Evolutionary modeling suggests that addictions may be driven by competition-induced microbiome dysbiosis.

Recent studies revealed mechanisms by which the microbiome affects its host's brain, behavior and wellbeing, and that dysbiosis - persistent microbiome-imbalance - is associated with the onset and progress of various chronic diseases, including addictive behaviors. Yet, understanding of the ecological and evolutionary processes that shape the host-microbiome ecosystem and affect the host state, is still limited. Here we propose that competition dynamics within the microbiome, associated with host-microbiome mutual regulation, may promote dysbiosis and aggravate addictive behaviors. We construct a mathematical framework, modeling the dynamics of the host-microbiome ecosystem in response to alterations. We find that when this ecosystem is exposed to substantial perturbations, the microbiome may shift towards a composition that reinforces the new host state. Such a positive feedback loop augments post-perturbation imbalances, hindering attempts to return to the initial equilibrium, promoting relapse episodes and prolonging addictions. We show that the initial microbiome composition is a key factor: a diverse microbiome enhances the ecosystem's resilience, whereas lower microbiome diversity is more prone to lead to dysbiosis, exacerbating addictions. This framework provides evolutionary and ecological perspectives on host-microbiome interactions and their implications for host behavior and health, while offering verifiable predictions with potential relevance to clinical treatments.

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.

Modeling the evolution of SARS-CoV-2 under non-pharmaceutical interventions and testing.

Background and objectives: Social and behavioral non-pharmaceutical interventions (NPIs), such as mask-wearing, social distancing and travel restrictions, as well as diagnostic tests, have been broadly implemented in response to the COVID-19 pandemic. Epidemiological models and data analysis affirm that wide adoption of NPIs helps to control the pandemic. However, SARS-CoV-2 has extensively demonstrated its ability to evolve. Therefore, it is crucial to examine how NPIs may affect the evolution of the virus. Such evolution could have important effects on the spread and impact of the pandemic. Methodology: We used evo-epidemiological models to examine the effect of NPIs and testing on two evolutionary trajectories for SARS-CoV-2: attenuation and test evasion. Results: Our results show that when stronger measures are taken, selection may act to reduce disease severity. Additionally, the timely application of NPIs could significantly affect the competition between viral strains, favoring the milder strain. Furthermore, a higher testing rate can select for a test-evasive viral strain, even if that strain is less infectious than the detectable competing strain. Importantly, if a less detectable strain evolves, epidemiological metrics such as confirmed daily cases may distort our assessment of the pandemic. Conclusions and implications: Our results highlight the important implications NPIs can have on the evolution of SARS-CoV-2. Lay Summary: We used evo-epidemiological models to examine the effect of non-pharmaceutical interventions and testing on two evolutionary trajectories for SARS-CoV-2: attenuation and test evasion. Our results show that when stronger measures are taken, selection may act to reduce disease severity.

Transgenerational inheritance of sexual attractiveness via small RNAs enhances evolvability in C. elegans.

It is unknown whether transient transgenerational epigenetic responses to environmental challenges affect the process of evolution, which typically unfolds over many generations. Here, we show that in C. elegans, inherited small RNAs control genetic variation by regulating the crucial decision of whether to self-fertilize or outcross. We found that under stressful temperatures, younger hermaphrodites secrete a male-attracting pheromone. Attractiveness transmits transgenerationally to unstressed progeny via heritable small RNAs and the Argonaute Heritable RNAi Deficient-1 (HRDE-1). We identified an endogenous small interfering RNA pathway, enriched in endo-siRNAs that target sperm genes, that transgenerationally regulates sexual attraction, male prevalence, and outcrossing rates. Multigenerational mating competition experiments and mathematical simulations revealed that over generations, animals that inherit attractiveness mate more and their alleles spread in the population. We propose that the sperm serves as a "stress-sensor" that, via small RNA inheritance, promotes outcrossing in challenging environments when increasing genetic variation is advantageous.

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.

Annual climatic fluctuations and short-term genetic variation in the eastern spadefoot toad.

In addition to variations on the spatial scale, short- and long-term temporal variations, too, can impose intense selection on the overall genetic diversity and composition of a population. We hypothesized that the allelic composition in populations of the eastern spadefoot toad (Pelobates syriacus) would change among successive years in accordance with the short-term changes in environmental conditions. Surprisingly, the effect of short-term climate fluctuations on genetic composition have rarely been addressed in the literature, and to our knowledge the effect of annual climatic fluctuations have not been considered meaningful. Our findings show that climatic variation among successive years, primarily the amount of rainfall and rainy days, can significantly alter both microsatellite allelic composition and diversity. We suggest that environmental (i.e. fluctuating) selection is differential across the globe, and that its intensity is expected to be greatest in regions where short-term climatic conditions are least stable.

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.

The evolution of paternal care: a role for microbes?

Paternal care, particularly in cases of uncertain paternity, carries significant costs. Extensive research, both theoretical and experimental, has explored the conditions in which paternal care behaviour would be favoured. Common explanations include an adjustment of care with uncertainty in paternity and limited accuracy in parentage assessment. Here, we propose a new explanation that microbes may play a role in the evolution of paternal care among their hosts. Using computational models, we demonstrate that microbes associated with increased paternal care could be favoured by natural selection. We find that microbe-induced paternal care could evolve under wider conditions than suggested by genetic models. Moreover, we show that microbe-induced paternal care is more likely to evolve when considering paternal care interactions that increase microbial transmission, such as feeding and grooming. Our results imply that factors affecting the composition of host microbiome may also alter paternal behaviour. This article is part of the theme issue 'The role of the microbiome in host evolution'.

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.

Random search with resetting as a strategy for optimal pollination.

The problem of pollination is unique among a wide scope of search problems, since it requires optimization of benefits for both the searcher (pollinator) and its targets (plants). To address this challenge, we propose a pollination model which is based on a framework of first passage under stochastic restart. We derive equations for the search time and number of visited plants as functions of the distribution of nectar in the plant population and of the probability that a pollinator will leave the plant after examining a flower, thus effectively restarting the search. We demonstrate that nectar variation in plants serves as a driving force for pollination and establish conditions required for optimal pollination, which provides an efficient pollinator search strategy and the maximum number of plants visited by the pollinator.

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.

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.

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.

Less fit Lamium amplexicaule plants produce more dispersible seeds.

Theory predicts that less fit individuals would disperse more often than fitter ones (Fitness Associated Dispersal, FAD hypothesis). To test this prediction under laboratory conditions, an entire life cycle of Lamium amplexicaule plants and the preferences of its dispersal agent, Messor ebeninus ants, were tracked. Characterization of individual L. amplexicaule plant revealed high variability in spot cover on the surface of the seeds, where less fit plants produce "unspotted seeds" (see Fig. 1 in Introduction). Unspotted L. amplexicaule seeds showed higher variation in germination time and lower germination rate. Moreover, M. ebeninus ants preferably collected these unspotted seeds. Our results show that low fitness L. amplexicaule plants produce seeds with higher potential for dispersal, supporting the FAD hypothesis in a plant-animal system.

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.

Spontaneous Changes in Ploidy Are Common in Yeast.

Changes in ploidy are relatively rare, but play important roles in the development of cancer and the acquisition of long-term adaptations. Genome duplications occur across the tree of life, and can alter the rate of adaptive evolution. Moreover, by allowing the subsequent loss of individual chromosomes and the accumulation of mutations, changes in ploidy can promote genomic instability and/or adaptation. Although many studies have been published in the last years about changes in chromosome number and their evolutionary consequences, tracking and measuring the rate of whole-genome duplications have been extremely challenging. We have systematically studied the appearance of diploid cells among haploid yeast cultures evolving for over 100 generations in different media. We find that spontaneous diploidization is a relatively common event, which is usually selected against, but under certain stressful conditions may become advantageous. Furthermore, we were able to detect and distinguish between two different mechanisms of diploidization, one that requires whole-genome duplication (endoreduplication) and a second that involves mating-type switching despite the use of heterothallic strains. Our results have important implications for our understanding of evolution and adaptation in fungal pathogens and the development of cancer, and for the use of yeast cells in biotechnological applications.