Counteraction of antibiotic production and degradation stabilizes microbial communities.

A major challenge in theoretical ecology is understanding how natural microbial communities support species diversity, and in particular how antibiotic-producing, -sensitive and -resistant species coexist. While cyclic 'rock­paper­scissors' interactions can stabilize communities in spatial environments, coexistence in unstructured environments remains unexplained. Here, using simulations and analytical models, we show that the opposing actions of antibiotic production and degradation enable coexistence even in well-mixed environments. Coexistence depends on three-way interactions in which an antibiotic-degrading species attenuates the inhibitory interactions between two other species. These interactions enable coexistence that is robust to substantial differences in inherent species growth rates and to invasion by 'cheating' species that cease to produce or degrade antibiotics. At least two antibiotics are required for stability, with greater numbers of antibiotics enabling more complex communities and diverse dynamic behaviours ranging from stable fixed points to limit cycles and chaos. Together, these results show how multi-species antibiotic interactions can generate ecological stability in both spatially structured and mixed microbial communities, suggesting strategies for engineering synthetic ecosystems and highlighting the importance of toxin production and degradation for microbial biodiversity.

Stochastic exits from dormancy give rise to heavy-tailed distributions of descendants in bacterial populations.

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for five strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy-tailed, with a few cells effectively 'winning the jackpot' to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early-germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy-tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations and explaining variability in infection outcomes across patients.

DesignSignatures: a tool for designing primers that yields amplicons with distinct signatures.

UNLABELLED: For numerous experimental applications, PCR primers must be designed to efficiently amplify a set of homologous DNA sequences while giving rise to amplicons with maximally diverse signatures. We developed DesignSignatures to automate the process of designing primers for high-resolution melting (HRM), fragment length polymorphism (FLP) and sequencing experiments. The program also finds the best restriction enzyme to further diversify HRM or FLP signatures. This enables efficient comparison across many experimental designs in order to maximize signature diversity. AVAILABILITY AND IMPLEMENTATION: DesignSignatures is accessible as a web tool at www.DECIPHER.cee.wisc.edu, or as part of the DECIPHER open source software package for R available from BioConductor. CONTACT: kalin@discovery.wisc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Experimental Framework for Generating Evolvable Chemical Systems in the Laboratory.

Most experimental work on the origin of life has focused on either characterizing the chemical synthesis of particular biochemicals and their precursors or on designing simple chemical systems that manifest life-like properties such as self-propagation or adaptive evolution. Here we propose a new class of experiments, analogous to artificial ecosystem selection, where we select for spontaneously forming self-propagating chemical assemblages in the lab and then seek evidence of a response to that selection as a key indicator that life-like chemical systems have arisen. Since surfaces and surface metabolism likely played an important role in the origin of life, a key experimental challenge is to find conditions that foster nucleation and spread of chemical consortia on surfaces. We propose high-throughput screening of a diverse set of conditions in order to identify combinations of "food," energy sources, and mineral surfaces that foster the emergence of surface-associated chemical consortia that are capable of adaptive evolution. Identification of such systems would greatly advance our understanding of the emergence of self-propagating entities and the onset of adaptive evolution during the origin of life.

Quality filtering of Illumina index reads mitigates sample cross-talk.

BACKGROUND: Multiplexing multiple samples during Illumina sequencing is a common practice and is rapidly growing in importance as the throughput of the platform increases. Misassignments during de-multiplexing, where sequences are associated with the wrong sample, are an overlooked error mode on the Illumina sequencing platform. This results in a low rate of cross-talk among multiplexed samples and can cause detrimental effects in studies requiring the detection of rare variants or when multiplexing a large number of samples. RESULTS: We observed rates of cross-talk averaging 0.24 % when multiplexing 14 different samples with unique i5 and i7 index sequences. This cross-talk rate corresponded to 254,632 misassigned reads on a single lane of the Illumina HiSeq 2500. Notably, all types of misassignment occur at similar rates: incorrect i5, incorrect i7, and incorrect sequence reads. We demonstrate that misassignments can be nearly eliminated by quality filtering of index reads while preserving about 90 % of the original sequences. CONCLUSIONS: Cross-talk among multiplexed samples is a significant error mode on the Illumina platform, especially if samples are only separated by a single unique index. Quality filtering of index sequences offers an effective solution to minimizing cross-talk among samples. Furthermore, we propose a straightforward method for verifying the extent of cross-talk between samples and optimizing quality score thresholds that does not require additional control samples and can even be performed post hoc on previous runs.

Structure and evolution of Streptomyces interaction networks in soil and in silico.

Soil grains harbor an astonishing diversity of Streptomyces strains producing diverse secondary metabolites. However, it is not understood how this genotypic and chemical diversity is ecologically maintained. While secondary metabolites are known to mediate signaling and warfare among strains, no systematic measurement of the resulting interaction networks has been available. We developed a high-throughput platform to measure all pairwise interactions among 64 Streptomyces strains isolated from several individual grains of soil. We acquired more than 10,000 time-lapse movies of colony development of each isolate on media containing compounds produced by each of the other isolates. We observed a rich set of such sender-receiver interactions, including inhibition and promotion of growth and aerial mycelium formation. The probability that two random isolates interact is balanced; it is neither close to zero nor one. The interactions are not random: the distribution of the number of interactions per sender is bimodal and there is enrichment for reciprocity--if strain A inhibits or promotes B, it is likely that B also inhibits or promotes A. Such reciprocity is further enriched in strains derived from the same soil grain, suggesting that it may be a property of coexisting communities. Interactions appear to evolve rapidly: isolates with identical 16S rRNA sequences can have very different interaction patterns. A simple eco-evolutionary model of bacteria interacting through antibiotic production shows how fast evolution of production and resistance can lead to the observed statistical properties of the network. In the model, communities are evolutionarily unstable--they are constantly being invaded by strains with new sets of interactions. This combination of experimental and theoretical observations suggests that diverse Streptomyces communities do not represent a stable ecological state but an intrinsically dynamic eco-evolutionary phenomenon.

Investigating the eco-evolutionary tunnels for establishing cooperative communities.

Diversity is abundant among microbial communities. Understanding the assembly of diverse microbial communities is a significant challenge. One of the recent plausible explanations for the assembly involves eco-evolutionary tunnels, where species interact in the same timescale with the mutational rate. Analysis of data generated by agent-based models was used to understand these tunnels. However, modeling the interactions explicitly by dynamic models is lacking. Here, we present the modeling and characterization of eco-evolutionary tunnels that give rise to cooperative evolutionary stable communities (ESC). We find that higher order, but common interactions are sufficient for eco-evolutionary tunnels. We identify three distinct scenarios: evolution of costly cooperation, mutationally inaccessible assembly, and bistability. Biological interpretations of the models are shedding light on the evolution of cooperation. One of the important findings is that if species maximize their benefit by preying on the other strain when dominant and cooperating at intermediate abundances, the assembly process needs eco-evolutionary tunneling. In addition, we characterize the importance of genetic drift with respect to eco-evolutionary tunnels, intermittently stable communities, and the effect of high population limits on the tunnels.

Genome rhetoric and the emergence of compositional bias.

Genomes exhibit diverse patterns of species-specific GC content, GC and AT skews, codon bias, and mutation bias. Despite intensive investigations and the rapid accumulation of sequence data, the causes of these a priori different genome biases have not been agreed on and seem multifactorial and idiosyncratic. We show that these biases can arise generically from an instability of the coevolutionary dynamics between genome composition and resource allocation for translation, transcription, and replication. Thus, we offer a unifying framework for understanding and analyzing different genome biases. We develop a test of multistability of nucleotide composition of completely sequenced genomes and reveal a bistability for Borrelia burgdorferi, a genome with pronounced replication-related biases. These results indicate that evolution generates rhetoric, it improves the efficiency of the genome's communication with the cell without modifying the message, and this leads to bias.

Multi-stable bacterial communities exhibit extreme sensitivity to initial conditions.

Microbial communities can have dramatically different compositions even among similar environments. This might be due to the existence of multiple alternative stable states, yet there exists little experimental evidence supporting this possibility. Here, we gathered a large collection of absolute population abundances capturing population dynamics in one- to four-strain communities of soil bacteria with a complex life cycle in a feast-or-famine environment. This dataset led to several observations: (i) some pairwise competitions resulted in bistability with a separatrix near a 1:1 initial ratio across a range of population densities; (ii) bistability propagated to multi-stability in multispecies communities; and (iii) replicate microbial communities reached different stable states when starting close to initial conditions separating basins of attraction, indicating finite-sized regions where the dynamics are unpredictable. The generalized Lotka-Volterra equations qualitatively captured most competition outcomes but were unable to quantitatively recapitulate the observed dynamics. This was partly due to complex and diverse growth dynamics in monocultures that ranged from Allee effects to nonmonotonic behaviors. Overall, our results highlight that multi-stability might be generic in multispecies communities and, combined with ecological noise, can lead to unpredictable community assembly, even in simple environments.

Chemical Ecosystem Selection on Mineral Surfaces Reveals Long-Term Dynamics Consistent with the Spontaneous Emergence of Mutual Catalysis.

How did chemicals first become organized into systems capable of self-propagation and adaptive evolution? One possibility is that the first evolvers were chemical ecosystems localized on mineral surfaces and composed of sets of molecular species that could catalyze each other's formation. We used a bottom-up experimental framework, chemical ecosystem selection (CES), to evaluate this perspective and search for surface-associated and mutually catalytic chemical systems based on the changes in chemistry that they are expected to induce. Here, we report the results of preliminary CES experiments conducted using a synthetic "prebiotic soup" and pyrite grains, which yielded dynamical patterns that are suggestive of the emergence of mutual catalysis. While more research is needed to better understand the specific patterns observed here and determine whether they are reflective of self-propagation, these results illustrate the potential power of CES to test competing hypotheses for the emergence of protobiological chemical systems.

Emergence of evolutionarily stable communities through eco-evolutionary tunnelling.

Ecological and evolutionary dynamics of communities are inexorably intertwined. The ecological state determines the fate of newly arising mutants, and mutations that increase in frequency can reshape the ecological dynamics. Evolutionary game theory and its extensions within adaptive dynamics have been the mathematical frameworks for understanding this interplay, leading to notions such as evolutionarily stable states (ESS) in which no mutations are favoured, and evolutionary branching points near which the population diversifies. A central assumption behind these theoretical treatments has been that mutations are rare so that the ecological dynamics has time to equilibrate after every mutation. A fundamental question is whether qualitatively new phenomena can arise when mutations are frequent. Here, we describe an adaptive diversification process that robustly leads to complex ESS, despite the fact that such communities are unreachable through a step-by-step evolutionary process. Rather, the system as a whole tunnels between collective states over a short timescale. The tunnelling rate is a sharply increasing function of the rate at which mutations arise in the population. This makes the emergence of ESS communities virtually impossible in small populations, but generic in large ones. Moreover, communities emerging through this process can spatially spread as single replication units that outcompete other communities. Overall, this work provides a qualitatively new mechanism for adaptive diversification and shows that complex structures can generically evolve even when no step-by-step evolutionary path exists.

Phenotypic variability and community interactions of germinating Streptomyces spores.

A case can be made for stochastic germination and interactions among germinating spores as beneficial germination strategies in uncertain environments. However, there is little data on how widespread, species-specific or diverse such phenomena are. Focusing on Streptomycetes, a platform was developed for quantification of germination and early growth within communities of spores. We found that the germination process is stochastic at three levels: spores vary in their germination times, mycelium networks grow at different rates, and a fraction of germlings stall their growth shortly after germination. Furthermore, by monitoring how these stochastic properties are affected by spore density and chemicals released from spores, germination interactions were quantified for four species. Stochastically germinating spores were frequently promoted or inhibited by compounds released by spores from the same or different species, and all species had distinct interaction profiles. The spatial distribution patterns were important with clusters of spores behaving differently than individual spores. Aged spores exhibited higher dormancy but could efficiently geminate in the presence of chemicals released during germination. All interactions were specific to germination and only weakly affected growth rates. This work suggests that stochastic germination is commonly affected by the community context and species have adapted diverse germination strategies.

Inhibitory interactions promote frequent bistability among competing bacteria.

It is largely unknown how the process of microbial community assembly is affected by the order of species arrival, initial species abundances and interactions between species. A minimal way of capturing competitive abilities in a frequency-dependent manner is with an invasibility network specifying whether a species at low abundance can increase in frequency in an environment dominated by another species. Here, using a panel of prolific small-molecule producers and a habitat with feast-and-famine cycles, we show that the most abundant strain can often exclude other strains--resulting in bistability between pairs of strains. Instead of a single winner, the empirically determined invasibility network is ruled by multiple strains that cannot invade each other, and does not contain loops of cyclic dominance. Antibiotic inhibition contributes to bistability by helping producers resist invasions while at high abundance and by reducing producers' ability to invade when at low abundance.

Computationally efficient phase-field models with interface kinetics.

We present a phase-field model of solidification which allows efficient computations in the regime when interface kinetic effects dominate over capillary effects. The asymptotic analysis required to relate the parameters in the phase field with those of the original sharp-interface model is straightforward, and the resultant phase-field model can be used for a wide range of material parameters.

Exposing the fitness contribution of duplicated genes.

Duplicate genes from the whole-genome duplication (WGD) in yeast are often dispensable--removing one copy has little or no phenotypic consequence. It is unknown, however, whether such dispensability reflects insignificance of the ancestral function or compensation from paralogs. Here, using precise competition-based measurements of the fitness cost of single and double deletions, we estimate the exposed fitness contribution of WGD duplicate genes in metabolism and bound the importance of their ancestral pre-duplication function. We find that the functional overlap between paralogs sufficiently explains the apparent dispensability of individual WGD genes. Furthermore, the lower bound on the fitness value of the ancestral function, which is estimated by the degree of synergistic epistasis, is at least as large as the average fitness cost of deleting single non-WGD genes. These results suggest that most metabolic functions encoded by WGD genes are important today and were also important at the time of duplication.

Evolution and multilevel optimization of the genetic code.

The discovery of the genetic code was one of the most important advances of modern biology. But there is more to a DNA code than protein sequence; DNA carries signals for splicing, localization, folding, and regulation that are often embedded within the protein-coding sequence. In this issue, Itzkovitz and Alon show that the specific 64-to-20 mapping found in the genetic code may have been optimized for permitting protein-coding regions to carry this extra information and suggest that this property may have evolved as a side benefit of selection to minimize the negative effects of frameshift errors.

Collective evolution and the genetic code.

A dynamical theory for the evolution of the genetic code is presented, which accounts for its universality and optimality. The central concept is that a variety of collective, but non-Darwinian, mechanisms likely to be present in early communal life generically lead to refinement and selection of innovation-sharing protocols, such as the genetic code. Our proposal is illustrated by using a simplified computer model and placed within the context of a sequence of transitions that early life may have made, before the emergence of vertical descent.

Global divergence of microbial genome sequences mediated by propagating fronts.

We model the competition between homologous recombination and point mutation in microbial genomes, and present evidence for two distinct phases, one uniform, the other genetically diverse. Depending on the specifics of homologous recombination, we find that global sequence divergence can be mediated by fronts propagating along the genome, whose characteristic signature on genome structure is elucidated, and apparently observed in closely related Bacillus strains. Front propagation provides an emergent, generic mechanism for microbial "speciation," and suggests a classification of microorganisms on the basis of their propensity to support propagating fronts.