Biology subjects

Grilli, J.

Publications and source records attributed to Grilli, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Environmental history can counteract nutrient quality in shaping the evolution of bacterial growth rates

Despite sharing nearly identical enzymes, strains of the same microbial species display wide variability of growth rates when growing on the same nutrients. Here, we show that such growth rate variability can emerge as a consequence of evolutionary adaptation to different patterns of environmental fluctuations. We develop a mathematical model combining cellular proteome allocation with eco-evolutionary dynamics in fluctuating environments. We find that different patterns of environmental fluctuations select for distinct growth profiles. Slow-growing strains repeatedly outcompete fast-growers when evolved in rapidly changing environments. In a range of environments with asymmetric nutrient availability, evolution reproducibly leads to the robust diversification of two coexisting strains. We develop a theoretical framework based on adaptive dynamics which quantitatively predicts these evolutionary outcomes and explains them in terms of the growth-lag tradeoff that all cells face. Finally, we show that the model can reproduce several patterns in growth-rate data from closely-related strains. Our results highlight that bacterial growth rates can rapidly adapt to their recent environmental history by proteome allocation alone, in ways that often oppose expectations based on nutrient quality.

evolutionary biology↗

bioRxiv2026

Leveraging DNA and RNA oscillatory dynamics to investigate the ecology and physiology of a freshwater microbial community

The dynamics of microbial communities in temporally varying environments are often mediated by the physiological responses of their members. However, physiological measurements remain difficult to obtain \textit{in situ} from diverse communities in nature, limiting our understanding of microbial community dynamics in changing environments. Here, we investigate the physiological dynamics of a microbial community by applying a macroecological approach to a multi-year timeseries of paired ribosomal RNA (rRNA) and DNA (rDNA) measurements from a freshwater community, interpreting their relationship by developing a model of microbial physiology. Leveraging the existence of seasonal oscillations, we developed a data-driven minimal model that provided macroecological predictions and interpretations of rDNA, rRNA, and their relationship. Using this model, we examined two macroecological patterns with physiological interpretations: 1) time averaged rRNA:rDNA as a measure of a community member's steady-state and 2) the temporal ordering of rRNA fluctuations relative to rDNA. We found that rRNA:rDNA increased with the number of 16S rRNA operon copies, which in the context of our model suggests a superlinear effect of copy number. Temporal fluctuations of rRNA tended to \textit{precede} those of rDNA, consistent with the view that increases in ribosomal content can lead to future biomass. We then identified environmental variables where rRNA and rRNA:rDNA displayed greater sensitivity than rDNA, reasoning that rRNA:rDNA, as a reflection of the growth rate, may respond more strongly to environmental change. The ratio rRNA:rDNA provided greater sensitivity than rDNA for the majority of the aquatic environmental variables tested, particularly those reflecting the degree of eutrophy vs. oligotrophy. The results of this work indicate that the ratio rRNA:rDNA may be underutilized as a measure of the physiological state of natural communities.

microbiology↗

bioRxiv2025

Universal sublinear population growth density dependence unrelated to resource limitation

Growth slows as population density increases, sensitively influencing species coexistence, conservation, and resource management. This slowdown is widely attributed to declining per-capita access to energy and nutrients, and standard models of resource-limited growth predict superlinear declines of growth rate with density. However, we observed sublinear declines for 94% of 4,022 bacterial and eukaryotic growth curves. High-resolution experiments with Escherichia coli revealed a two-phase density dependence: a dominant sublinear regime that transitions to superlinear decline only very near population saturation. The sublinear pattern was invariant across initial resource concentrations, ruling out resource depletion as the cause of early slowing. Our results challenge the standard resource-limitation paradigm and identify non-resource inhibitory processes as a widespread and previously unappreciated regulator of population growth.

ecology↗

bioRxiv2025

A balance between environmental filtering and competitive exclusion modulates the macroecology of alternative stable states in microbial communities

Predicting the assembly and stability of microbial ecosystems is fundamentally challenged by the stochas-ticity inherent in historical contingencies. Although these dynamics often appear reproducible at coarse taxonomic or functional scales, the mechanisms governing fine-grained species-level variation and the emergence of alternative stable states remain an unresolved problem in ecology. Bridging the gap between individual species variability and the emergence of predictable community-level attractors is essential for a mechanistic understanding of ecosystem resilience. Here, by analyzing replicate microbial communities assembled in vitro, we demonstrate that community structure is built upon individual species exhibiting robustly bimodal abundance distributions. Each taxon exists in either a high- or low-abundance state. We show that the collective configuration of these binary species states--a phenomenon also observed in natural systems--drives the correlation structure between community members and defines a set of discrete, alternative community-level attractors. By examining the prevalence of species across these attractors, we uncover a striking taxonomic signal: phylogenetically close relatives are significantly more likely to display reciprocal prevalence patterns--where the dominance of one relative necessitates the suppression of the other--than expected by chance. This discovery directly rejects models based on simple functional redundancy. Instead, it proves that state-dependent competitive exclusion is a primary driver of community divergence, where the identity of the "winner" is contingent on the collective state of the entire community. Our work reframes microbial community structure through the lens of discrete population states, providing a predictive framework for understanding ecosystem stability and the engineering of community functions.

ecology↗

bioRxiv2025

Resource ratio fluctuations drive the evolution of microbial metabolic strategies

Microorganisms employ diverse metabolic strategies to cope with environmental variability, yet the evolutionary conditions that favor specialists, hierarchical utilizers, or co-utilizers remain poorly understood. Here, we investigate how fluctuations in resource supply ratios drive the evolution of metabolic strategies. By simulating microbial community assembly in environments with variable resource ratios, we show that diverse metabolic strategies emerge as evolutionary optima in different fluctuation regimes. We find that environments with balanced resource supply select for specialists, whereas imbalanced environments select for generalists. As the magnitude of fluctuations in resource supply ratios progressively increases, generalist strategies smoothly shift from those of hierarchical utilizers to co-utilizers. We identify the duration of temporal niches as the mechanistic driver that selects for reduced lag times over maximal growth rates. These findings demonstrate how the temporal structure of resource availability can spontaneously generate the three distinct metabolic strategies observed in nature.

ecology↗

bioRxiv2025

Label invariance: a guiding principle for ecological models

Ecological models, though diverse in form, are strengthened when they obey guiding principles. We formalize and advocate for a foundational principle we call "label invariance", which says that a models dynamics must remain the same when identical individuals are arbitrarily grouped into distinct sub-populations. This principle is a necessary consequence of trait continuity--the observation that ecological interactions change continuously as organisms become more similar. Violation of label invariance often implies a hidden, intrinsic niche differentiation between species, which may obscure the mechanisms of biodiversity maintenance. We provide a general framework for constructing both deterministic and stochastic models that follow label invariance. We further demonstrate its utility as a complementary, non-statistical tool for empirical model selection. In sum, label invariance provides an important test for evaluating existing ecological models and a guide for developing new ones, promoting clarity in model assumptions from the outset.

ecology↗

bioRxiv2025

Longitudinal dynamics of ethanol-resistant microbes and sporulation in the human gut

BackgroundA considerable proportion of human gut bacteria use some form of dormancy to survive unfavorable conditions such as oxygen exposure, antibiotic treatment, and inflammation. Dormancy strategies enable persistence, allow recolonization following disruptions, and facilitate transmission between individuals. In this study, we explored the composition, characteristics, and longitudinal dynamics of the dormant community in the gut as determined by ethanol resistance. We conducted a six-month longitudinal study on nine healthy male adults, who were sampled every two weeks. Gut microbiota composition was determined with 16S rRNA amplicon sequencing in untreated (bulk microbiota) and ethanol treated samples. A rigorous algorithm was implemented to determine the ethanol-resistant and non-resistant community within the gut microbiota. ResultsWe detected ethanol-resistant gut bacteria in eight distinct phyla, with the highest number of OTUs observed in the phylum Bacillota, and substantial representation in Actinomycetota and Pseudomonadota. In general, ethanol-resistant taxa exhibited higher relative abundance and longer within-individual persistence compared to non-resistant taxa. Ethanol resistance indicated a higher rate of sharing between individuals, independent of phylum. Ethanol-resistant Bacillota also exhibited longer persistence within the gut over the six-months period in comparison to ethanol non-resistant Bacillota and other ethanol-resistant or ethanol non-resistant phyla. Finally, the analysis of sporulation frequency of highly prevalent ethanol-resistant Bacillota members revealed that sporulation occurred in each individual synchronously among different community members but varied significantly between individuals. Major shifts in sporulation frequency were associated with well-known disturbances such as antibiotic therapy and travel. ConclusionOur findings highlight that sporulation of Bacillota members and alternative dormancy strategies employed by non-Bacillota members contribute to bacterial persistence in the human gut and facilitate transmission between unrelated individuals. Moreover, we show that sporulation frequency (specific to Bacillota) is a dynamic, community-level process shaped by host- and time-specific environmental factors rather than intrinsic, species-specific traits.

ecology↗

bioRxiv2025

The macroecological dynamics of sojourn trajectories in the human gut microbiome

The human gut microbiome is a dynamic ecosystem. Host behaviors (e.g., diet) provide a regular source of environmental variation that induces fluctuations in the abundances of resident microbiota. Despite these displacements, microbial community members remain highly resilient. Population abundances tend to fluctuate around characteristic steady-state abundances over long timescales in healthy human hosts. These temporary excursions from steady-state abundances, known as sojourn trajectories, have the potential to inform our understanding of the fundamental dynamics of the microbiome. However, to our knowledge, the macroecology of sojourn trajectories has yet to be systematically characterized. In this study, we leverage theoretical tools from the study of random walks to characterize the duration of sojourn trajectories, their shape, and the degree that diverse community members exhibit similar qualitative and quantitative dynamics. We apply the Stochastic Logistic Model as a theoretical lens for interpreting our empirical observations. We find that the typical timescale of a sojourn trajectory does not depend on the mean abundance of a community member (i.e., carrying capacity), although it is strongly related to its coefficient of variation (i.e., environmental noise). This work provides fundamental insight into the dynamics, timescales, and fluctuations exhibited by diverse microbial communities. ImportanceMicroorganisms in the human gut often fluctuate around a characteristic abundance in healthy hosts over extended periods of time. These typical abundances can be viewed as steady-states, meaning that fluctuating abundances do not continue towards extinction or dominance, but rather return to a specific value over a typical timescale. Here we empirically characterize the 1) length (i.e., number of days), 2) relationship between length and area, and 3) typical deviation of a sojourn trajectory. These three patterns can be explained and unified through an established minimal model of ecological dynamics, the Stochastic Logistic Model of growth.

ecology↗

bioRxiv2023

Coupled ribosome allocation and nutrient sensing lead to incoherent feedback and oscillatory response in bacterial growth.

Current theories of bacterial growth physiology demonstrate impressive predictive power but are often phenomenological, lacking mechanistic detail. Incorporating such details would significantly enhance our ability to predict and control bacterial growth under varying environmental conditions. The "Flux Controlled Regulation" (FCR) model serves as a reference framework, linking ribosome allocation to translation efficiency through a steady-state assumption. However, it neglects ppGpp-mediated nutrient sensing and transcriptional regulation of ribosomal operons. Here, we propose a mechanistic model that extends the FCR framework by incorporating three key components: (i) the amino acid pool, (ii) ppGpp sensing of translation elongation rate, and (iii) transcriptional regulation of protein allocation by ppGpp-sensitive promoters. Our model aligns with observed steady-state growth laws and makes testable predictions for unobserved quantities. We show that during environmental changes, the incoherent feedback between sensing and regulation generates oscillatory relaxation dynamics, a behavior that we support by new and existing experimental data.

systems biology↗

bioRxiv2023

Macroecological laws in experimental microbial systems

Ecology has historically benefited from the characterization of statistical patterns of biodiversity within and across communities, an approach known as macroecology. Within microbial ecology, macroecological approaches have identified universal patterns of diversity and abundance that can be captured by effective models. Experimentation has simultaneously played a crucial role, as the advent of high-replication community time-series has allowed researchers to investigate underlying ecological forces. However, there remains a gap between experiments performed in the laboratory and macroecological patterns documented in natural systems, as we do not know whether these patterns can be recapitulated in the lab and whether experimental manipulations produce macroecological effects. This work aims at bridging the gap between experimental ecology and macroecology. Using high-replication time-series, we demonstrate that microbial macroecological patterns observed in nature exist in a laboratory setting, despite controlled conditions, and can be unified under the Stochastic Logistic Model of growth (SLM). We find that demographic manipulations (e.g., migration) impact observed macroecological patterns. By modifying the SLM to incorporate said manipulations alongside experimental details (e.g., sampling), we obtain predictions that are consistent with macroecological outcomes. By combining high-replication experiments with ecological models, microbial macroecology can be viewed as a predictive discipline.

ecology↗

bioRxiv2023

Macroecological patterns in coarse-grained microbial communities

The structure and diversity of microbial communities is intrinsically hierarchical due to the shared evolutionary history of their constituents. This history is typically captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. Connecting community diversity to the joint ecological dynamics of the abundances of these groups is a central problem of community ecology. However, how microbial diversity depends on the scale of observation at which groups are defined has never been systematically examined. Here, we used a macroecological approach to quantitatively characterize the structure and diversity of microbial communities among disparate environments across taxonomic and phylogenetic scales. We found that measures of biodiversity at a given scale can be consistently predicted using a minimal model of ecology, the Stochastic Logistic Model of growth (SLM). This result suggests that the SLM is a more appropriate null-model for microbial biodiversity than alternatives such as the Unified Neutral Theory of Biodiversity. Extending these within-scale results, we examined the relationship between measures of biodiversity calculated at different scales (e.g., genus vs. family), an empirical pattern predicted by the Diversity Begets Diversity (DBD) hypothesis. We found that the relationship between richness estimates at different scales can be quantitatively predicted assuming independence among community members.Contrastingly, only by including correlations between the abundances of community members (e.g., as the consequence of interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of organization and establish a sharp demarcation between recently proposed macroecological patterns that are not and are affected by ecological interactions.

ecology↗

bioRxiv2023

Metacommunity Structure Preserves Genome Diversity in the Presence of Gene-specific Selective Sweeps.

The horizontal transfer of genes is fundamental for the eco-evolutionary dynamics of microbial communities, such as oceanic plankton, soil, and the human microbiome. In the case of an acquired beneficial gene, classic population genetics would predict a genome-wide selective sweep, whereby the genome spreads clonally with the gene, removing genome diversity. Instead, several sources of metagenomic data show the existence of "gene-specific sweeps", whereby a beneficial gene spreads across a bacterial community, maintaining genome diversity. Several hypotheses have been proposed to explain this process, including the decreasing gene flow between ecologically distant populations, frequency-dependent selection from linked deleterious allelles, and very high rates of horizontal gene transfer. Here, we propose an additional possible scenario grounded in eco-evolutionary principles. Specifically, we show by a mathematical model and simulations that a metacommunity where species can occupy multiple patches helps maintain genome diversity. Assuming a scenario of patches dominated by single species, our model predicts that diversity only decreases moderately upon the arrival of a new beneficial gene, and that losses in diversity can be quickly restored. We explore the generic behavior of diversity as a function of three key parameters, frequency of insertion of new beneficial genes, migration rates and horizontal transfer rates.Our results provides a testable explanation for how diversity can be maintained given gene-specific sweeps even in the absence of high horizontal gene transfer rates.

evolutionary biology↗

bioRxiv2021

The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial

The large taxonomic variability of microbial community composition is a consequence of the combination of environmental variability, mediated through ecological interactions, and stochasticity. Most of the analysis aiming to infer the biological factors determining this difference in community structure start by quantifying how much communities are similar in their composition, trough beta-diversity metrics. The central role that these metrics play in microbial ecology does not parallel with a quantitative understanding of their relationships and statistical properties. In particular, we lack a framework that reproduces the empirical statistical properties of beta-diversity metrics. Here we take a macroecological approach and introduce a model to reproduce the statistical properties of community similarity. The model is based on the statistical properties of individual communities and on a single tunable parameter, the correlation of species carrying capacities across communities, which sets the difference of two communities. The model reproduces quantitatively the empirical values of several commonly-used beta-diversity metrics, as well as the relationships between them. In particular, this modeling framework naturally reproduces the negative correlation between overlap and dissimilarity, which has been observed in both empirical and experimental communities and previously related to the existence of universal features of community dynamics. In this framework, such correlation naturally emerges due to the effect of random sampling. AUTHORS SUMMARYSeveral biological and ecological forces shape the composition of microbial communities. But also contingency and stochasticity play an important role. Comparing communities, identifying which conditions are similar in communities with similar species composition, allows to identify which forces shape their structure. Here we introduce a modeling framework which reproduces the statistical features of community similarity. We identify a single relevant parameter that captures in a single number the multidimensional nature of similarity in community composition. These results set the basis for a quantitative, and predicting, theory of the statistical properties of microbial communities.

ecology↗

bioRxiv2021

Ecological stochasticity and phage induction diversify bacterioplankton communities at the microscale

In many natural environments, microorganisms self-assemble around heterogeneously distributed resource patches. The growth and collapse of populations on resource patches can unfold within spatial ranges of a few hundred micrometers or less, making such microscale ecosystems hotspots of biological interactions and nutrient fluxes. Despite the potential importance of patch-level dynamics for the large-scale evolution and function of microbial communities, we have not yet been able to delineate the ecological processes that control natural populations at the microscale. Here, we addressed this challenge in the context of microbially-mediated degradation of particulate organic matter by characterizing the natural marine communities that assembled on over one thousand individual microscale chitin particles. Through shotgun metagenomics, we found significant variation in microscale community composition despite the similarity in initial species pools across replicates. Strikingly, a subset of particles was highly populated by rare chitin-degrading strains; we hypothesized that their conditional success reflected the impact of stochastic colonization and growth on community assembly. In contrast to the conserved functional structures that emerge in ecosystems at larger scales, this taxonomic variability translated to a wide range of predicted chitinolytic abilities and growth returns at the level of individual particles. We found that predation by temperate bacteriophages, especially of degrader strains, was a significant contributor to the variability in the bacterial compositions and yields observed across communities. Our study suggests that initial stochasticity in assembly states at the microscale, amplified through biotic interactions, may have significant consequences for the diversity and functionality of microbial communities at larger scales. Significance StatementThe biogeochemical consequences of the degradation of particulate organic matter by microorganisms represent the cumulative effect of microbial activity on individual microscale resource patches. The ecological processes controlling community dynamics in these highly localized microenvironments remain poorly understood. Here, we find that complex marine communities growing on microscale resource particles diverge both taxonomically and functionally despite assembling under identical abiotic conditions from a common species pool. We show that this variability stems from bacteriophage predation and history-dependent factors in community assembly, which create stochastic dynamics that are spatially structured at the microscale. This microscale stochasticity may have significant consequences for the coexistence, evolution, and function of diverse bacterial and viral populations in the global ocean.

microbiology↗

bioRxiv2021

Universality of evolutionary dynamics with arbitrary demography

The assumption of constant population size is central in population genetics. It led to a large body of results, that are robust to modeling choices and that have proven successful to understand evolutionary dynamics. In reality, allele frequencies and population size are both determined by the interaction between a population and the environment. Relaxing the constant-population assumption have two big drawbacks. It increases the technical difficulty of the analysis, and it requires specifying a mechanism for the saturation of the population size, possibly making the results contingent on model details. Here, we develop a framework that encompasses a great variety of systems with an arbitrary mechanism for population growth limitation. By using techniques based on scale separation for stochastic processes, we are able to calculate analytically properties of evolutionary trajectories, such as the fixation probability. Remarkably, these properties assume a universal form with respect to our framework, which depends on only three parameters related to the inter-generation timescale, the invasion fitness, and the carrying capacity of the strains. In other words, different systems, such as Lotka-Volterra or a chemostat model (contained in our framework), share the same evolutionary outcomes after a proper re-mapping of their parameters. An important and surprising consequence of our results is that the direction of selection can be inverted, with a population evolving to reach lower values of invasion fitness.

evolutionary biology↗

bioRxiv2021

Eco-evolutionary dynamics lead to functionally robust and redundant communities

Microbial communities are functionally stable and taxonomically variable: species abundances fluctuate over time and space, while the functional composition is robust and reproducible. These observations imply functional redundancy: the same function is performed by many species, so that one may assemble communities with different species but the same functional composition. The clarity of this observation does not parallel with a theoretical understanding of its origin. Here we study the eco-evolutionary dynamics of communities interacting through competition and cross-feeding. We show that the eco-evolutionary trajectories rapidly converge to a "functional attractor", characterized by a functional composition uniquely determined by environmental conditions. The taxonomic composition instead follows non-reproducible dynamics, constrained by the conservation of the functional composition. Our framework provides a deep theoretical foundation to the empirical observations of functional robustness and redundancy.

ecology↗

bioRxiv2021

Role of protein degradation in growth laws

Growing cells adopt common basic strategies to achieve optimal resource allocation under limited resource availability. Our current understanding of such "growth laws" neglects degradation, assuming that it occurs slowly compared to the cell cycle duration. Here we argue that this assumption cannot hold at slow growth, leading to important consequences. We propose a simple framework showing that at slow growth protein degradation is balanced by a fraction of "maintenance" ribosomes. Consequently, active ribosomes do not drop to zero at vanishing growth, but as growth rate diminishes, an increasing fraction of active ribosomes performs maintenance. Through a detailed analysis of compiled data, we show that the predictions of this model agree with data from E. coli and S. cerevisiae. Intriguingly, we also find that protein degradation increases at slow growth, which we interpret as a consequence of active waste management and/or recycling. Our results highlight protein turnover as an underrated factor for our understanding of growth laws across kingdoms. Author summaryThe idea that simple quantitative relationships relate cell physiology to cellular composition dates back to the 1950s, but the recent years saw a leap in our understanding of such "growth laws", with relevant implications regarding the interdependence between growth, metabolism and biochemical networks. However, recent works on nutrient-limited growth mainly focused on laboratory conditions that are favourable to growth. Thus, our current mathematical understanding of the growth laws neglects protein degradation, under the argument that it occurs slowly compared to the timescale of the cell cycle. Instead, at slow growth the timescales of mass loss from protein degradation and dilution become comparable. In this work, we propose that protein degradation shapes the quantitative relationships between ribosome allocation and growth rate, and determines a fraction of ribosomes that do not contribute to growth and need to remain active to balance degradation.

systems biology↗

bioRxiv2021

Heavy-tailed abundance distributions from stochastic Lotka-Volterra models

Microbial communities found in nature are composed of many rare species and few abundant ones, as reflected by their heavy-tailed abundance distributions. How a large number of species can coexist in those complex communities and why they are dominated by rare species is still not fully understood. We show how heavy-tailed distributions arise as an emergent property from large communities with many interacting species in population-level models. To do so we rely on stochastic logistic and generalized Lotka-Volterra models for which we introduce a global maximal capacity. This maximal capacity accounts for the fact that communities are limited by available resources and space. In a parallel ad-hoc approach, we obtain heavy-tailed abundance distributions from non-interacting models through specific distributions of the parameters. We expect both mechanisms, interactions between many species and specific parameter distributions, to be relevant to explain the observed heavy tails.

ecology↗