Sounds emitted by plants under stress are airborne and informative.

Stressed plants show altered phenotypes, including changes in color, smell, and shape. Yet, airborne sounds emitted by stressed plants have not been investigated before. Here we show that stressed plants emit airborne sounds that can be recorded from a distance and classified. We recorded ultrasonic sounds emitted by tomato and tobacco plants inside an acoustic chamber, and in a greenhouse, while monitoring the plant's physiological parameters. We developed machine learning models that succeeded in identifying the condition of the plants, including dehydration level and injury, based solely on the emitted sounds. These informative sounds may also be detectable by other organisms. This work opens avenues for understanding plants and their interactions with the environment and may have significant impact on agriculture.

Gamblers: An Antibiotic-Induced Evolvable Cell Subpopulation Differentiated by Reactive-Oxygen-Induced General Stress Response.

Antibiotics can induce mutations that cause antibiotic resistance. Yet, despite their importance, mechanisms of antibiotic-promoted mutagenesis remain elusive. We report that the fluoroquinolone antibiotic ciprofloxacin (cipro) induces mutations by triggering transient differentiation of a mutant-generating cell subpopulation, using reactive oxygen species (ROS). Cipro-induced DNA breaks activate the Escherichia coli SOS DNA-damage response and error-prone DNA polymerases in all cells. However, mutagenesis is limited to a cell subpopulation in which electron transfer together with SOS induce ROS, which activate the sigma-S (σS) general-stress response, which allows mutagenic DNA-break repair. When sorted, this small σS-response-"on" subpopulation produces most antibiotic cross-resistant mutants. A U.S. Food and Drug Administration (FDA)-approved drug prevents σS induction, specifically inhibiting antibiotic-promoted mutagenesis. Further, SOS-inhibited cell division, which causes multi-chromosome cells, promotes mutagenesis. The data support a model in which within-cell chromosome cooperation together with development of a "gambler" cell subpopulation promote resistance evolution without risking most cells.

Microbiome-related aspects of locust density-dependent phase transition.

Locust plagues are a notorious, ancient phenomenon. These swarming pests tend to aggregate and perform long migrations, decimating cultivated fields along their path. When population density is low, however, the locusts will express a cryptic, solitary, non-aggregating phenotype that is not considered a pest. Although the transition from the solitary to the gregarious phase has been well studied, associated shifts in the locust's microbiome have yet to be addressed. Here, using 16S rRNA amplicon sequencing, we compared the bacterial composition of solitary desert locusts before and after a phase transition. Our findings revealed that the microbiome is altered during the phase transition, and that a major aspect of this change is the acquisition of Weissella (Firmicutes). Our findings led us to hypothesize that the locust microbiome plays a role in inducing aggregation behaviour, contributing to the formation and maintenance of a swarm. Employing a mathematical model, we demonstrate the potential evolutionary advantage of inducing aggregation under different conditions; specifically, when the aggregation-inducing microbe exhibits a relatively high horizontal transmission rate. This is the first report of a previously unknown and important aspect of locust phase transition, demonstrating that the phase shift includes a shift in the gut and integument bacterial composition.

Predicting microbial growth in a mixed culture from growth curve data.

Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current "gold standard" for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments with Escherichia coli and demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.

Predicting Antibiotic Resistance in Hospitalized Patients by Applying Machine Learning to Electronic Medical Records.

BACKGROUND: Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning (ML) algorithms. However, they are scarcely used for empiric antibiotic therapy. Here, we predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using ML algorithms applied to patients' electronic medical records (EMRs). METHODS: The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their EMRs. Five antibiotics were examined: ceftazidime (n = 2942), gentamicin (n = 4360), imipenem (n = 2235), ofloxacin (n = 3117), and sulfamethoxazole-trimethoprim (n = 3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble that combined all 3 algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis. RESULTS: The ensemble outperformed the separate models and produced accurate predictions on test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble yielded area under the receiver-operating characteristic (auROC) scores of 0.73-0.79 for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. Variables' effects on predictions were assessed and found to be consistent with previously identified risk factors for antibiotic resistance. CONCLUSIONS: We demonstrate the potential of ML to predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapidly gained information regarding the infecting bacterial species can improve predictions substantially. Clinicians should consider the implementation of such systems to aid correct empiric therapy and to potentially reduce antibiotic misuse.

Plants' ability to sense and respond to airborne sound is likely to be adaptive: reply to comment by Pyke et al.

Ecol. Lett. 22, 2019, 1483 demonstrated, for the first time, a rapid response of a plant to the airborne sounds of pollinators. Pyke et al. argue that this response is unlikely to be adaptive. Here we clarify some misunderstandings, and demonstrate the potential adaptive value using theoretical modelling and field observations.

Increased sugar concentration in response to a wide range of pollinator sounds can be adaptive for the plant: answer to Raguso et al.

In Veits et al., we showed that flowers respond to a range of pollinator sounds by increased nectar sugar concentration. Here we clarify that (1) our argument is relevant to most pollinators, and not limited to bees (2) specifically, bees do access Oenothera Drumondii nectar in this area.

Host-microbiome coevolution can promote cooperation in a rock-paper-scissors dynamics.

Cooperation is a fundamental behaviour observed in all forms of life. The evolution of cooperation has been widely studied, but almost all theories focused on the cooperating individual and its genes. We suggest a different approach, taking into account the microbes carried by the interacting individuals. Accumulating evidence reveals that microbes can affect their host's well-being and behaviour, yet hosts can evolve mechanisms to resist the manipulations of their microbes. We thus propose that coevolution of microbes with their hosts may favour microbes that induce their host to cooperate. Using computational modelling, we show that microbe-induced cooperation can evolve and be maintained in a wide range of conditions, including when facing hosts' resistance to the microbial effect. We find that host-microbe coevolution leads the population to a rock-paper-scissors dynamics that enables maintenance of cooperation in a polymorphic state. Our results suggest a mechanism for the evolution and maintenance of cooperation that may be relevant to a wide variety of organisms, including cases that are difficult to explain by current theories. This study provides a new perspective on the coevolution of hosts and their microbiome, emphasizing the potential role of microbes in shaping their host's behaviour.

Flowers respond to pollinator sound within minutes by increasing nectar sugar concentration.

Can plants sense natural airborne sounds and respond to them rapidly? We show that Oenothera drummondii flowers, exposed to playback sound of a flying bee or to synthetic sound signals at similar frequencies, produce sweeter nectar within 3 min, potentially increasing the chances of cross pollination. We found that the flowers vibrated mechanically in response to these sounds, suggesting a plausible mechanism where the flower serves as an auditory sensory organ. Both the vibration and the nectar response were frequency-specific: the flowers responded and vibrated to pollinator sounds, but not to higher frequency sound. Our results document for the first time that plants can rapidly respond to pollinator sounds in an ecologically relevant way. Potential implications include plant resource allocation, the evolution of flower shape and the evolution of pollinators sound. Finally, our results suggest that plants may be affected by other sounds as well, including anthropogenic ones.

Evolution of Stress-Induced Mutagenesis in the Presence of Horizontal Gene Transfer.

Stress-induced mutagenesis has been observed in multiple species of bacteria and yeast. It has been suggested that in asexual populations, a mutator allele that increases the mutation rate during stress can sweep to fixation with the beneficial mutations it generates. However, even asexual microbes can undergo horizontal gene transfer and rare recombination, which typically interfere with the spread of mutator alleles. Here we examine the effect of horizontal gene transfer on the evolutionary advantage of stress-induced mutator alleles. Our results demonstrate that stress-induced mutator alleles are favored by selection even in the presence of horizontal gene transfer and more so when the mutator alleles also increase the rate of horizontal gene transfer. We suggest that when regulated by stress, mutation and horizontal gene transfer can be complementary rather than competing adaptive strategies and that stress-induced mutagenesis has important implications for evolutionary biology, ecology, and epidemiology, even in the presence of horizontal gene transfer and rare recombination.

Some topics in theoretical population genetics: Editorial commentaries on a selection of Marc Feldman's TPB papers.

This article consists of commentaries on a selected group of papers of Marc Feldman published in Theoretical Population Biology from 1970 to the present. The papers describe a diverse set of population-genetic models, covering topics such as cultural evolution, social evolution, and the evolution of recombination. The commentaries highlight Marc Feldman's role in providing mathematically rigorous formulations to explore qualitative hypotheses, in many cases generating surprising conclusions.

Key issues review: evolution on rugged adaptive landscapes.

Adaptive landscapes represent a mapping between genotype and fitness. Rugged adaptive landscapes contain two or more adaptive peaks: allele combinations with higher fitness than any of their neighbors in the genetic space. How do populations evolve on such rugged landscapes? Evolutionary biologists have struggled with this question since it was first introduced in the 1930s by Sewall Wright. Discoveries in the fields of genetics and biochemistry inspired various mathematical models of adaptive landscapes. The development of landscape models led to numerous theoretical studies analyzing evolution on rugged landscapes under different biological conditions. The large body of theoretical work suggests that adaptive landscapes are major determinants of the progress and outcome of evolutionary processes. Recent technological advances in molecular biology and microbiology allow experimenters to measure adaptive values of large sets of allele combinations and construct empirical adaptive landscapes for the first time. Such empirical landscapes have already been generated in bacteria, yeast, viruses, and fungi, and are contributing to new insights about evolution on adaptive landscapes. In this Key Issues Review we will: (i) introduce the concept of adaptive landscapes; (ii) review the major theoretical studies of evolution on rugged landscapes; (iii) review some of the recently obtained empirical adaptive landscapes; (iv) discuss recent mathematical and statistical analyses motivated by empirical adaptive landscapes, as well as provide the reader with instructions and source code to implement simulations of evolution on adaptive landscapes; and (v) discuss possible future directions for this exciting field.

With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing.

BACKGROUND: Natural selection favors changes that lead to genotypes possessing high fitness. A conflict arises when several mutations are required for adaptation, but each mutation is separately deleterious. The process of a population evolving from a genotype encoding for a local fitness maximum to a higher fitness genotype is termed an adaptive peak shift. RESULTS: Here we suggest cooperative behavior as a factor that can facilitate adaptive peak shifts. We model cooperation in a public goods scenario, wherein each individual contributes resources that are later equally redistributed among all cooperating individuals. We use mathematical modeling and stochastic simulations to study the effect of cooperation on peak shifts in both panmictic and structured populations. Our results show that cooperation can substantially affect the rate of complex adaptation. Furthermore, we show that cooperation increases the population diversity throughout the peak shift process, thus increasing the robustness of the population to sudden environmental changes. CONCLUSIONS: We provide a new explanation to adaptive valley crossing in natural populations and suggest that the long term evolution of a species depends on its social behavior.

Bimodal regulation of ICR1 levels generates self-organizing auxin distribution.

Auxin polar transport, local maxima, and gradients have become an important model system for studying self-organization. Auxin distribution is regulated by auxin-dependent positive feedback loops that are not well-understood at the molecular level. Previously, we showed the involvement of the RHO of Plants (ROP) effector INTERACTOR of CONSTITUTIVELY active ROP 1 (ICR1) in regulation of auxin transport and that ICR1 levels are posttranscriptionally repressed at the site of maximum auxin accumulation at the root tip. Here, we show that bimodal regulation of ICR1 levels by auxin is essential for regulating formation of auxin local maxima and gradients. ICR1 levels increase concomitant with increase in auxin response in lateral root primordia, cotyledon tips, and provascular tissues. However, in the embryo hypophysis and root meristem, when auxin exceeds critical levels, ICR1 is rapidly destabilized by an SCF(TIR1/AFB) [SKP, Cullin, F-box (transport inhibitor response 1/auxin signaling F-box protein)]-dependent auxin signaling mechanism. Furthermore, ectopic expression of ICR1 in the embryo hypophysis resulted in reduction of auxin accumulation and concomitant root growth arrest. ICR1 disappeared during root regeneration and lateral root initiation concomitantly with the formation of a local auxin maximum in response to external auxin treatments and transiently after gravitropic stimulation. Destabilization of ICR1 was impaired after inhibition of auxin transport and signaling, proteasome function, and protein synthesis. A mathematical model based on these findings shows that an in vivo-like auxin distribution, rootward auxin flux, and shootward reflux can be simulated without assuming preexisting tissue polarity. Our experimental results and mathematical modeling indicate that regulation of auxin distribution is tightly associated with auxin-dependent ICR1 levels.

Antibiotic Restriction Might Facilitate the Emergence of Multi-drug Resistance.

High antibiotic resistance frequencies have become a major public health issue. The decrease in new antibiotics' production, combined with increasing frequencies of multi-drug resistant (MDR) bacteria, cause substantial limitations in treatment options for some bacterial infections. To diminish overall resistance, and especially the occurrence of bacteria that are resistant to all antibiotics, certain drugs are deliberately scarcely used--mainly when other options are exhausted. We use a mathematical model to explore the efficiency of such antibiotic restrictions. We assume two commonly used drugs and one restricted drug. The model is examined for the mixing strategy of antibiotic prescription, in which one of the drugs is randomly assigned to each incoming patient. Data obtained from Rabin medical center, Israel, is used to estimate realistic single and double antibiotic resistance frequencies in incoming patients. We find that broad usage of the hitherto restricted drug can reduce the number of incorrectly treated patients, and reduce the spread of bacteria resistant to both common antibiotics. Such double resistant infections are often eventually treated with the restricted drug, and therefore are prone to become resistant to all three antibiotics. Thus, counterintuitively, a broader usage of a formerly restricted drug can sometimes lead to a decrease in the emergence of bacteria resistant to all drugs. We recommend re-examining restriction of specific drugs, when multiple resistance to the relevant alternative drugs already exists.

Does stress induce (para)sex? Implications for Candida albicans evolution.

Theory predicts that stress is a key factor in explaining the evolutionary role of sex in facultatively sexual organisms, including microorganisms. Organisms capable of reproducing both sexually and asexually are expected to mate more frequently when stressed, and such stress-induced mating is predicted to facilitate adaptation. Here, we propose that stress has an analogous effect on the parasexual cycle in Candida albicans, which involves alternation of generations between diploid and tetraploid cells. The parasexual cycle can generate high levels of diversity, including aneuploidy, yet it apparently occurs only rarely in nature. We review the evidence that stress facilitates four major steps in the parasexual cycle and suggest that parasex occurs much more frequently under stress conditions. This may explain both the evolutionary significance of parasex and its apparent rarity.

The probability of improvement in Fisher's geometric model: a probabilistic approach.

Fisher developed his geometric model to support the micro-mutationalism hypothesis which claims that small mutations are more likely to be beneficial and therefore to contribute to evolution and adaptation. While others have provided a general solution to the model using geometric approaches, we derive an equivalent general solution using a probabilistic approach. Our approach to Fisher's geometric model provides alternative intuition and interpretation of the solution in terms of the model's parameters: for mutation to improve a phenotype, its relative beneficial effect must be larger than the ratio of its total effect and twice the difference between the current phenotype and the optimal one. Our approach provides new insight into this classical model of adaptive evolution.

Stress-induced mutagenesis and complex adaptation.

Because mutations are mostly deleterious, mutation rates should be reduced by natural selection. However, mutations also provide the raw material for adaptation. Therefore, evolutionary theory suggests that the mutation rate must balance between adaptability-the ability to adapt-and adaptedness-the ability to remain adapted. We model an asexual population crossing a fitness valley and analyse the rate of complex adaptation with and without stress-induced mutagenesis (SIM)-the increase of mutation rates in response to stress or maladaptation. We show that SIM increases the rate of complex adaptation without reducing the population mean fitness, thus breaking the evolutionary trade-off between adaptability and adaptedness. Our theoretical results support the hypothesis that SIM promotes adaptation and provide quantitative predictions of the rate of complex adaptation with different mutational strategies.

Resistance profiles of coagulase-negative staphylococci contaminating blood cultures predict pathogen resistance and patient mortality.

OBJECTIVES: Blood culture isolates are the cornerstone of adequate antibiotic treatment. However, many blood cultures are contaminated with bacteria residing on the skin, the most common contaminants being coagulase-negative staphylococci (CoNS). Such contaminated cultures are mostly disregarded. In this retrospective study, we show that contaminated cultures contain diagnostic information. We tested the association between resistance profiles of CoNS contaminants and those of the actual infecting bacteria isolated subsequently from the same patient, as well as their association with short-term mortality. METHODS: We identified all patients in Rabin Medical Center, Israel, with positive blood cultures during 2009-12. Data included patient demographics, hospitalization records, comorbidities, blood culture results and date of death. RESULTS: Our cohort consists of 2518 patients with 5290 blood cultures, where 1124 patients had 1664 blood cultures with CoNS contaminants. High overall CoNS resistance predicted high overall resistance of the subsequent bacterial isolates (P<0.004 and P<0.0006, for Gram-positive and -negative bacteria, respectively). Moreover, the resistance of CoNS contaminants to a specific antibiotic predicted the resistance of the subsequent bacterial isolates to that antibiotic (OR=5.55, 95% CI=3.54-8.66, P<10(-15) and OR=2.47, 95% CI=1.61-3.78, P<3 ×10(-5), for Gram-positive and -negative bacteria, respectively). Finally, highly resistant CoNS isolates were associated with higher short-term mortality (hazard ratio=1.71, 95% CI=1.4-2.11, P<10(-6)). CONCLUSIONS: Resistance patterns of CoNS contaminants predict specific and overall resistance of subsequent blood culture isolates and short-term mortality. These results may help predict patient mortality and correct empirical antibiotic therapy if blood cultures yield contaminant bacteria and imply that skin commensals may serve as an additional, non-invasive, diagnostic tool.