T Cell Fate at the Single-Cell Level.

T cell responses display two key characteristics. First, a small population of epitope-specific naive T cells expands by several orders of magnitude. Second, the T cells within this proliferating population take on diverse functional and phenotypic properties that determine their ability to exert effector functions and contribute to T cell memory. Recent technological advances in lineage tracing allow us for the first time to study these processes in vivo at single-cell resolution. Here, we summarize resulting data demonstrating that although epitope-specific T cell responses are reproducibly similar at the population level, expansion potential and diversification patterns of the offspring derived from individual T cells are highly variable during both primary and recall immune responses. In spite of this stochastic response variation, individual memory T cells can serve as adult stem cells that provide robust regeneration of an epitope-specific tissue through population averaging. We discuss the relevance of these findings for T cell memory formation and clinical immunotherapy.

An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation.

Transcription is a highly regulated and inherently stochastic process. The complexity of signal transduction and gene regulation makes it challenging to analyze how the dynamic activity of transcriptional regulators affects stochastic transcription. By combining a fast-acting, photo-regulatable transcription factor with nascent RNA quantification in live cells and an experimental setup for precise spatiotemporal delivery of light inputs, we constructed a platform for the real-time, single-cell interrogation of transcription in Saccharomyces cerevisiae. We show that transcriptional activation and deactivation are fast and memoryless. By analyzing the temporal activity of individual cells, we found that transcription occurs in bursts, whose duration and timing are modulated by transcription factor activity. Using our platform, we regulated transcription via light-driven feedback loops at the single-cell level. Feedback markedly reduced cell-to-cell variability and led to qualitative differences in cellular transcriptional dynamics. Our platform establishes a flexible method for studying transcriptional dynamics in single cells.

From genetic mosaicism to tumorigenesis through indirect genetic effects.

Genetic mosaicism has long been linked to aging, and several hypotheses have been proposed to explain the potential connections between mosaicism and susceptibility to cancer. It has been proposed that mosaicism may disrupt tissue homeostasis by affecting intercellular communications and releasing microenvironmental constraints within tissues. The underlying mechanisms driving these tissue-level influences remain unidentified, however. Here, we present an evolutionary perspective on the interplay between mosaicism and cancer, suggesting that the tissue-level impacts of genetic mosaicism can be attributed to Indirect Genetic Effects (IGEs). IGEs can increase the level of cellular stochasticity and phenotypic instability among adjacent cells, thereby elevating the risk of cancer development within the tissue. Moreover, as cells experience phenotypic changes in response to challenging microenvironmental conditions, these changes can initiate a cascade of nongenetic alterations, referred to as Indirect non-Genetic Effects (InGEs), which in turn catalyze IGEs among surrounding cells. We argue that incorporating both InGEs and IGEs into our understanding of the process of oncogenic transformation could trigger a major paradigm shift in cancer research with far-reaching implications for practical applications.

Reproductive variance can drive behavioral dynamics.

The concept of fitness is central to evolution, but it quantifies only the expected number of offspring an individual will produce. The actual number of offspring is also subject to demographic stochasticity-that is, randomness associated with birth and death processes. In nature, individuals who are more fecund tend to have greater variance in their offspring number. Here, we develop a model for the evolution of two types competing in a population of nonconstant size. The fitness of each type is determined by pairwise interactions in a prisoner's dilemma game, and the variance in offspring number depends upon its mean. Although defectors are preferred by natural selection in classical population models, since they always have greater fitness than cooperators, we show that sufficiently large offspring variance can reverse the direction of evolution and favor cooperation. Large offspring variance produces qualitatively new dynamics for other types of social interactions, as well, which cannot arise in populations with a fixed size or with a Poisson offspring distribution.

The adaptive stochasticity hypothesis: Modeling equifinality, multifinality, and adaptation to adversity.

Neural phenotypes are the result of probabilistic developmental processes. This means that stochasticity is an intrinsic aspect of the brain as it self-organizes over a protracted period. In other words, while both genomic and environmental factors shape the developing nervous system, another significant-though often neglected-contributor is the randomness introduced by probability distributions. Using generative modeling of brain networks, we provide a framework for probing the contribution of stochasticity to neurodevelopmental diversity. To mimic the prenatal scaffold of brain structure set by activity-independent mechanisms, we start our simulations from the medio-posterior neonatal rich club (Developing Human Connectome Project, n = 630). From this initial starting point, models implementing Hebbian-like wiring processes generate variable yet consistently plausible brain network topologies. By analyzing repeated runs of the generative process (>107 simulations), we identify critical determinants and effects of stochasticity. Namely, we find that stochastic variation has a greater impact on brain organization when networks develop under weaker constraints. This heightened stochasticity makes brain networks more robust to random and targeted attacks, but more often results in non-normative phenotypic outcomes. To test our framework empirically, we evaluated whether stochasticity varies according to the experience of early-life deprivation using a cohort of neurodiverse children (Centre for Attention, Learning and Memory; n = 357). We show that low-socioeconomic status predicts more stochastic brain wiring. We conclude that stochasticity may be an unappreciated contributor to relevant developmental outcomes and make specific predictions for future research.

Seasonality, density dependence, and spatial population synchrony.

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of season-specific density dependence in shaping large-scale population synchrony has not received attention. Here, we present a widely applicable analytical protocol that allows us to account for both season and geographic context-specific density dependence to better elucidate the relative roles of deterministic and stochastic sources of population synchrony, including the renowned Moran effect. We exemplify our protocol by analyzing time series of seasonal (spring and fall) abundance estimates of cyclic rodent populations, revealing that season-specific density dependence is a major component of population synchrony. By accounting for deterministic sources of synchrony (in particular season-specific density dependence), we are able to identify stochastic components. These stochastic components include mild winter weather events, which are expected to increase in frequency under climate warming in boreal and Arctic ecosystems. Interestingly, these weather effects act both directly and delayed on the vole populations, thus enhancing the Moran effect. Our study demonstrates how different drivers of population synchrony, presently altered by climate warming, can be disentangled based on seasonally sampled population time-series data and adequate population models.

Tuning the Intrinsic Stochasticity of Resistive Switching in VO2 Microresistors.

Vanadium dioxide (VO2) microresistors exhibit resistive switching above a certain threshold voltage, allowing them to emulate neurons in neuromorphic systems. However, such devices present intrinsic cycle-to-cycle variations in their resistances and threshold voltages, which can be detrimental or beneficial, depending on their use. Here, we study this stochasticity in VO2 microresistors with various grain sizes and dimensions, through high-resolution electrical and optical measurements across numerous cycles. Our results highlight that the cycle-to-cycle variations in threshold voltage increase as the grain size becomes comparable to the device dimensions. We also present observations of multimodal threshold voltage distributions in the smaller-length resistors. To understand the underlying phenomenon, we investigate the relationship between the device insulating resistance and threshold voltage distributions, showing that these modes could correspond to distinct percolation paths and filaments. Our findings provide the first experimentally verified guidelines for designing VO2 devices with minimized/maximized stochasticity, depending on the targeted application.

The role of plasticity and stochasticity in coexistence.

Species coexistence in ecological communities is a central feature of biodiversity. Different concepts, i.e., contemporary niche theory, modern coexistence theory, and the unified neutral theory, have identified many building blocks of such ecological assemblies. However, other factors, such as phenotypic plasticity and stochastic inter-individual variation, have received little attention, in particular in animals. For example, how resource polyphenisms resulting in predator-prey interactions affect coexistence is currently unknown. Here, we present an integrative theoretical-experimental framework using the nematode plasticity model Pristionchus pacificus with its well-studied mouth-form dimorphism resulting in cannibalism. We develop an individual-based model that relies upon synthetic data based on our empirical measurements of fecundity and polyphenism to preserve demographic heterogeneity. We demonstrate how the interplay between plasticity and individual stochasticity result in all-or-nothing outcomes at the local level. Coexistence is made possible when spatial structure is introduced.

The natural history of luck: A synthesis study of structured population models.

Chance pervades life. In turn, life histories are described by probabilities (e.g. survival and breeding) and averages across individuals (e.g. mean growth rate and age at maturity). In this study, we explored patterns of luck in lifetime outcomes by analysing structured population models for a wide array of plant and animal species. We calculated four response variables: variance and skewness in both lifespan and lifetime reproductive output (LRO), and partitioned them into contributions from different forms of luck. We examined relationships among response variables and a variety of life history traits. We found that variance in lifespan and variance in LRO were positively correlated across taxa, but that variance and skewness were negatively correlated for both lifespan and LRO. The most important life history trait was longevity, which shaped variance and skew in LRO through its effects on variance in lifespan. We found that luck in survival, growth, and fecundity all contributed to variance in LRO, but skew in LRO was overwhelmingly due to survival luck. Rapidly growing populations have larger variances in LRO and lifespan than shrinking populations. Our results indicate that luck-induced genetic drift may be most severe in recovering populations of species with long mature lifespan and high iteroparity.

High Juvenile Mortality Overwhelms Benefits of Mating Potential for Reproductive Fitness.

AbstractAn individual's access to mates (i.e., its "mating potential") can constrain its reproduction but may also influence its fitness through effects on offspring survival. For instance, mate proximity may correspond with relatedness and lead to inbreeding depression in offspring. While offspring production and survival might respond differently to mating potential, previous studies have not considered the simultaneous effects of mating potential on these fitness components. We investigated the relationship of mating potential with both production and survival of offspring in populations of a long-lived herbaceous perennial, Echinacea angustifolia. Across 7 years and 14 sites, we quantified the mating potential of maternal plants in 1,278 mating bouts and followed the offspring from these bouts over 8 years. We used aster models to evaluate the relationship of mating potential with the number of offspring that emerged and that were alive after 8 years. Seedling emergence increased with mating potential. Despite this, the number of offspring surviving after 8 years showed no relationship to mating potential. Our results support the broader conclusion that the effect of mating potential on fitness erodes over time because of demographic stochasticity at the maternal level.

To Prosper, Live Long: Understanding the Sources of Reproductive Skew and Extreme Reproductive Success in Structured Populations.

AbstractIn many species, a few individuals produce most of the next generation. How much of this reproductive skew is driven by variation among individuals in fixed traits, how much by external factors, and how much by random chance? And what does it take to have truly exceptional lifetime reproductive output (LRO)? In the past, we and others have partitioned the variance of LRO as a proxy for reproductive skew. Here we explain how to partition LRO skewness itself into contributions from fixed trait variation, four forms of "demographic luck" (birth state, fecundity luck, survival trajectory luck, and growth trajectory luck), and two kinds of "environmental luck" (birth environment and environment trajectory). Each of these is further partitioned into contributions at different ages. We also determine what we can infer about individuals with exceptional LRO. We find that reproductive skew is largely driven by random variation in lifespan, and exceptional LRO generally results from exceptional lifespan. Other kinds of luck frequently bring skewness down rather than increasing it. In populations where fecundity varies greatly with environmental conditions, getting a good year at the right time can be an alternate route to exceptional LRO, so that LRO is less predictive of lifespan.

Building pyramids against the evolutionary emergence of pathogens.

Mutations allowing pathogens to escape host immunity promote the spread of infectious diseases in heterogeneous host populations and can lead to major epidemics. Understanding the conditions that slow down this evolution is key for the development of durable control strategies against pathogens. Here, we use theory and experiments to compare the efficacy of three strategies for the deployment of resistance: (i) a mixing strategy where the host population contains two single-resistant genotypes, (ii) a pyramiding strategy where the host carries a double-resistant genotype, (iii) a combining strategy where the host population is a mix of a single-resistant genotype and a double-resistant genotype. First, we use evolutionary epidemiology theory to clarify the interplay between demographic stochasticity and evolutionary dynamics to show that the pyramiding strategy always yields lower probability of evolutionary emergence. Second, we test experimentally these predictions with the introduction of bacteriophages into bacterial populations where we manipulated the diversity and the depth of immunity using a Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR associated (CRISPR-Cas) system. These biological assays confirm that pyramiding multiple defences into the same host genotype and avoiding combination with single-defence genotypes is a robust way to reduce pathogen evolutionary emergence. The experimental validation of these theoretical recommendations has practical implications in various areas, including for the optimal deployment of resistance varieties in agriculture and for the design of durable vaccination strategies.

Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures.

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression.

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Why so many polyploids? Accounting for environmental stochasticity in unreduced gamete formation lowers the perceived barriers to polyploid establishment.

While polyploids are common in nature, existing models suggest that polyploid establishment should be difficult and rare. We explore this apparent paradox by focussing on the role of unreduced gametes, as their union is the main route for the formation of neopolyploids. Production of such gametes is affected by genetic and environmental factors, resulting in variation in the formation rate of unreduced gametes (u). Once formed, neopolyploids face minority cytotype exclusion (MCE) due to a lack of viable mating opportunities. More than a dozen theoretical models have explored factors that could permit neopolyploids to overcome MCE and become established. Until now, however, none have explored variability in u and its consequences for the rate of polyploid establishment. Here, we determine the distribution that best fits the available empirical data on u. We perform a global sensitivity analysis exploring the consequences of using empirical distributions of u to investigate effects on polyploid establishment. We determined that in many cases, u is best fit by a log-normal distribution. We found environmental stochasticity in u dramatically impacts model predictions when compared to a static u. Our results help reconcile previous modelling results suggesting high barriers to the polyploid establishment with the observation that polyploids are common in nature.

Inferring stochastic group interactions within structured populations via coupled autoregression.

The internal behaviour of a population is an important feature to take account of when modelling its dynamics. In line with kin selection theory, many social species tend to cluster into distinct groups in order to enhance their overall population fitness. Temporal interactions between populations are often modelled using classical mathematical models, but these sometimes fail to delve deeper into the, often uncertain, relationships within populations. Here, we introduce a stochastic framework that aims to capture the interactions of animal groups and an auxiliary population over time. We demonstrate the model's capabilities, from a Bayesian perspective, through simulation studies and by fitting it to predator-prey count time series data. We then derive an approximation to the group correlation structure within such a population, while also taking account of the effect of the auxiliary population. We finally discuss how this approximation can lead to ecologically realistic interpretations in a predator-prey context. This approximation also serves as verification to whether the population in question satisfies our various assumptions. Our modelling approach will be useful for empiricists for monitoring groups within a conservation framework and also theoreticians wanting to quantify interactions, to study cooperation and other phenomena within social populations.

Assessing the sensitivity and resistance of communities in a changing environment.

We propose that the ecological resilience of communities to permanent changes of the environment can be based on how variation in the overall abundance of individuals affects the number of species. Community sensitivity is defined as the ratio between the rate of change in the log expected number of species and the rate of change in the log expected number of individuals in the community. High community sensitivity means that small changes in the total abundance strongly impact the number of species. Community resistance is the proportional reduction in expected number of individuals that the community can sustain before expecting to lose one species. A small value of community resistance means that the community can only endure a small reduction in abundance before it is expected to lose one species. Based on long-term studies of four bird communities in European deciduous forests at different latitudes large differences were found in the resilience to environmental perturbations. Estimating the variance components of the species abundance distribution revealed how different processes contributed to the community sensitivity and resistance. Species heterogeneity in the population dynamics was the largest component, but its proportion varied among communities. Species-specific response to environmental fluctuations was the second major component of the variation in abundance. Estimates of community sensitivity and resistance based on data only from a single year were in general larger than those based on estimates from longer time series. Thus, our approach can provide rapid and conservative assessment of the resilience of communities to environmental changes also including only short-term data. This study shows that a general ecological mechanism, caused by increased strength of density dependence due to reduction in resource availability, can provide an intuitive measure of community resilience to environmental variation. Our analyses also illustrate the importance of including specific assumptions about how different processes affect community dynamics. For example, if stochastic fluctuations in the environment affect all species in a similar way, the sensitivity and resistance of the community to environmental changes will be different from communities in which all species show independent responses.

Are plant traits drivers of endophytic communities in seasonally flooded tropical forests?

PREMISE: In the Amazon basin, seasonally flooded (SF) forests offer varying water constraints, providing an excellent way to investigate the role of habitat selection on microbial communities within plants. However, variations in the microbial community among host plants cannot solely be attributed to environmental factors, and how plant traits contribute to microbial assemblages remains an open question. METHODS: We described leaf- and root-associated microbial communities using ITS2 and 16 S high-throughput sequencing and investigated the stochastic-deterministic balance shaping these community assemblies using two null models. Plant ecophysiological functioning was evaluated by focusing on 10 leaf and root traits in 72 seedlings, belonging to seven tropical SF tree species in French Guiana. We then analyzed how root and leaf traits drove the assembly of endophytic communities. RESULTS: While both stochastic and deterministic processes governed the endophyte assembly in the leaves and roots, stochasticity prevailed. Discrepancies were found between fungi and bacteria, highlighting that these microorganisms have distinct ecological strategies within plants. Traits, especially leaf traits, host species and spatial predictors better explained diversity than composition, but they were modest predictors overall. CONCLUSIONS: This study widens our knowledge about tree species in SF forests, a habitat sensitive to climate change, through the combined analyses of their associated microbial communities with functional traits. We emphasize the need to investigate other plant traits to better disentangle the drivers of the relationship between seedlings and their associated microbiomes, ultimately enhancing their adaptive capacities to climate change.

Connections between soil microbes, land use and European climate: Insights for management practices.

Soil microbial biomass and activity strongly depend on land use, vegetation cover, climate, and soil physicochemical properties. In most cases, this dependence was assessed by one-to-one correlations while by employing network analysis, information about network robustness and the balance between stochasticity and determinism controlling connectivity, was revealed. In this study, we further elaborated on the hypothesis of Smith et al. (2021) that cropland soils depended more on climate variables and therefore are more vulnerable to climate change. We used the same dataset with that of Smith et al. (2021) that contains seasonal microbial, climate and soil variables collected from 881 soil points representing the main land uses in Europe: forests, grassland, cropland. We examined complete (both direct and indirect relationships) and incomplete networks (only direct relationships) and recorded higher robustness in the former. Partial Least Square results showed that on average more than 45% of microbial attributes' variability was predicted by climate and habitat drivers denoting medium to strong effect of habitat filtering. Network architecture slightly affected by season or land use type; it followed the core/periphery structure with positive and negative interactions and no hub nodes. Microbial attributes (biomass, activity and their ratio) mostly belong to core block together with Soil Organic Carbon (SOC), while climate and soil variables to periphery block with the exception of cropland networks, denoting the higher dependence between microbial and climate variables in these latter. All complete networks appeared robust except for cropland and forest in summer, a finding that disagrees with our initial hypothesis about cropland. Networks' connectivity was controlled stronger by stochasticity in forest than in croplands. The lack of human interventions in forest soils increase habitat homogeneity enhancing the influence of stochastic agents such as microbial unlimited dispersal and/or stochastic extinction. The increased stochasticity implies the necessity for proactive management actions.

Transcriptional repression in stochastic gene expression, patterning, and cell fate specification.

Development is often driven by signaling and lineage-specific cues, yielding highly uniform and reproducible outcomes. Development also involves mechanisms that generate noise in gene expression and random patterns across tissues. Cells sometimes randomly choose between two or more cell fates in a mechanism called stochastic cell fate specification. This process diversifies cell types in otherwise homogenous tissues. Stochastic mechanisms have been extensively studied in prokaryotes where noisy gene activation plays a pivotal role in controlling cell fates. In eukaryotes, transcriptional repression stochastically limits gene expression to generate random patterns and specify cell fates. Here, we review our current understanding of repressive mechanisms that produce random patterns of gene expression and cell fates in flies, plants, mice, and humans.