Ohad Lewin-Epstein

Microbial genomes continuously adapt to environmental conditions, but identifying universal signatures of adaptation remains challenging. Here we show that environmental temperature can be accurately predicted across ecosystems from DNA composition alone (R² = 0.75), using tetranucleotide frequencies from 1,235 marine and soil metagenomes and a machine learning approach. This predictive signal was also apparent within individual taxa, consistent with a fundamental temperature-associated signature. By contrast, GC content exhibited opposite correlations with temperature in soil (positive) and marine (negative) environments. This phenomenon was probably driven by differences in nutrient availability, as GC content increases with nutrients while nutrients decrease with temperature in marine samples. By integrating these observations, we identified specific tetranucleotides, with 50% GC, that displayed consistent and robust temperature correlations across environments and may have contributed to the stability of predictions. This work highlights metagenome-wide DNA–temperature associations, relevant for understanding microbial community responses to global changes.

Microorganisms adapt to their environment through changes in both their genes and overall genome composition. However, identifying universal principles of genomic adaptation remains challenging because different environments present multiple, overlapping selective pressures. To overcome this challenge, we analyzed DNA composition patterns across diverse environments using machine learning. By examining tetranucleotide frequencies from 1,112 marine and soil metagenomic samples, we discovered that environmental temperature can be accurately predicted from DNA composition alone (R ² =0.82). This temperature signal remained robust even when analyzing individual bacterial phyla and classes, suggesting a fundamental adaptive response. To examine this adaptation mechanism, we analyzed GC content relationships with temperature and found opposing relationships in different environments: positive correlations in soil but negative correlations in marine samples. We show that this inverse relationship in marine environments is driven by nutrient availability, as GC content increases with nutrient levels while nutrients decrease with temperature in marine ecosystems. Despite these contrasting GC patterns, we identified specific tetranucleotides composed of equal numbers of GC and AT bases (50% GC content) that showed consistent temperature correlations across all environments. These findings reveal a complex interplay between temperature adaptation and nutrient limitation in shaping microbial genomes.

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.

Mechanisms of host–microbe interactions and their direct impact on both parties have been extensively researched, however, much less is known on the effect of these interactions on the ecology of the host-community. Here we investigate tree-fungi mycorrhizal interactions, focusing on mycorrhizal-mediated resource sharing among trees, while examining the dynamics between specialist and generalist fungi and their implications on the forest ecology. Using genetic meta-barcoding, we identified the fungal community colonizing different trees in a mixed forest, and generated an extensive mapping connecting fungal sequences to their tree hosts. The mycorrhizal fungal community diverged between ectomycorrhizal and arbuscular host trees, but, unexpectedly, multiple ectomycorrhizal species colonized roots of non-ectomycorrhizal host trees. We complemented these findings by a novel computational framework, modeling competition between generalist and specialist mycorrhizal fungi, accounting for fungal-mediated resource sharing among neighboring trees. The analysis of the model revealed that generalist mycorrhizal networks may affect the entire tree community, and contribute to the maintenance of forest diversity in the long run. Furthermore, higher initial spatial mixing of trees can promote the evolution of generalist mycorrhizal species. These novel belowground interactions among trees and fungi may significantly impact forest biodiversity.

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 constructed 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 positive feedback loop augments post-perturbation imbalances, hindering attempts to return to the initial equilibrium, thus promoting relapse episodes and prolonged addictions. We also find 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 reach dysbiosis, exacerbating addictions. This framework provides novel 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.

Summary: 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.

Cultural evolution of cooperation under vertical and non-vertical cultural transmission is studied, and conditions are found for fixation and coexistence of cooperation and defection. The evolution of cooperation is facilitated by its horizontal transmission and by an association between social interactions and horizontal transmission. The effect of oblique transmission depends on the horizontal transmission bias. Stable polymorphism of cooperation and defection can occur, and when it does, reduced association between social interactions and horizontal transmission evolves, which leads to a decreased frequency of cooperation and lower population mean fitness. The deterministic conditions are compared to outcomes of stochastic simulations of structured populations. Parallels are drawn with Hamilton’s rule incorporating relatedness and assortment.

Cultural evolution of cooperation under vertical and non-vertical cultural transmission is studied, and conditions are found for fixation and coexistence of cooperation and defection. The evolution of cooperation is facilitated by its horizontal transmission and by an association between social interactions and horizontal transmission. The effect of oblique transmission depends on the horizontal transmission bias. Stable polymorphism of cooperation and defection can occur, and when it does, reduced association between social interactions and horizontal transmission evolves, which leads to a decreased frequency of cooperation and lower population mean fitness. The deterministic conditions are compared to outcomes of stochastic simulations of structured populations. Parallels are drawn with Hamilton’s rule incorporating assortment and effective relatedness.

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.

Locust plagues are an ancient phenomenon, with references going back to the Old Testament. These swarming pests are notorious for their tendency to aggregate and perform long migrations, consuming vast amounts of vegetation and decimating the cultivated fields on their path. However, when population density is low, locusts will express a solitary, cryptic, non-aggregating phenotype that is not considered as an agricultural pest. Although transition of locusts from the solitary to the gregarious phase has been well studied, the shifts in the locust microbiome composition associated with this phase-transition have yet to be addressed. Here, using 16S rRNA amplicon sequencing, we compared the bacterial composition of solitary desert locusts before and after a crowding-induced phase-transition. Our findings reveal that the microbiome is altered during the phase transition. We also show that this significant change in bacterial composition includes the acquisition of a specific bacterial species - Weissella cibaria (Firmicutes), which has been previously shown to induce aggregation in cockroaches. Our findings led us to hypothesize that the locust microbiome may play a role in inducing aggregation behavior, contributing to the formation and maintenance of a swarm. Employing a mathematical model, we demonstrate the potential evolutionary advantage of inducing aggregation under various environmental conditions; and specifically, when the aggregation-inducing microbe exhibits a relatively high horizontal transmission rate. This is a first description 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.