Using functional annotations to study pairwise interactions in urinary tract infection communities

The behaviour of microbial communities depends on environmental factors and on the interactions of the community members. This is also the case for urinary tract infection (UTI) microbial communities. Here, we devise a computational approach that uses indices of complementarity and competition based on metabolic gene annotation to rapidly predict putative interactions between pair of organisms with the aim to explain pairwise growth effects. We apply our method to 66 genomes selected from online databases, which belong to 6 genera representing members of UTI communities. This resulted in a selection of metabolic pathways with high correlation for each pairwise combination between a complementarity index and the experimentally derived growth data. Our results indicated that Enteroccus spp. were most complemented in its metabolism by the other members of the UTI community. This suggests that the growth of Enteroccus spp. can potentially be enhanced by complementary metabolites produced by other community members. We tested a few putative predicted interactions by experimental supplementation of the relevant predicted metabolites. As predicted by our method, folic acid supplementation led to the increase in the population density of UTI Enterococcus isolates. Overall, we believe our method is a rapid initial in silico screening for the prediction of metabolic interactions in microbial communities

A genome-wide genetic map of NB-LRR disease resistance loci in potato

Like all plants, potato has evolved a surveillance system consisting of a large array of genes encoding for immune receptors that confer resistance to pathogens and pests. The majority of these so-called resistance or R proteins belong to the super-family that harbour a nucleotide binding and a leucine-rich-repeat domain (NB-LRR). Here, sequence information of the conserved NB domain was used to investigate the genome-wide genetic distribution of the NB-LRR resistance gene loci in potato. We analysed the sequences of 288 unique BAC clones selected using filter hybridisation screening of a BAC library of the diploid potato clone RH89-039-16 (S. tuberosum ssp. tuberosum) and a physical map of this BAC library. This resulted in the identification of 738 partial and full-length NB-LRR sequences. Based on homology of these sequences with known resistance genes, 280 and 448 sequences were classified as TIR-NB-LRR (TNL) and CC-NB-LRR (CNL) sequences, respectively. Genetic mapping revealed the presence of 15 TNL and 32 CNL loci. Thirty-six are novel, while three TNL loci and eight CNL loci are syntenic with previously identified functional resistance genes. The genetic map was complemented with 68 universal CAPS markers and 82 disease resistance trait loci described in literature, providing an excellent template for genetic studies and applied research in potato

Ecology dictates evolution? About the importance of genetic and ecological constraints in adaptation

The topography of the adaptive landscape is a major determinant of the course of evolution. In this review we use the adaptive landscape metaphor to highlight the effect of ecology on evolution. We describe how ecological interactions modulate the shape of the adaptive landscape, and how this affects adaptive constraints. We focus on microbial communities as model systems

Ecology dictates evolution?:About the importance of genetic and ecological constraints in adaptation

The topography of the adaptive landscape is a major determinant of the course of evolution. In this review we use the adaptive landscape metaphor to highlight the effect of ecology on evolution. We describe how ecological interactions modulate the shape of the adaptive landscape, and how this affects adaptive constraints. We focus on microbial communities as model systems. Copyright (C) EPLA, 201

Breaking evolutionary constraint with a tradeoff ratchet

International audienceEpistatic interactions can frustrate and shape evolutionary change. Indeed, phenotypes may fail to evolve when essential mutations are only accessible through positive selection if they are fixed simultaneously. How environmental variability affects such constraints is poorly understood. Here, we studied genetic constraints in fixed and fluctuating environments using the Escherichia coli lac operon as a model system for genotype–environment interactions. We found that, in different fixed environments, all trajectories that were reconstructed by applying point mutations within the transcription factor–operator interface became trapped at suboptima, where no additional improvements were possible. Paradoxically, repeated switching between these same environments allows unconstrained adaptation by continuous improvements. This evolutionary mode is explained by pervasive cross-environmental tradeoffs that reposition the peaks in such a way that trapped genotypes can repeatedly climb ascending slopes and hence, escape adaptive stasis. Using a Markov approach, we developed a mathematical framework to quantify the landscape-crossing rates and show that this ratchet-like adaptive mechanism is robust in a wide spectrum of fluctuating environments. Overall, this study shows that genetic constraints can be overcome by environmental change and that cross-environmental tradeoffs do not necessarily impede but also, can facilitate adaptive evolution. Because tradeoffs and environmental variability are ubiquitous in nature, we speculate this evolutionary mode to be of general relevance

Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience

Metabolic interactions shape a community's phenotype

Metabolic interactions between auxotrophs and prototrophs in microbial communities are understudied. Yu et al. showed how intracellular as well as intercellular metabolism affects community fitness in the absence and presence of abiotic stress, that is, drugs

Microbial evolutionary medicine: From theory to clinical practice

Medicine and clinical microbiology have traditionally attempted to identify and eliminate the agents that cause disease. However, this traditional approach is becoming inadequate for dealing with a changing disease landscape. Major challenges to human health are non-communicable chronic diseases, often driven by altered immunity and inflammation, and communicable infections from agents which harbour antibiotic resistance. This Review focuses on the so-called evolutionary medicine framework, to study how microbial communities influence human health. The evolutionary medicine framework aims to predict and manipulate microbial effects on human health by integrating ecology, evolutionary biology, microbiology, bioinformatics, and clinical expertise. We focus on the potential of evolutionary medicine to address three key challenges: detecting microbial transmission, predicting antimicrobial resistance, and understanding microbe–microbe and human–microbe interactions in health and disease, in the context of the microbiome

Metabolic interactions shape a community's phenotype

Metabolic interactions between auxotrophs and prototrophs in microbial communities are understudied. Yu et al. showed how intracellular as well as intercellular metabolism affects community fitness in the absence and presence of abiotic stress, that is, drugs