Shiri Freilich

Construction, Visualisation, and Clustering of Transcription Networks from Microarray Expression Data

Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express 3D.

Competitive and cooperative metabolic interactions in bacterial communities

Revealing the ecological principles that shape communities is a major challenge of the post-genomic era. To date, a systematic approach for describing inter-species interactions has been lacking. Here we independently predict the competitive and cooperative potential between 6,903 bacterial pairs derived from a collection of 118 species metabolic models. We chart an intricate association between competition and cooperation indicating that the cooperative potential is maximized at moderate levels of resource overlap. Utilizing ecological data from 2,801 samples, we explore the associations between bacterial interactions and coexistence patterns. The high level of competition observed between species with mutual-exclusive distribution patterns supports the role of competition in community assembly. Cooperative interactions are typically unidirectional with no obvious benefit to the giver. However, within their natural communities, bacteria typically form close cooperative loops resulting in indirect benefit to all species involved. These findings are important for the future design of consortia optimized towards bioremediation and bio-production applications.

The COP9 signalosome is essential for development of Drosophila melanogaster.

The COP9 signalosome (originally described as the COP9 complex) is an essential multi-subunit repressor of light-regulated development in plants [1] [2]. It has also been identified in mammals, though its role remains obscure [3] [4] [5]. This complex is similar to the regulatory lid of the proteasome and eIF3 [5] [9] [10] [11] [12] and several of its subunits are known to be involved in kinase signaling pathways [4] [6] [7] [8]. No proteins homologous to COP9 signalosome components were identified in the Saccharomyces cerevisiae genome, suggesting that the COP9 signalosome is specific for multi-cellular differentiation [13]. In order to reveal the developmental function of the COP9 signalosome in animals, we have isolated Drosophila melanogaster genes encoding eight subunits of the COP9 signalosome, and have shown by co-immunoprecipitation and gel-filtration analysis that these proteins are components of the Drosophila COP9 signalosome. Yeast two-hybrid assays indicated that several of these proteins interact, some through the PCI domain. Disruption of one of the subunits by either a P-element insertion or deletion of the gene caused lethality at the late larval or pupal stages. This lethality is probably a result of numerous pleiotropic effects. Our results indicate that the COP9 signalosome is conserved in invertebrates and that it has an essential role in animal development.

Challenges in microbial ecology: building predictive understanding of community function and dynamics

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Emergence, development and diversification of the TGF- β signalling pathway within the animal kingdom

BackgroundThe question of how genomic processes, such as gene duplication, give rise to co-ordinated organismal properties, such as emergence of new body plans, organs and lifestyles, is of importance in developmental and evolutionary biology. Herein, we focus on the diversification of the transforming growth factor-β (TGF-β) pathway – one of the fundamental and versatile metazoan signal transduction engines.ResultsAfter an investigation of 33 genomes, we show that the emergence of the TGF-β pathway coincided with appearance of the first known animal species. The primordial pathway repertoire consisted of four Smads and four receptors, similar to those observed in the extant genome of the early diverging tablet animal (Trichoplax adhaerens). We subsequently retrace duplications in ancestral genomes on the lineage leading to humans, as well as lineage-specific duplications, such as those which gave rise to novel Smads and receptors in teleost fishes. We conclude that the diversification of the TGF-β pathway can be parsimoniously explained according to the 2R model, with additional rounds of duplications in teleost fishes. Finally, we investigate duplications followed by accelerated evolution which gave rise to an atypical TGF-β pathway in free-living bacterial feeding nematodes of the genus Rhabditis.ConclusionOur results challenge the view of well-conserved developmental pathways. The TGF-β signal transduction engine has expanded through gene duplication, continually adopting new functions, as animals grew in anatomical complexity, colonized new environments, and developed an active immune system.

Metabolic-network-driven analysis of bacterial ecological strategies

BackgroundThe growth-rate of an organism is an important phenotypic trait, directly affecting its ability to survive in a given environment. Here we present the first large scale computational study of the association between ecological strategies and growth rate across 113 bacterial species, occupying a variety of metabolic habitats. Genomic data are used to reconstruct the species metabolic networks and habitable metabolic environments. These reconstructions are then used to investigate the typical ecological strategies taken by organisms in terms of two basic species-specific measures: metabolic variability - the ability of a species to survive in a variety of different environments; and co-habitation score vector - the distribution of other species that co-inhabit each environment.ResultsWe find that growth rate is significantly correlated with metabolic variability and the level of co-habitation (that is, competition) encountered by an organism. Most bacterial organisms adopt one of two main ecological strategies: a specialized niche with little co-habitation, associated with a typically slow rate of growth; or ecological diversity with intense co-habitation, associated with a typically fast rate of growth.ConclusionsThe pattern observed suggests a universal principle where metabolic flexibility is associated with a need to grow fast, possibly in the face of competition. This new ability to produce a quantitative description of the growth rate-metabolism-community relationship lays a computational foundation for the study of a variety of aspects of the communal metabolic life.

Association between translation efficiency and horizontal gene transfer within microbial communities

Horizontal gene transfer (HGT) is a major force in microbial evolution. Previous studies have suggested that a variety of factors, including restricted recombination and toxicity of foreign gene products, may act as barriers to the successful integration of horizontally transferred genes. This study identifies an additional central barrier to HGT—the lack of co-adaptation between the codon usage of the transferred gene and the tRNA pool of the recipient organism. Analyzing the genomic sequences of more than 190 microorganisms and the HGT events that have occurred between them, we show that the number of genes that were horizontally transferred between organisms is positively correlated with the similarity between their tRNA pools. Those genes that are better adapted to the tRNA pools of the target genomes tend to undergo more frequent HGT. At the community (or environment) level, organisms that share a common ecological niche tend to have similar tRNA pools. These results remain significant after controlling for diverse ecological and evolutionary parameters. Our analysis demonstrates that there are bi-directional associations between the similarity in the tRNA pools of organisms and the number of HGT events occurring between them. Similar tRNA pools between a donor and a host tend to increase the probability that a horizontally acquired gene will become fixed in its new genome. Our results also suggest that frequent HGT may be a homogenizing force that increases the similarity in the tRNA pools of organisms within the same community.

Relationship between the tissue-specificity of mouse gene expression and the evolutionary origin and function of the proteins

BackgroundThe combination of complete genome sequence information with expression data enables us to characterize the relationship between a proteins evolutionary origin or functional category and its expression pattern. In this study, mouse proteins were assigned into functional and phyletic groups and the gene expression patterns of the different protein groupings were examined by microarray analysis in various mouse tissues.ResultsOur results suggest that the proteins that are universally distributed in all tissues are predominantly enzymes and transporters. In contrast, the tissue-specific set is dominated by regulatory proteins (signal transduction and transcription factors). An increased tendency to tissue-specificity is observed for metazoan-specific proteins. As the composition of the phyletic groups highly correlates with that of the functional groups, the data were tested in order to determine which of the two factors - function or phyletic age - is dominant in shaping the expression profile of a protein. The observed differences in expression patterns of genes between functional groups were found mainly to reflect their different phyletic origin. The connection between tissue specificity and phyletic age cannot be explained by the recent rate of evolution. Finally, although metazoan-specific proteins tend to be tissue-specific compared with phyletically conserved proteins present in all domains of life, many such universal proteins are also tissue-specific.ConclusionThe minimal cellular transcriptome of the metazoan cell differs from that of the ancestral unicellular eukaryote: new functions were added (metazoan-specific proteins), whilst other functions became specialized and no longer took place in all cells (tissue-specific pre-metazoan proteins).

Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species

The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic requirements, or directly due to an adaptive selection in favor of genes that allow a species to endure genetic perturbations. Stratifying the individual effects of each origin requires one to study the pertaining evolutionary forces across many species under diverse conditions. Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments. We provide evidence that variations among species in their level of robustness reflect ecological adaptations. We decouple metabolic robustness into two components and quantify the extents of each: the first, environmental-dependent, is responsible for at least 20% of the non-essential reactions and its extent is associated with the species lifestyle (specialized/generalist); the second, environmental-independent, is associated (correlationu200a=u200a∼0.6) with the intrinsic metabolic capacities of a species—higher robustness is observed in fast growers or in organisms with an extensive production of secondary metabolites. Finally, we identify reactions that are uniquely susceptible to perturbations in human pathogens, potentially serving as novel drug-targets.

NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation

BackgroundHost-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm.ResultsNetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbe cooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasitic or a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair of microbial organisms’ niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metrics as well as a list of potential syntrophic metabolic compounds.ConclusionsThe Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-based tool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html.