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.

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.

The Good and the Bad: Ecological Interaction Measurements Between the Urinary Microbiota and Uropathogens.

The human body harbors numerous populations of microorganisms in various ecological niches. Some of these microbial niches, such as the human gut and the respiratory system, are well studied. One system that has been understudied is the urinary tract, primarily because it has been considered sterile in the absence of infection. Thanks to modern sequencing and enhanced culture techniques, it is now known that a urinary microbiota exists. The implication is that these species live as communities in the urinary tract, forming microbial ecosystems. However, the interactions between species in such an ecosystem remains unknown. Various studies in different parts of the human body have highlighted the ability of the pre-existing microbiota to alter the course of infection by impacting the pathogenicity of bacteria either directly or indirectly. For the urinary tract, the effect of the resident microbiota on uropathogens and the phenotypic microbial interactions is largely unknown. No studies have yet measured the response of uropathogens to the resident urinary bacteria. In this study, we investigate the interactions between uropathogens, isolated from elderly individuals suffering from UTIs, and bacteria isolated from the urinary tract of asymptomatic individuals using growth measurements in conditioned media. We observed that bacteria isolated from individuals with UTI-like symptoms and bacteria isolated from asymptomatic individuals can affect each other's growth; for example, bacteria isolated from symptomatic individuals affect the growth of bacteria isolated from asymptomatic individuals more negatively than vice versa. Additionally, we show that Gram-positive bacteria alter the growth characteristics differently compared to Gram-negative bacteria. Our results are an early step in elucidating the role of microbial interactions in urinary microbial ecosystems that harbor both uropathogens and pre-existing microbiota.

Predicting Evolution Using Regulatory Architecture.

The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization-in molecular recognition, within a single regulatory network, and between different networks-providing first indications of predictable features of evolutionary constraint.

Fast identification of Escherichia coli in urinary tract infections using a virulence gene based PCR approach in a novel thermal cycler.

Uropathogenic Escherichia coli (UPEC) is the most common causal agent of urinary tract infections (UTIs) in humans. Currently, clinical detection methods take hours (dipsticks) to days (culturing methods), limiting rapid intervention. As an alternative, the use of molecular methods could improve speed and accuracy, but their applicability is complicated by high genomic variability within UPEC strains. Here, we describe a novel PCR-based method for the identification of E. coli in urine. Based on in silico screening of UPEC genomes, we selected three UPEC-specific genes predicted to be involved in pathogenesis (c3509, c3686 (yrbH) and chuA), and one E. coli-specific marker gene (uidA). We validated the method on 128 clinical (UTI) strains. Despite differential occurrences of these genes in uropathogenic E. coli, the method, when using multi-gene combinations, specifically detected the target organism across all samples. The lower detection limit, assessed with model UPEC strains, was approximately 104 CFU/ml. Additionally, the use of this method in a novel ultrafast PCR thermal cycler (Nextgen PCR) allowed a detection time from urine sampling to identification of only 52 min. This is the first study that uses such defined sets of marker genes for the detection of E. coli in UTIs. In addition, we are the first to demonstrate the potential of the Nextgen thermal cycler. Our E. coli identification method has the potential to be a rapid, reliable and inexpensive alternative for traditional methods.

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.

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.

Breaking evolutionary constraint with a tradeoff ratchet.

Epistatic 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.

Suppressive drug interactions between antifungals.

In this issue of Chemistry & Biology, Cokol and colleagues report a systematic study of drug interactions between antifungal compounds. Suppressive drug interactions occur more frequently than previously realized and come in different flavors with interesting implications.

Environmental dependence of genetic constraint.

The epistatic interactions that underlie evolutionary constraint have mainly been studied for constant external conditions. However, environmental changes may modulate epistasis and hence affect genetic constraints. Here we investigate genetic constraints in the adaptive evolution of a novel regulatory function in variable environments, using the lac repressor, LacI, as a model system. We have systematically reconstructed mutational trajectories from wild type LacI to three different variants that each exhibit an inverse response to the inducing ligand IPTG, and analyzed the higher-order interactions between genetic and environmental changes. We find epistasis to depend strongly on the environment. As a result, mutational steps essential to inversion but inaccessible by positive selection in one environment, become accessible in another. We present a graphical method to analyze the observed complex higher-order interactions between multiple mutations and environmental change, and show how the interactions can be explained by a combination of mutational effects on allostery and thermodynamic stability. This dependency of genetic constraint on the environment should fundamentally affect evolutionary dynamics and affects the interpretation of phylogenetic data.

Optimality in evolution: new insights from synthetic biology.

Whether organisms evolve to perform tasks optimally has intrigued biologists since Lamarck and Darwin. Optimality models have been used to study diverse properties such as shape, locomotion, and behavior. However, without access to the genetic underpinnings or the ability to manipulate biological functions, it has been difficult to understand an organism's intrinsic potential and limitations. Now, novel experiments are overcoming these technical obstacles and have begun to test optimality in more quantitative terms. With the use of simple model systems, genetic engineering, and mathematical modeling, one can independently quantify the prevailing selective pressures and optimal phenotypes. These studies have given an exciting view into the evolutionary potential and constraints of biological systems, and hold the promise to further test the limits of predicting future evolutionary change.

Tradeoffs and optimality in the evolution of gene regulation.

Cellular regulation is believed to evolve in response to environmental variability. However, this has been difficult to test directly. Here, we show that a gene regulation system evolves to the optimal regulatory response when challenged with variable environments. We engineered a genetic module subject to regulation by the lac repressor (LacI) in E. coli, whose expression is beneficial in one environmental condition and detrimental in another. Measured tradeoffs in fitness between environments predict the competition between regulatory phenotypes. We show that regulatory evolution in adverse environments is delayed at specific boundaries in the phenotype space of the regulatory LacI protein. Once this constraint is relieved by mutation, adaptation proceeds toward the optimum, yielding LacI with an altered allosteric mechanism that enables an opposite response to its regulatory ligand IPTG. Our results indicate that regulatory evolution can be understood in terms of tradeoff optimization theory.

Optimality and evolution of transcriptionally regulated gene expression.

BACKGROUND: How transcriptionally regulated gene expression evolves under natural selection is an open question. The cost and benefit of gene expression are the driving factors. While the former can be determined by gratuitous induction, the latter is difficult to measure directly. RESULTS: We addressed this problem by decoupling the regulatory and metabolic function of the Escherichia coli lac system, using an inducer that cannot be metabolized and a carbon source that does not induce. Growth rate measurements directly identified the induced expression level that maximizes the metabolism benefits minus the protein production costs, without relying on models. Using these results, we established a controlled mismatch between sensing and metabolism, resulting in sub-optimal transcriptional regulation with the potential to improve by evolution. Next, we tested the evolutionary response by serial transfer. Constant environments showed cells evolving to the predicted expression optimum. Phenotypes with decreased expression emerged several hundred generations later than phenotypes with increased expression, indicating a higher genetic accessibility of the latter. Environments alternating between low and high expression demands resulted in overall rather than differential changes in expression, which is explained by the concave shape of the cross-environmental tradeoff curve that limits the selective advantage of altering the regulatory response. CONCLUSIONS: This work indicates that the decoupling of regulatory and metabolic functions allows one to directly measure the costs and benefits that underlie the natural selection of gene regulation. Regulated gene expression is shown to evolve within several hundreds of generations to optima that are predicted by these costs and benefits. The results provide a step towards a quantitative understanding of the adaptive origins of regulatory systems.

Revealing evolutionary pathways by fitness landscape reconstruction.

The concept of epistasis has since long been used to denote non-additive fitness effects of genetic changes and has played a central role in understanding the evolution of biological systems. Owing to an array of novel experimental methodologies, it has become possible to experimentally determine epistatic interactions as well as more elaborate genotype-fitness maps. These data have opened up the investigation of a host of long-standing questions in evolutionary biology, such as the ruggedness of fitness landscapes and the accessibility of mutational trajectories, the evolution of sex, and the origin of robustness and modularity. Here we review this recent and timely marriage between systems biology and evolutionary biology, which holds the promise to understand evolutionary dynamics in a more mechanistic and predictive manner.

Identification of the missing links in prokaryotic pentose oxidation pathways: evidence for enzyme recruitment.

The pentose metabolism of Archaea is largely unknown. Here, we have employed an integrated genomics approach including DNA microarray and proteomics analyses to elucidate the catabolic pathway for D-arabinose in Sulfolobus solfataricus. During growth on this sugar, a small set of genes appeared to be differentially expressed compared with growth on D-glucose. These genes were heterologously overexpressed in Escherichia coli, and the recombinant proteins were purified and biochemically studied. This showed that D-arabinose is oxidized to 2-oxoglutarate by the consecutive action of a number of previously uncharacterized enzymes, including a D-arabinose dehydrogenase, a D-arabinonate dehydratase, a novel 2-keto-3-deoxy-D-arabinonate dehydratase, and a 2,5-dioxopentanoate dehydrogenase. Promoter analysis of these genes revealed a palindromic sequence upstream of the TATA box, which is likely to be involved in their concerted transcriptional control. Integration of the obtained biochemical data with genomic context analysis strongly suggests the occurrence of pentose oxidation pathways in both Archaea and Bacteria, and predicts the involvement of additional enzyme components. Moreover, it revealed striking genetic similarities between the catabolic pathways for pentoses, hexaric acids, and hydroxyproline degradation, which support the theory of metabolic pathway genesis by enzyme recruitment.

Reconstruction of central carbon metabolism in Sulfolobus solfataricus using a two-dimensional gel electrophoresis map, stable isotope labelling and DNA microarray analysis.

In the last decade, an increasing number of sequenced archaeal genomes have become available, opening up the possibility for functional genomic analyses. Here, we reconstructed the central carbon metabolism in the hyperthermophilic crenarchaeon Sulfolobus solfataricus (glycolysis, gluconeogenesis and tricarboxylic acid cycle) on the basis of genomic, proteomic, transcriptomic and biochemical data. A 2-DE reference map of S. solfataricus grown on glucose, consisting of 325 unique ORFs in 255 protein spots, was created to facilitate this study. The map was then used for a differential expression study based on (15)N metabolic labelling (yeast extract + tryptone-grown cells (YT) vs. glucose-grown cells (G)). In addition, the expression ratio of the genes involved in carbon metabolism was studied using DNA microarrays. Surprisingly, only 3 and 14% of the genes and proteins, respectively, involved in central carbon metabolism showed a greater than two-fold change in expression level. All results are discussed in the light of the current understanding of central carbon metabolism in S. solfataricus and will help to obtain a system-wide understanding of this organism.

A fast method for quantitative proteomics based on a combination between two-dimensional electrophoresis and 15N-metabolic labelling.

We provide a method for accurate protein quantitation that uses two-dimensional (2-D) gel electrophoresis for protein separation, but does not require extensive statistical analysis of staining intensities on gels. Instead, accurate quantitation occurs on the mass spectrometer (MAS) on multiple peptides to provide statistical evidence. In an example study, Sulfolobus solfataricus cells were grown on the carbon sources glucose, fructose and glutamate. The glucose phenotype (reference) was grown on (15)N-enriched medium. Next, the glutamate and the fructose phenotypes are mixed with the reference and two 2-D gels are created. Staining intensities of gel spots in this case are used for initial, semiquantitative assessment of differential expression. On this basis, spots are selected for accurate quantitation on the MAS. A number of differentially expressed proteins were found, for example: a (25.2 +/- 8.2)-fold upregulation of isocitrate lyase and a (7.14 +/- 0.82)-fold downregulation of glucose dehydrogenase on glutamate compared to glucose. With this protocol, intergel and interlaboratory comparisons are facilitated, since the light and heavy versions of a protein are equally affected by variations in sample preparation and buffer composition. Because the statistical evidence is gathered on the MAS, the need to run vast numbers of gels is removed.

Novel approach for peptide quantitation and sequencing based on 15N and 13C metabolic labeling.

Here we describe a method for protein identification and quantification using stable isotopes via in vivo metabolic labeling of the hyperthermophilic crenarchaeon Sulfolobus solfataricus. Stable isotope labeling for quantitative proteomics is becoming increasingly popular; however, its usefulness in protein identification has not been fully exploited. We use both 15N and 13C labeling to create three different versions of the same peptide, corresponding to the unlabeled, 15N and 13C labeled versions. The peptide then appears as three different peaks in a TOF-MS scan and three corresponding sets of MS/MS spectra are obtained. With this information, the elemental carbon and nitrogen compositions for each peptide and each fragment can be calculated. When this is used as a constraint in database searching and/or de novo sequencing, the confidence of a match is increased (for an example intact peptide from 34 choices to 1). This makes the method a useful proteomic tool for both sequenced and unsequenced organisms. Furthermore, it allows for accurate protein quantitation (standard deviations over >4 peptides per protein were within 10%) of three phenotypes in one MS experiment. Abundances for each peptide are calculated by determining the relative areas of each of the three peaks in the TOF-MS spectrum.