Biology subjects

Chacon, J. M.

Publications and source records attributed to Chacon, J. M..

9 recordsLinked to original sources

bioRxiv2026

Cross-feeding reshapes interactions between antibiotic stressors through metabolic feedbacks

Stressors can combine to inhibit organismal growth or function more (synergy) or less (antagonism) strongly than expected from their individual effects, yet whether these interactions are intrinsic properties of cells or stressors, or emergent consequences of ecological interactions remains unclear. Antibiotic combinations provide a tractable model for studying stressor interactions, but have been characterized largely in bacterial monocultures. Here, we compared antibiotic stressor interactions in Escherichia coli growth in monoculture versus when cross-feeding with Salmonella enterica. Across 36 antibiotic pairs, cross-feeding frequently shifts interactions toward antagonism and away from synergy. In the sulfamethoxazole-trimethoprim pair, dual treatment increases carbon excretion by E. coli, promoting partner growth and attenuating stressor synergy through reciprocal metabolic feedback. Perturbing partner metabolite usage restores monoculture-like interaction outcomes across multiple drug pairs. Together, our results demonstrate that stressor interactions in microorganisms can emerge from ecological feedbacks mediated by metabolic excretion.

microbiology↗

bioRxiv2024

The strength of selection for costly toxin production increases with growth rate.

Microbes adopt a diversity of strategies to successfully compete with coexisting strains for space and resources. One common strategy is the production of toxic compounds to inhibit competitors, but the strength and direction of selection for this strategy varies depending on the environment. In particular, existing theoretical and experimental evidence suggests growth in spatially-structured environments makes toxin production more beneficial because competitive interactions are localized. Because higher growth rates increase the localization of resource competition in a structured environment, theory predicts that toxin production should be especially beneficial under these conditions. We tested this hypothesis by developing a genome-scale metabolic modeling approach and complementing it with comparative genomics to investigate the impact of growth rate on selection for costly toxin production. Our modeling approach expands the current abilities of the dynamic flux balance analysis platform COMETS to incorporate signaling and toxin production. Using this capability, we find that our modeling framework predicts that the strength of selection for toxin production increases as growth rate increases. This finding is supported by comparative genomics analyses that include diverse microbial species. Our work emphasizes that toxin production is more likely to be maintained in rapidly-growing, spatially-structured communities, thus improving our ability to manage critical microbial communities and informing natural product discovery.

evolutionary biology↗

bioRxiv2023

Mutualism reduces the severity of gene disruptions in predictable ways across microbial communities.

Predicting evolution in microbial communities is critical for problems from human health to global nutrient cycling. Understanding how species interactions impact the distribution of fitness effects for a focal population would enhance our ability to predict evolution. Specifically, it would be useful to know if the type of ecological interaction, such as mutualism or competition, changes the average effect of a mutation (i.e., the mean of the distribution of fitness effects). Furthermore, how often does increasing community complexity alter the impact of species interactions on mutant fitness? To address these questions, we created a transposon mutant library in Salmonella enterica and measured the fitness of loss of function mutations in 3,550 genes when grown alone versus competitive co-culture or mutualistic co-culture with Escherichia coli and Methylorubrum extorquens. We found that mutualism reduces the average impact of mutations, while competition had no effect. Additionally, mutant fitness in the 3-species communities can be predicted by averaging the fitness in each 2-species community. Finally, the fitness effects of several knockouts in the mutualistic communities were surprising. We discovered that S. enterica is obtaining a different source of carbon and more vitamins and amino acids than we had expected. Our results suggest that species interactions can predictably impact fitness effect distributions, in turn suggesting that evolution may ultimately be predictable in multi-species communities.

microbiology↗

bioRxiv2020

Phage cocktail strategies for the suppression of a pathogen in a cross-feeding coculture

Cocktail combinations of bacteria-infecting viruses (bacteriophage), can suppress pathogenic bacterial growth. However, predicting how phage cocktails influence microbial communities with complex ecological interactions, specifically cross-feeding interactions in which bacteria exchange nutrients, remains challenging. Here, we used experiments and mathematical simulations to determine how to best suppress a model pathogen, E. coli, when obligately cross-feeding with S. enterica. We tested whether the duration of pathogen suppression caused by a two-lytic phage cocktail was maximized when both phage targeted E. coli, or when one phage targeted E. coli and the other its cross-feeding partner, S. enterica. Experimentally, we observed that cocktails targeting both cross-feeders suppressed E. coli growth longer than cocktails targeting only E. coli. Two non-mutually-exclusive mechanisms could explain these results: 1) we found that treatment with two E. coli phage led to the evolution of a mucoid phenotype that provided cross-resistance against both phage, and 2) S. enterica set the growth rate of the co-culture, and therefore targeting S. enterica had a stronger effect on pathogen suppression. Simulations suggested that cross-resistance and the relative growth rates of cross-feeders modulated the duration of E. coli suppression. More broadly, we describe a novel bacteriophage cocktail strategy for pathogens that cross-feed. Originality-Significance StatementCross-feeding, or exchanging nutrients among bacteria, is a type of ecological interaction found in many important microbial communities. Furthermore, cross-feeding interactions are found to play a role in some infections, and research into treating infections with combinations of bacteriophage in cocktails is growing. Here, we used a combination of mathematical modelling and wet-lab experiments to optimize suppression of a model pathogen with a bacteriophage cocktail in a synthetic cross-feeding bacterial coculture. A key finding was that a physiological parameter - growth rate - of the bacteria was important to consider when choosing the most effective cocktail formulation. This work is novel because it highlights an unexpected multispecies-targeting strategy for designing phage cocktails for cross-feeding pathogens and has relevance to many ecological systems ranging from human health to agriculture. We demonstrate how leveraging knowledge of a pathogens ecological interaction has the potential to improve precision medicine and management of microbial systems.

microbiology↗

bioRxiv2019

Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica

With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. We have previously shown that in obligate mutualisms the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners, setting the community antibiotic tolerance at that of the weakest link species. In this study, we extended that hypothesis to test whether obligate cross-feeding would limit the extent and mechanisms of antibiotic resistance evolution. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.\n\nSignificance statementLittle is known about how ecological interactions between bacteria influence the evolution of antibiotic resistance. We tested the impact of metabolic interactions on resistance evolution in an engineered two-species bacterial community. Through experimental and modeling work, we found that obligate metabolic interdependency slows the rate of resistance acquisition, and can change the type and magnitude of resistance mutations that evolve. This work suggests that resistance evolution may be slowed by targeting both a pathogen and its metabolic partners with antibiotics. Additionally, we showed that community context can generate novel trajectories through which antibiotic resistance evolves.

evolutionary biology↗

bioRxiv2019

Increasing growth rate slows adaptation when genotypes compete for diffusing resources

The rate at which a species responds to natural selection is a central predictor of the species ability to adapt to environmental change. It is well-known that spatially-structured environments slow the rate of adaptation due to increased intra-genotype competition. Here, we show that this effect magnifies over time as a species becomes better adapted and grows faster. Using a reaction-diffusion model, we demonstrate that growth rates are inextricably coupled with effective spatial scales, such that higher growth rates cause more localized competition. This has two effects: selection requires more generations for beneficial mutations to fix, and spatially-caused genetic drift increases. Together, these effects diminish the value of additional growth rate mutations in structured environments.\n\nAuthor SummaryWhat determines how quickly a beneficial mutation will spread through a population? The intuitive answer is that mutations that confer faster growth rates will spread at a rate that is relative to the size of the growth-rate benefit. Indeed, this is true in a well-mixed environment where all genotypes compete globally. But most organisms dont live in a simple well-mixed environment. Many organisms, like bacteria, live in a structured environment, such as on the surface of a solid substrate. Does life on a surface change the expectation about the spread of faster-growing mutants? We developed a mathematical model to answer this question, and found that on a surface, the actual growth rates--not just the relative growth rates--were critical to determining how fast a faster-growing mutant spread through a population. When the simulated organisms grew slowly, competition was basically global and a faster-growing mutant could pre-empt resources from far-away competitors. In contrast, when organisms grew more quickly, competition became much more localized, and the faster-growing mutant could only steal resources from neighboring competitors. This result means that there are diminishing returns to series of mutations which confer growth-rate benefits. This idea will help us predict and understand future and past evolutionary trajectories.

evolutionary biology↗

bioRxiv2019

Lytic bacteriophage have diverse indirect effects in a synthetic cross-feeding community

Bacteriophage shape the composition and function of microbial communities. Yet, it remains difficult to predict the effect of phage on microbial interactions. Specifically, little is known about how phage influence mutualisms in networks of cross-feeding bacteria. We modeled the impacts of phage in a synthetic microbial community in which Escherichia coli and Salmonella enterica exchange essential metabolites. In this model, phage attack of either species was sufficient to inhibit both members of the mutualism; however, the evolution of phage resistance ultimately allowed both species to attain yields similar to those observed in the absence of phage. In laboratory experiments, attack of S. enterica with P22vir phage followed these modeling expectations of delayed community growth with little change in the final yield of bacteria. In contrast, when E. coli was attacked with T7 phage, S. enterica, the non-host species, reached higher yields compared to no-phage controls. T7 increased non-host yield by releasing consumable cell debris and by driving evolution of phage resistant E. coli that secreted more carbon. Additionally, E. coli evolved only partial resistance, increasing the total amount of lysed cells available for S. enterica to consume. Our results demonstrate that phage can have extensive indirect effects in microbial communities, and that the nature of these indirect effects depends on metabolic and evolutionary mechanisms.

systems biology↗

bioRxiv2019

Genome-wide assessment of Mycobacterium tuberculosis conditionally essential metabolic pathways

Better understanding of essential cellular functions in pathogenic bacteria is important for the development of more effective antimicrobial agents. We performed a comprehensive identification of essential genes in Mycobacterium tuberculosis, the major causative agent of tuberculosis, using a combination of transposon insertion sequencing (Tn-seq) and comparative genomic analysis. To identify conditional essential genes by Tn-seq, we used media with different nutrient composition. Although many conditional gene essentialities were affected by the presence of relevant nutrient sources, we also found that the essentiality of genes in a subset of metabolic pathways was unaffected by metabolites. Comparative genomic analysis revealed that not all essential genes identified by Tn-seq were fully conserved within the M. tuberculosis complex including some existing anti-tubercular drug target genes. In addition, we utilized an available M. tuberculosis genome-scale metabolic model, iSM810, to predict M. tuberculosis gene essentiality in silico. Comparing the sets of essential genes experimentally identified by Tn-seq to those predicted in silico reveals the capabilities and limitations of gene essentiality predictions highlighting the complexity of M. tuberculosis essential metabolic functions. This study provides a promising platform to study essential cellular functions in M. tuberculosis. Author SummaryMycobacterium tuberculosis causes 10 million cases of tuberculosis (TB) each year resulting in over one million deaths. TB therapy is challenging because it requires a minimum of six months of treatment with multiple drugs. Protracted treatment times and the emergent spread of drug resistant TB necessitate the identification of novel targets for drug discovery to curb this global health threat. Essential functions, defined as those indispensable for growth and/or survival, are potential targets for new antimicrobial drugs. In this study, we aimed to define gene essentialities of M. tuberculosis on a genome-wide scale to comprehensively identify potential targets for drug discovery. We utilized a combination of experimental (functional genomics) and in silico approaches (comparative genomics and flux balance analysis). Our functional genomics approach identified sets of genes whose essentiality was affected by nutrient availability. Comparative genomics revealed that not all essential genes were fully conserved within the M. tuberculosis complex. Comparing sets of essential genes identified by functional genomics to those predicted by flux balance analysis highlighted gaps in current knowledge regarding M. tuberculosis metabolic capabilities. Thus, our study identifies numerous potential anti-tubercular drug targets and provides a comprehensive picture of the complexity of M. tuberculosis essential cellular functions.

microbiology↗

bioRxiv2018

A shared limiting resource leads to competitive exclusion in a cross-feeding system

Species interactions and coexistence are often highly dependent upon environmental conditions. This is especially true for cross-feeding bacteria that rely on one another for essential nutrients. The addition of a cross-fed nutrient to the environment can release one species from its dependence on another, thereby altering the species interaction and potentially affecting coexistence. Using invasion-from-rare experiments with cross-feeding bacteria, genome-scale metabolic modeling, and classical ecological models, we explored the potential for coexistence when one cross-feeding mutualist becomes independent. We show that whether nutrient addition shifts an interaction from mutualism to commensalism or parasitism depends on whether the limiting nutrient can be metabolized by only one species or by both species. Furthermore, we show that coexistence is only lost when the interaction becomes parasitism, and the obligate species has a slower maximum growth rate. Surprisingly, models suggest that rates of cross-fed nutrient production have a negligible effect. These results contribute to an understanding of how resource changes, whether intentional or not, will manipulate interactions and coexistence in microbial communities.

ecology↗