Explaining the microbial diversity conundrum: can microbiome composition be crucial for host fitness, while highly divergent among healthy individuals?

Recent empirical studies offer conflicting findings regarding the relation between host fitness and the composition of its microbiome, a conflict which we term the microbial β-diversity conundrum: it has been shown that the microbiome is crucial for host wellbeing and survival. At the same time, different healthy individuals' microbiome compositions, even in the same population, often differ dramatically, contrary to the notion that a vital trait should be highly conserved. Moreover, gnotobiotic individuals exhibit highly deleterious phenotypes, supporting the notion that the microbiome is paramount to host fitness. However, the introduction of almost arbitrarily selected microbiota into the system often achieves a significant rescue effect of the deleterious phenotypes, even microbiota from soil or phylogenetically distant host species, highlighting an apparent paradox. Here we suggest several solutions to the paradox using a computational framework, simulating the population dynamics of hosts and their microbiomes over multiple generations. The answers, relating to factors such as host population size, the specific mode of contribution of the microbes to host fitness, and the typical microbiome richness, offer solutions to the conundrum by creating scenarios where even when a host's fitness is determined in full by its microbiome composition, this composition has little or no effect on the natural selection dynamics of the population.

Population size impacts host–pathogen coevolution

Ongoing host–pathogen interactions are characterized by rapid coevolutionary changes forcing species to continuously adapt to each other. The interacting species are often defined by finite population sizes. In theory, finite population size limits genetic diversity and compromises the efficiency of selection owing to genetic drift, in turn constraining any rapid coevolutionary responses. To date, however, experimental evidence for such constraints is scarce. The aim of our study was to assess to what extent population size influences the dynamics of host–pathogen coevolution. We used Caenorhabditus elegans and its pathogen Bacillus thuringiensis as a model for experimental coevolution in small and large host populations, as well as in host populations which were periodically forced through a bottleneck. By carefully controlling host population size for 23 host generations, we found that host adaptation was constrained in small populations and to a lesser extent in the bottlenecked populations. As a result, coevolution in large and small populations gave rise to different selection dynamics and produced different patterns of host–pathogen genotype-by-genotype interactions. Our results demonstrate a major influence of host population size on the ability of the antagonists to co-adapt to each other, thereby shaping the dynamics of antagonistic coevolution.

Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict

The cortico-basal ganglia circuit is needed to suppress prepotent actions and to facilitate controlled behavior. Under conditions of response conflict, the frontal cortex and subthalamic nucleus [STN] exhibit increased spiking and theta band power, which are linked to adaptive regulation of behavioral output. The electrophysiological mechanisms underlying these neural signatures of impulse control remain poorly understood. To address this lacuna, we constructed a novel large-scale, biophysically principled model of the subthalamopallidal [STN-Globus Pallidus externus (GPe)] network, and examined the mechanisms that modulate theta power and spiking in response to cortical input. Simulations confirmed that theta power does not emerge from intrinsic network dynamics but is robustly elicited in response to cortical input as burst events representing action selection dynamics. Rhythmic burst events of multiple cortical populations, representing a state of conflict where cortical motor plans vacillate in the theta range, led to prolonged STN theta and increased spiking, consistent with empirical literature. Notably, theta band signaling required NMDA, but not AMPA, currents, which were in turn related to a triphasic STN response characterized by spiking, silence and bursting periods. Finally, theta band resonance was also strongly modulated by architectural connectivity, with maximal theta arising when multiple cortical populations project to individual STN "conflict detector" units, due to an NMDA-dependent supralinear response. Our results provide insights into the biophysical principles and architectural constraints that give rise to STN dynamics during response conflict, and how their disruption can lead to impulsivity and compulsivity.

Evolutionary Dynamics, Evolutionary Forces, and Robustness: A Nonequilibrium Statistical Mechanics Perspective

Any realistic evolutionary theory has to consider: (i) the dynamics of organisms that reproduce and possess heritable traits; (ii) the appearance of stochastic variations in these traits; and (iii) the selection of those organisms that better survive and reproduce. These elements shape the “evolutionary forces” that characterize the evolutionary dynamics. Here, we introduce a general model of reproduction–variation–selection dynamics. By treating these dynamics as a non-equilibrium thermodynamic process, we make precise the notion of the forces that characterize evolution. One of these forces, in particular, can be associated with the robustness of reproduction to variations. The emergence of this trait in our model—without any explicit selection for it—is an example of a general phenomenon, which can be called enaptation, distinct from the well-known and studied phenomena of adaptation and exaptation. Some of the detailed predictions of our model can be tested by quantitative laboratory experiments, similar to those performed in the past on evolving populations of proteins or viruses.

Dynamics of gut mucosal colonisation with extended spectrum beta-lactamase producing Enterobacterales in Malawi

Shortening courses of antimicrobials has been proposed to reduce risk of antimicrobial resistant (AMR) infections, but acquisition and selection dynamics under antimicrobial pressure at the individual level are poorly understood. We combine multi-state modelling and whole-genome sequencing to understand colonisation dynamics of extended-spectrum beta-lactamase producing Enterobacterales (ESBL-E) in Malawian adults. We demonstrate prolonged post-exposure antibiotic effect, meaning short courses exert similar colonisation pressure to longer ones. Genome data does not identify widespread hospital-associated ESBL-E transmission, hence apparent acquisitions may be selected from the patient microbiota by antimicrobial exposure. Understanding ESBL-E dynamics under antimicrobial pressure is crucial for evidence-based stewardship protocols.

Why we should monitor disparities in old-age mortality with the modal age at death

BackgroundIndicators based a fixed ``old’’ age threshold have been widely used for assessing socioeconomic disparities in mortality at older ages. Interpretation of long-term trends and determinants of these indicators is challenging because mortality above a fixed age that in the past would have reflected old age deaths is today mixing premature and old-age mortality. We propose the modal (i.e. most frequent) age at death, $M$, an indicator increasingly recognized in aging research, but which has been infrequently used for monitoring mortality disparities at older ages. MethodsWe use mortality and population exposure data by occupational class over the 1971-2017 period from Finnish register data. The modal age and life expectancy indicators are estimated from mortality rates smoothed with penalized B-splines.ResultsOver the 1971-2017 period, occupational class disparities in life expectancy at 65 and 75 widened while disparities in $M$ remained relatively stable. The proportion of the group surviving to the modal age was constant across time and occupational class. In contrast, the proportion surviving to age 65 and 75 has roughly doubled since 1971 and showed strong occupational class differences. ConclusionsIncreasing socioeconomic disparities in mortality based on fixed old age thresholds may be a feature of changing selection dynamics in a context of overall declining mortality. Unlike life expectancy at a selected fixed old age, $M$ compares individuals with similar survival chances over time and across occupational classes. This property makes trends and differentials in $M$ easier to interpret in countries where old-age survival has improved significantly.

Foraging for the Self: Environment selection for agency inference

Sometimes agents choose to occupy environments that are neither traditionally rewarding or worth exploring, but which rather promise to help minimise uncertainty related to what they can control. Selecting environments that afford inferences about agency seems a foundational aspect of environment selection dynamics – if an agent can’t form reliable beliefs about what they can and can’t control, then they can’t act efficiently to achieve rewards. This relatively neglected aspect of environment selection is important to study so that we can better understand why agents occupy certain environments over others; something may also be relevant for mental and developmental conditions, such as autism. This online experiment investigates the impact of uncertainty about agency on the way participants choose to freely move between two environments, one that has greater irreducible variability and one that is more complex to model. We hypothesise that increasingly erroneous predictions about the expected outcome of agency-exploring actions can be a driver of switching environments; and we explore which type of environment agents prefer. Results show that participants actively switch between the two environments following increases in prediction error, and that the tolerance for prediction error before switching is modulated by individuals’ autism traits. Further, we find that participants more frequently occupy the variable environment, which is predicted by greater accuracy and higher confidence than the complex environment. This demonstrates how participants forage to reduce uncertainty of action-outcome contingencies and support agency inferences.

Building reusable phage and antibiotic treatments via exploitation of bacteria-phage coevolutionary dynamics.

People with chronic (long-lasting) infections face the problem that treatment options diminish in time as the pathogen evolves increasing resistance. To address this challenge, we exploit phage and bacterial co-evolution, producing dynamic selection pressures that can return the pathogen to a state of susceptibility to the initial (regulator-approved) therapy. We show that phage OMKO1 alone triggers Arms Race Dynamic (ARD) co-evolution with the pathogen Pseudomonas aeruginosa, leading to generalized phage resistance and crucially - failure at reuse. In contrast, co-administration of the phage with antibiotics triggers Fluctuating Selection Dynamics (FSD) co-evolution, allowing for effective reuse after 20 days of treatment. We pursue medical relevance in our experiments with the use of clinically important pathogens, antibiotics, phage, and a benchmarked synthetic sputum medium. Phenotypic and genomic characterization of evolved isolates demonstrates that efflux-targeting phage OMKO1 exerts continued selection for antibiotic susceptibility regardless of co-evolutionary dynamic or antibiotic co-treatment, opening the door for evolutionary robust phage therapy.

Different selection dynamics of S and RdRp between SARS-CoV-2 genomes with and without the dominant mutations

SARS-CoV-2 is a betacoronavirus responsible for the COVID-19 pandemic that has affected millions of people worldwide, with no dedicated treatment or vaccine currently available. As pharmaceutical research against and the most frequently used tests for SARS-CoV-2 infection both depend on the genomic and peptide sequences of the virus for their efficacy, understanding the mutation rates and content of the virus is critical. Two key proteins for SARS-CoV-2 infection and replication are the S protein, responsible for viral entry into the cells, and RdRp, the RNA polymerase responsible for replicating the viral genome. Due to their roles in the viral cycle, these proteins are crucial for the fitness and infectiousness of the virus. Our previous findings had shown that the two most frequently observed mutations in the SARS-CoV-2 genome, 14408C>T in the RdRp coding region, and 23403A>G in the S gene, are correlated with higher mutation density over time. In this study, we further detail the selection dynamics and the mutation rates of SARS-CoV-2 genes, comparing them between isolates carrying both mutations, and isolates carrying neither. We find that the S gene and the RdRp coding region show the highest variance between the genotypes, and their selection dynamics contrast each other over time. The S gene displays higher positive selection in mutant isolates early on, and undergoes increasing negative selection over time, whereas the RdRp region in the mutant isolates shows strong negative selection throughout the pandemic.