Biology subjects

Mitri, S.

Publications and source records attributed to Mitri, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A reproductive hierarchy controls mating fates among sibling yeast gametes

Mating strategies shape inbreeding, gene flow and genetic diversity, yet their outcomes remain challenging to quantify. In Saccharomyces, population-genomic models predict mating predominantly between sibling meiotic products, with rare selfing and outcrossing. Here we developed an integrated framework combining multicolor flow cytometry, time-resolved imaging and mathematical modelling to directly quantify mating across scales. Bulk measurements revealed coexisting strategies whose frequencies vary with density, genotype and environment. A minimal stochastic model uses these population-level patterns to infer probabilistic hierarchies among mating modes, which single-tetrad analyses independently support. These reveal divergent and interdependent reproductive trajectories among sibling gametes, with one pair typically mating first and reshaping opportunities for the others. Temporal heterogeneity in germination and mating further diversifies reproductive outcomes and shapes compatibility, as experimental synchronization overcomes temporal reproductive barriers, enabling hybridization. Together, reproductive strategy emerges from heterogeneous gamete fates, raising the possibility that sibling diversification constitutes reproductive bet-hedging.

genetics↗

bioRxiv2026

Metabolic processes shape microbial interaction distributions

Microbial communities are largely driven by metabolic interactions, whereby species compete for shared resources and exchange byproducts. Such interactions are commonly classified by their sign alone. Yet, theory shows that the strength of interactions and the shape of their distribution impact how many and which species coexist. What this shape looks like in microbial communities and why has rarely been examined. Starting from a consumer-resource model of resource competition and cross-feeding, we find that these metabolic processes generate a skewed distribution with "many weak, few strong" interactions - a pattern previously documented in the food webs of larger organisms. This skew emerges whenever species have different substrate preferences and when metabolite leakage is sufficiently high. Across nine microbial datasets spanning diverse community origins, empirical interactions not only qualitatively display this skewed pattern but quantitatively follow the relationship between higher-order statistics predicted by the model. Generalized Lotka-Volterra (gLV) models, the standard framework for predicting community diversity, typically assume Gaussian interaction strengths. Instead, we show that the observed "many weak, few strong" interaction distribution is better captured by a lognormal distribution than the symmetric Gaussian distribution. Sampling interactions in gLV models from a lognormal distribution yields more stable communities in simulations and more accurately predicts the diversity observed in the two datasets where community-assembly experiments were performed. Together, these results reveal a metabolic origin for the "many weak, few strong" pattern of microbial interactions and show that using empirically grounded interaction distributions improves predictions of community diversity. SignificanceHow strongly do microbes affect each others growth? The distribution of their interaction strengths shapes how many species coexist in a community and how stable they are. Across nine datasets, we show that most interactions are weak, with rare strong outliers, mirroring the macro-organism "many weak, few strong" interaction pattern. Using mathematical models, we show that competition for shared resources and metabolite cross-feeding, both prevalent in microbes, suffice to generate this pattern. Accounting for the full distribution of interaction strengths, rather than only their mean and variance, improves predictions of community diversity in simulations and empirical datasets. This work connects microbial metabolic processes to community-level structure and provides a route for predicting diversity when interactions cannot be exhaustively measured.

ecology↗

bioRxiv2026

A competition-protection balance explains the evolution of resistance within simple microbial communities

Stressful environments can pose a threat to microbial populations, but resistant individuals can emerge and avoid extinction. Adaptation to stress is classically studied in isolated microbial species, ignoring ecological interactions, a key component of natural ecosystems. A growing body of experimental work has shown that community context can affect resistance evolution due to a large variety of mechanisms. Here we set out to identify the minimal components needed to predict the likelihood of acquiring resistance in a focal species embedded within a simple community. To achieve this, we developed a mathematical model based on evolutionary rescue theory and validated it with two experimental systems: Escherichia coli evolving on exposure to the antibiotic nitrofurantoin alone or with one of 14 bacterial isolates from urinary tract infections, and Microbacterium liquefaciens evolving in ampicillin alone or with ampicillin-degrading Comamonas testosteroni. One key factor that emerged from our analyses - the relative strength of competition versus protection - could explain whether a focal species is more or less likely to evolve resistance in the presence of a partner species. While competition always hinders the emergence of resistance, protection can rescue the focal species in two ways: (i) ecological rescue, when the partner species completely removes the antibiotic and favors the survival of the susceptible population, or (ii) evolutionary rescue, when the partner only lowers antibiotic concentrations and favors the emergence of resistant variants, a previously overlooked evolutionary consequence of detoxification. Overall, by integrating theory and experiments, we propose a framework that clarifies how ecological interactions favor or hinder the evolution of resistance to antibiotics or potentially other stressors. SignificanceBacteria can rapidly adapt to resist stressors, such as antibiotics. While resistance evolution in single populations or species is well understood, it remains unclear how ecological interactions with other species influence this process. We develop a mathematical framework to predict what interactions should favor resistance evolution and validate it with two sets of experiments where bacteria adapt to antibiotics in small communities. Our work demonstrates that interactions with other species shape the probability of evolving resistance in a predictable way, determined by the balance between competition and protection against the stressor. By identifying the key factors that drive these dynamics, our work helps explain how bacteria adapt to environmental challenges within species-rich ecosystems.

microbiology↗

bioRxiv2026

Micromolar concentrations of metabolites enable coexistence of bacterial species in chemostats

Competition for a single limiting resource is expected to lead to competitive exclusion, yet diverse microbial communities persist even in nutrient-poor environments. Cross-feeding of essential metabolites is one mechanism that can promote coexistence between species, but its contribution is difficult to pinpoint experimentally. Here, we studied a prototroph-auxotroph pair growing on a single carbon source in chemostats. In minimal medium, the prototroph Comamonas testosteroni (Ct) supplied thiamine to the thiamine-auxotroph Ochrobactrum anthropi (Oa), allowing stable coexistence in agreement with consumer-resource theory. Contrary to our expectation that supplying thiamine would remove the dependency and lead to exclusion of Ct, coexistence persisted even when thiamine was supplemented. Our theoretical anlaysis showed that coexistence between competitors can be maintained by trace concentrations of an additional metabolite if it is taken up at sufficiently high affinity by the weaker competitor. Consistent with this prediction, targeted metabolomics and spent-medium assays identified growth-enhancing compounds at micromolar concentrations in Oa spent medium and as residues in fresh medium. Model analysis further showed that such weak positive effects can qualitatively change coexistence outcomes in chemostats while remaining undetected in standard batch interaction assays. Together, our results show that trace metabolites and subtle positive effects can reshape coexistence outcomes and should be incorporated into ecological models and interaction measurements.

ecology↗

bioRxiv2026

Cross-feeding enables robust coexistence between four bacterial species

AbstractMicrobial diversity is often assumed to be limited by the number of available resources, yet many communities persist well beyond that expectation. Understanding the mechanisms that enable such coexistence remains a central question in microbial ecology. Here, using a four-species bacterial consortium, we asked whether coexistence can emerge from interactions between species rather than from the external environment alone. Across 31 simple nutrient conditions, including 16 single-resource environments, all four species persisted and repeatedly reached stable coexistence. We then chose 27 additional conditions to further probe the boundaries of coexistence by varying resource concentrations, temporal dynamics, nutrient complexity and relief of auxotrophy-associated dependencies, and only observed the extinction of one species in one of these conditions. Although the community composition in each environment was largely shaped by species fitness on the supplied resources, experimental assays and consumer-resource modeling showed that the coexistence was not explained by resource supply, but rather by cross-feeding and niche partitioning of metabolic byproducts. These metabolic interactions were strong enough to sustain coexistence even for species unable to use the supplied resources directly. Furthermore, robust coexistence across environments appears to be an emergent property of microbial communities, ingrained in members metabolic byproduct profiles and niche differences. Our findings demonstrate how microbes can increase the chemical complexity of their environment sufficiently to maintain coexistence well beyond what is expected from external resource supply. SignificanceUnderstanding the drivers of microbial diversity is essential for managing natural ecosystems and designing synthetic microbiomes. This study challenges the conventional application of the competitive exclusion principle, demonstrating that a four-species consortium can coexist across 31 chemically and metabolically diverse one- and two-carbon source environments. By systematically testing and ruling out alternative stabilizing mechanisms, we show that co-existence is an emergent property of the consortium, sustained by metabolic cross-feeding and niche partitioning. Guided by computational models, we identify hallmarks of robust co-existence in simple environments, including high variance in resource affinities and growth on partner-derived metabolites. Our work demonstrates how microbes modify their environment to sustain high diversity and provides principles for designing synthetic microbiomes that persist across environments.

microbiology↗

bioRxiv2025

Feeding your enemy's enemy: Acidifying bacteria inhibit pathogenic bacteria more strongly with increasing glucose

Classical microbiology has focused on directly suppressing pathogens using drugs, ignoring other harmless microbial species living alongside the pathogens. We now have a much better understanding of how species interact and affect one anothers growth within microbial communities, for example through chemical production. Here we capitalize on this understanding to demonstrate how one can manipulate and control the strength of interactions between bacterial species, and combine this with antibiotics to fully suppress and eliminate pathogens. Using experiments and a mathematical model, we first show how Citrobacter freundii can reduce the environmental pH to enhance the effect of ampicillin on the pathogen Pseudomonas aeruginosa. This negative interaction from C. freundii to P. aeruginosa can be strengthened by increasing glucose concentrations. Our proof-of-concept approach also worked against other pathogens: Klebsiella pneumoniae and Agrobacterium tumefaciens, and a different commensal: Lactobacillus plantarum, a common probiotic species. Overall, we show that taking advantage of the community and chemical context in which microbes live can help to develop efficient strategies to control them. In the medical context, this approach can help to eliminate pathogens thereby reducing our reliance on antibiotics.

microbiology↗

bioRxiv2025

Microbes release lower-value metabolites at higher rates

Microbial interactions are shaped by the exchange of metabolites, ranging from intermediates in central carbon metabolism to amino acids, vitamins and fermentation by-products. While specific metabolic processes such as overflow metabolism are well documented, a general principle explaining the chemical diversity of metabolite release and the variation in individual release rates remains elusive. Here, using a combination of computational and experimental approaches on seven different microbes, we show that release rates are negatively correlated with the value of metabolites, and that this relationship explains more variability in release rates than other frequently associated factors. By measuring metabolite release from 67 E. coli mutants lacking individual transport genes, we find that transporters both cause and counteract net metabolite release. These findings suggest that metabolite release constitutes a fitness cost that microbes have evolved to reduce, while revealing the underlying transport mechanism. This unifying explanation reshapes our understanding of how microbial interactions emerge and persist.

microbiology↗

bioRxiv2024

The evolution of reduced facilitation in a four-species bacterial community

Microbial evolution is typically studied in mono-cultures or in communities of competing species. But microbes do not always compete and how positive inter-species interactions drive evolution is less clear: Initially facilitative communities may either evolve increased mutualism, increased reliance on certain species according to the Black Queen Hypothesis (BQH), or weaker interactions and resource specialization. To distinguish between these outcomes, we evolved four species for 44 weeks either alone or together in a toxic pollutant. These species initially facilitated each other, promoting each others survival and pollutant degradation. After evolution, two species (Microbacterium liquefaciens and Ochrobactrum anthropi) that initially relied fully on others to survive continued to do so, with no evidence for increased mutualism. Instead, Agrobacterium tumefaciens and Comamonas testosteroni (Ct) whose ancestors interacted positively, evolved in community to interact more neutrally and grew less well than when they had evolved alone, suggesting that the community limited their adaptation. We detected several gene loss events in Ct when evolving with others, but these events did not increase its reliance on other species, contrary to expectations under the BQH. We hypothesize instead that these gene loss events are a consequence of resource specialization. Finally, co-evolved communities degraded the pollutant worse than their ancestors. Together, our results support the evolution of weakened interactions and resource specialization, similar to what has been observed in competitive communities.

evolutionary biology↗

bioRxiv2023

Environment-organism feedbacks drive changes in ecological interactions

Ecological interactions, the impact of one organism on the growth and death of another, underpin our understanding of the long-term composition and the functional properties of communities. In recent years, the context-dependency of interactions - their tendency to change values in different environments, locations and at different times - has become an increasingly important theme in ecological research. However, an overarching theoretical assumption has been that external environmental factors are responsible for driving these changes. Here, we derive a theoretical interaction framework which teases apart the separate roles played by these extrinsic environmental inputs and the intrinsic environmental changes driven by organisms within the environment itself. At the heart of our theory is the instantaneous interaction, a quantity that captures the feedback between environmental composition and the growth of organisms within it. In the limit that intrinsic, organismdriven environmental change dominates over external drivers, we find that this feedback can give rise to temporal and spatial context-dependencies as organisms modify the environment over time and/or space. We use small synthetic microbial communities as model ecosystems to demonstrate the power of this framework, using it to predict time-dependent intra-specific interactions in a toxin degradation system and to relate time and spatial dependencies in crossfeeding communities. Our framework helps to explain the ubiquity of interaction context-dependencies in systems where population changes are driven by environmental changes - such as microbial communities - by placing the environment on an equal theoretical footing as the organisms that inhabit it.

ecology↗

bioRxiv2023

Artificial selection optimizes pollutant-degrading bacterial communities

Artificial selection is a promising way to improve microbial community functions, but previous experiments have only shown moderate success. Here, we experimentally evaluate a new method that was inspired by genetic algorithms to artificially select small bacterial communities of known species composition based on their degradation of an industrial pollutant. Starting from 29 randomly generated four-species communities, we repeatedly grew communities for four days, selected the 10 best-degrading communities, and rearranged them into 29 new communities composed of four species of equal ratios whose species compositions resembled those of the most successful communities from the previous round. The best community after 18 such rounds of selection degraded the pollutant better than the best community in the first round. It featured member species that degrade well, species that degrade badly alone but improve community degradation, and free-rider species that did not contribute to community degradation. Most species in the evolved communities did not differ significantly from their ancestors in their phenotype, suggesting that genetic evolution plays a small role at this time scale. These experiments show that artificial selection on microbial communities can work in principle, and inform on how to improve future experiments.

evolutionary biology↗

bioRxiv2023

Oxidative stress changes interactions between two bacterial species

Knowing how species interact within microbial communities is crucial to predicting and controlling community dynamics, but interactions can depend on environmental conditions. The stress-gradient hypothesis (SGH) predicts that species are more likely to facilitate each other in harsher environments. Even if the SGH gives some intuition, quantitative modeling of the context-dependency of interactions requires understanding the mechanisms behind the SGH. In this study, we show with both experiments and a theoretical analysis, that varying the con-centration of a single compound, linoleic acid, modifies the interaction between two bacterial species from competitive at a low concentration, to facilitative at higher concentrations where linoleic acid becomes toxic for one of the two species. We demonstrate that the mechanism behind facilitation is that one species is able to reduce Reactive Oxygen Species (ROS) that are produced spontaneously at higher concentrations of linoleic acid, allowing for short-term rescue of the species that is sensitive to ROS and longer coexistence in serial transfers. In our system, competition and facilitation between species can occur simultaneously, and changing the concentration of a single compound can alter the balance between the two.

microbiology↗

bioRxiv2023

Novel artificial selection method improves function of simulated microbial communities

There is increasing interest in artificially selecting or breeding microbial communities, but experiments have reported modest success. Here, we develop computational models to simulate two previously known selection methods and compare them to a new "disassembly" method. We evaluate all three methods in their ability to find a community that could efficiently degrade toxins, whereby investment into degradation results in slower growth. Our disassembly method relies on repeatedly competing different communities of known species combinations against one another, while regularly shuffling around their species combinations. This approach allows many species combinations to be explored, thereby maintaining enough between-community diversity for selection to act on, and resulting in communities with high performance. Nevertheless, selection at the community level in our simulations did not counteract selection at the individual level, nor the communities ecological dynamics. Species in our model evolved to invest less into community function and more into growth, but increased growth compensated for reduced investment, such that overall community performance was barely affected by within-species evolution. Within-community ecological dynamics were more of a challenge, as we could control them during the selection process, but community composition and function dropped in the longer term. Our work shows that the strength of disassembly lies mainly in its ability to explore different species combinations, and helps to propose alternative designs for community selection experiments.

evolutionary biology↗

bioRxiv2022

Microbial invasion of a toxic medium is facilitated by a resident community but inhibited as the community co-evolves

Predicting whether microbial invaders will colonize an environment is critical for managing natural and engineered ecosystems, and controlling infectious disease. Invaders often face competition by resident microbes. But how invasions play out in communities dominated by facilitative interactions is less clear. We previously showed that growth medium toxicity can promote facilitation between four bacterial species, as species that cannot grow alone rely on others to survive. Following the same logic, here we allowed other bacterial species to invade the four-species community, and found that invaders could more easily colonize a toxic medium when the community was present. In a more benign environment instead, invasive species that could survive alone colonized more successfully when the residents were absent. Next, we asked whether early colonists could exclude future ones through a priority effect, by inoculating the invaders into the resident community only after its members had co-evolved for 44 weeks. Compared to the ancestral community, the co-evolved resident community was more competitive toward invaders, and less affected by them. Our experiments show how communities may assemble by facilitating one another in harsh, sterile environments, but that arriving after community members have co-evolved can limit invasion success.

microbiology↗

bioRxiv2022

Classifying interactions in a synthetic bacterial community is hindered by inhibitory growth medium

Predicting the fate of a microbial community and its member species relies on understanding the nature of their interactions. However, designing simple assays that distinguish between interaction types can be challenging. Here, we performed spent media assays based on the predictions of a mathematical model to decipher the interactions between four bacterial species: Agrobacterium tumefaciens (At), Comamonas testosteroni (Ct), Microbacterium saperdae (Ms) and Ochrobactrum anthropi (Oa). While most experimental results matched model predictions, the behavior of Ct did not: its lag phase was reduced in the pure spent media of At and Ms, but prolonged again when we replenished with our growth medium. Further experiments showed that the growth medium actually delayed the growth of Ct, leading us to suspect that At and Ms could alleviate this inhibitory effect. There was, however, no evidence supporting such "cross-detoxification" and instead, we identified metabolites secreted by At and Ms that were then consumed or "crossfed" by Ct, shortening its lag phase. Our results highlight that even simple, defined growth media can have inhibitory effects on some species and that such negative effects need to be included in our models. Based on this, we present new guidelines to correctly distinguish between different interaction types, such as cross-detoxification and cross-feeding.

microbiology↗

bioRxiv2020

An Integrated Clinical, Omic, and Image Atlas of an Evolving Metastatic Breast Cancer

Mechanisms of therapeutic resistance manifest in metastatic cancers as tumor cell intrinsic alterations and extrinsic microenvironmental influences that can change during treatment. To support the development of methods for the identification of these mechanisms in individual patients, we present here an Omic and Multidimensional Spatial (OMS) Atlas generated from four serial biopsies of a metastatic breast cancer patient during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata including treatment times and doses, anatomic imaging, and blood-based response measurements to exploratory analytics including comprehensive DNA, RNA, and protein profiles, images of multiplexed immunostaining, and 2- and 3-dimensional scanning electron micrographs. These data reveal aspects of therapy-associated heterogeneity and evolution of the cancers genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples showing how integrative analyses of these data provide insights into potential mechanisms of response and resistance, and suggest novel therapeutic vulnerabilities.

cancer biology↗

bioRxiv2020

Microbial species interactions determine community diversity in fluctuating environments

Microorganisms live in environments that inevitably fluctuate between mild and harsh conditions. As harsh conditions may cause extinctions, the rate at which fluctuations occur can shape microbial communities and their diversity, but we still lack an intuition on how. Here, we build a mathematical model describing two microbial species living in an environment where substrate supplies randomly switch between abundant and scarce. We then vary the rate of switching as well as different properties of the interacting species, and measure the probability of the weaker species driving the stronger one extinct. We find that this probability increases with the strength of demographic noise under harsh conditions and peaks at either low, high, or intermediate switching rates depending on both species ability to withstand the harsh environment. This complex relationship shows why finding patterns between environmental fluctuations and diversity has historically been difficult. In parameter ranges where the fittest species was most likely to be excluded, however, the beta diversity in larger communities also peaked. In sum, how environmental fluctuations affect interactions between a few species pairs predicts their effect on the beta diversity of the whole community.

ecology↗

bioRxiv2019

Controlling evolutionary dynamics to optimize microbial bioremediation

Some microbes have a fascinating ability to degrade compounds that are toxic for humans in a process called bioremediation. Although these traits help microbes survive the toxins, carrying them can be costly if the benefit of detoxification is shared by all surrounding microbes, whether they detoxify or not. Detoxification can thereby be seen as a public goods game, where non-degrading mutants can sweep through the population and collapse bioremediation. Here, we constructed an evolutionary game theoretical model to optimize bioremediation in a chemostat initially containing "cooperating" (detoxifying) microbes. We consider two types of mutants: "cheaters" that do not detoxify, and mutants that become resistant to the toxin through private mechanisms that do not benefit others. By manipulating the concentration and flow rate of a toxin into the chemostat, we identified conditions where cooperators can exclude cheaters that differ in their private resistance. However, eventually, cheaters are bound to invade. To overcome this inevitable outcome and maximize detoxification efficiency, cooperators can be periodically reinoculated into the population. Our study investigates the outcome of an evolutionary game combining both public and private goods and demonstrates how environmental parameters can be used to control evolutionary dynamics in practical applications.

evolutionary biology↗

bioRxiv2019

Toxicity drives facilitation between four bacterial species

Competition between microbes is extremely common, with many investing in a wide range of mechanisms to harm other strains and species. Yet positive interactions between species have also been documented. What makes species help or harm each other is currently unclear. Here, we studied the interactions between four bacterial species capable of degrading Metal-Working Fluids (MWF), an industrial coolant and lubricant, which contains growth substrates as well as toxic biocides. We were surprised to find only positive or neutral interactions between the four species. Using mathematical modeling and further experiments, we show that positive interactions in this community are likely due to the toxicity of MWF, whereby each species detoxification benefited the others by facilitating their survival, such that they could grow and degrade MWF better when together. The addition of nutrients, the reduction of toxicity or the addition of more species instead resulted in competitive behavior. Our work provides support to the stress gradient hypothesis by showing how harsh, toxic environments can strongly favor facilitation between microbial species and mask underlying competitive interactions.

ecology↗