Species-specific transcriptomic network inference of interspecies interactions

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Abstract

The advent of high-throughput ‘omics approaches coupled with computational analyses to reconstruct individual genomes from metagenomes provides a basis for species-resolved functional studies. Here, a mutual information approach was applied to build a gene association network of a commensal consortium, in which a unicellular cyanobacterium Thermosynechococcus elongatus BP1 supported the heterotrophic growth of Meiothermus ruber strain A. Specifically, we used the context likelihood of relatedness (CLR) algorithm to generate a gene association network from 25 transcriptomic datasets representing distinct growth conditions. The resulting interspecies network revealed a number of linkages between genes in each species. While many of the linkages were supported by the existing knowledge of phototroph-heterotroph interactions and the metabolism of these two species several new interactions were inferred as well. These include linkages between amino acid synthesis and uptake genes, as well as carbohydrate and vitamin metabolism, terpenoid metabolism and cell adhesion genes. Further topological examination and functional analysis of specific gene associations suggested that the interactions are likely to center around the exchange of energetically costly metabolites between T. elongatus and M. ruber. Both the approach and conclusions derived from this work are widely applicable to microbial communities for identification of the interactions between species and characterization of community functioning as a whole.

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A protocol for generating a high-quality genome-scale metabolic reconstruction.

Ines Thiele, Bernhard Ø Palsson (2010)

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

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Initial community evenness favours functionality under selective stress.

Lieven Wittebolle, Massimo Marzorati, Lieven Clement … (2009)

Owing to the present global biodiversity crisis, the biodiversity-stability relationship and the effect of biodiversity on ecosystem functioning have become major topics in ecology. Biodiversity is a complex term that includes taxonomic, functional, spatial and temporal aspects of organismic diversity, with species richness (the number of species) and evenness (the relative abundance of species) considered among the most important measures. With few exceptions (see, for example, ref. 6), the majority of studies of biodiversity-functioning and biodiversity-stability theory have predominantly examined richness. Here we show, using microbial microcosms, that initial community evenness is a key factor in preserving the functional stability of an ecosystem. Using experimental manipulations of both richness and initial evenness in microcosms with denitrifying bacterial communities, we found that the stability of the net ecosystem denitrification in the face of salinity stress was strongly influenced by the initial evenness of the community. Therefore, when communities are highly uneven, or there is extreme dominance by one or a few species, their functioning is less resistant to environmental stress. Further unravelling how evenness influences ecosystem processes in natural and humanized environments constitutes a major future conceptual challenge.

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The sociobiology of biofilms.

Carey Nadell, Joao B Xavier, Kevin R. Foster (2009)

Biofilms are densely packed communities of microbial cells that grow on surfaces and surround themselves with secreted polymers. Many bacterial species form biofilms, and their study has revealed them to be complex and diverse. The structural and physiological complexity of biofilms has led to the idea that they are coordinated and cooperative groups, analogous to multicellular organisms. We evaluate this idea by addressing the findings of microbiologists from the perspective of sociobiology, including theories of collective behavior (self-organization) and social evolution. This yields two main conclusions. First, the appearance of organization in biofilms can emerge without active coordination. That is, biofilm properties such as phenotypic differentiation, species stratification and channel formation do not necessarily require that cells communicate with one another using specialized signaling molecules. Second, while local cooperation among bacteria may often occur, the evolution of cooperation among all cells is unlikely for most biofilms. Strong conflict can arise among multiple species and strains in a biofilm, and spontaneous mutation can generate conflict even within biofilms initiated by genetically identical cells. Biofilms will typically result from a balance between competition and cooperation, and we argue that understanding this balance is central to building a complete and predictive model of biofilm formation.