete genome sequences are determined from an ever‐increasing number of animal species, new opportunities are arising for comparative biology. For zoologists interested in the evolution of shape and form, however, there is a problem. The link between genome sequence and morphology is not direct and is obfuscated by complex and evolving genetic pathways, even when conserved regulatory genes are considered. Nonetheless, a large‐scale comparison of genome sequences between extant chordates reveals an intriguing parallel between genotypic and phenotypic evolution. Tunicates have highly altered genomes, with loss of ancestral genes and shuffled genetic arrangements, while vertebrate genomes are also derived through gene loss and genome duplication. The recently sequenced amphioxus genome, in contrast, reveals much greater stasis on the cephalochordate lineage, in parallel to a less derived body plan. The opportunities and challenges for relating genome evolution to morphological evolution are discussed.     

From genomes to in silico cells via metabolic networks

Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of phenotypes from phenotypic arrays and in gene-deletion studies. They have also been used for gene expression analysis guided by metabolic network structure, leading to the identification of commonly regulated genes. Thus, genome-scale metabolic modeling currently stands out as one of the most promising approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components.     

From systems biology to systems biomedicine

Systems Biology is about combining theory, technology, and targeted experiments in a way that drives not only data accumulation but knowledge as well. The challenge in Systems Biomedicine is to furthermore translate mechanistic insights in biological systems to clinical application, with the central aim of improving patients' quality of life. The challenge is to find theoretically well-chosen models for the contextually correct and intelligible representation of multi-scale biological systems. In this review, we discuss the current state of Systems Biology, highlight the emergence of Systems Biomedicine, and highlight some of the topics and views that we think are important for the efficient application of Systems Theory in Biomedicine.     

From cancer genomes to cancer models: bridging the gaps

Cancer genome projects are now being expanded in an attempt to provide complete landscapes of the mutations that exist in tumours. Although the importance of cataloguing genome variations is well recognized, there are obvious difficulties in bridging the gaps between high‐throughput resequencing information and the molecular mechanisms of cancer evolution. Here, we describe the current status of the high‐throughput genomic technologies, and the current limitations of the associated computational analysis and experimental validation of cancer genetic variants. We emphasize how the current cancer‐evolution models will be influenced by the high‐throughput approaches, in particular through efforts devoted to monitoring tumour progression, and how, in turn, the integration of data and models will be translated into mechanistic knowledge and clinical applications.     

From genes to genomes: beyond biodiversity in Spain's Rio Tinto

Spain's Rio Tinto, or Red River, an example of an extremely acidic (pH 1.7-2.5) environment with a high metal content, teems with prokaryotic and eukaryotic microbial life. Our recent studies based on small-subunit rRNA genes reveal an unexpectedly high eukaryotic phylogenetic diversity in the river when compared to the relatively low prokaryotic diversity. Protists can therefore thrive in and dominate extremely acidic, heavy-metal-laden environments. Further, because we have discovered protistan acidophiles closely related to neutrophiles, we can hypothesize that the transition from neutral to acidic environments occurs rapidly over geological time scales. How have these organisms adapted to such environments? We are currently exploring the alterations in physiological mechanisms that might allow for growth of eukaryotic microbes at acid extremes. To this end, we are isolating phylogenetically diverse protists in order to characterize and compare ion-transporting ATPases from cultured acidophiles with those from neutrophilic counterparts. We predict that special properties of these ion transporters allow protists to survive in the Rio Tinto.     

From genomes to cures: the start of a long journey

The EMBO/EMBL Science and Society Conference 'From Genomes to Cures', was held in Heidelberg, Germany, 16-18 November 2001.     

From barcodes to genomes: extending the concept of DNA barcoding

DNA barcoding has had a major impact on biodiversity science. The elegant simplicity of establishing massive scale databases for a few barcode loci is continuing to change our understanding of species diversity patterns, and continues to enhance human abilities to distinguish among species. Capitalizing on the developments of next generation sequencing technologies and decreasing costs of genome sequencing, there is now the opportunity for the DNA barcoding concept to be extended to new kinds of genomic data. We illustrate the benefits and capacity to do this, and also note the constraints and barriers to overcome before it is truly scalable. We advocate a twin track approach: (i) continuation and acceleration of global efforts to build the DNA barcode reference library of life on earth using standard DNA barcodes and (ii) active development and application of extended DNA barcodes using genome skimming to augment the standard barcoding approach.     

From annotated genomes to metabolic flux models and kinetic parameter fitting

Significant advances in system-level modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steady-state models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of whole-cell kinetic parameters.     

From protein networks to biological systems

A system-level understanding of any biological process requires a map of the relationships among the various molecules involved. Technologies to detect and predict protein interactions have begun to produce very large maps of protein interactions, some including most of an organism's proteins. These maps can be used to study how proteins work together to form molecular machines and regulatory pathways. They also provide a framework for constructing predictive models of how information and energy flow through biological networks. In many respects, protein interaction maps are an entrée into systems biology.     

From genomes to societies: a holistic view of determinants of human health

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