Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community

MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields

Motivation

The human microbiome, comprises complex associations and communication networks among microbial communities, which are crucial for maintaining health. The construction of microbial networks is vital for elucidating these associations. However, existing microbial networks inference methods cannot solve the issues of zero-inflation and non-linear associations. Therefore, necessitating novel methods to improve the accuracy of microbial networks inference.

Results

In this study, we introduce the Microbial Network based on Mutual Information and Markov Random Fields (MicroNet-MIMRF) as a novel approach for inferring microbial networks. Abundance data of microbes are modeled through the zero-inflated Poisson distribution, and the discrete matrix is estimated for further calculation. Markov random fields based on mutual information are used to construct accurate microbial networks. MicroNet-MIMRF excels at estimating pairwise associations between microbes, effectively addressing zero-inflation and non-linear associations in microbial abundance data. It outperforms commonly used techniques in simulation experiments, achieving area under the curve values exceeding 0.75 for all parameters. A case study on inflammatory bowel disease data further demonstrates the method's ability to identify insightful associations. Conclusively, MicroNet-MIMRF is a powerful tool for microbial network inference that handles the biases caused by zero-inflation and overestimation of associations.

Availability and implementation

The MicroNet-MIMRF is provided at https://github.com/Fionabiostats/MicroNet-MIMRF.

Seeking patterns of antibiotic resistance in ATLAS, an open, raw MIC database with patient metadata

Antibiotic resistance represents a growing medical concern where raw, clinical datasets are under-exploited as a means to track the scale of the problem. We therefore sought patterns of antibiotic resistance in the Antimicrobial Testing Leadership and Surveillance (ATLAS) database. ATLAS holds 6.5M minimal inhibitory concentrations (MICs) for 3,919 pathogen-antibiotic pairs isolated from 633k patients in 70 countries between 2004 and 2017. We show most pairs form coherent, although not stationary, timeseries whose frequencies of resistance are higher than other databases, although we identified no systematic bias towards including more resistant strains in ATLAS. We sought data anomalies whereby MICs could shift for methodological and not clinical or microbiological reasons and found artefacts in over 100 pathogen-antibiotic pairs. Using an information-optimal clustering methodology to classify pathogens into low and high antibiotic susceptibilities, we used ATLAS to predict changes in resistance. Dynamics of the latter exhibit complex patterns with MIC increases, and some decreases, whereby subpopulations' MICs can diverge. We also identify pathogens at risk of developing clinical resistance in the near future.

Would that it were so simple: Interactions between multiple traits undermine classical single-trait-based predictions of microbial community function and evolution

Understanding how microbial traits affect the evolution and functioning of microbial communities is fundamental for improving the management of harmful microorganisms, while promoting those that are beneficial. Decades of evolutionary ecology research has focused on examining microbial cooperation, diversity, productivity and virulence but with one crucial limitation. The traits under consideration, such as public good production and resistance to antibiotics or predation, are often assumed to act in isolation. Yet, in reality, multiple traits frequently interact, which can lead to unexpected and undesired outcomes for the health of macroorganisms and ecosystem functioning. This is because many predictions generated in a single-trait context aimed at promoting diversity, reducing virulence or controlling antibiotic resistance can fail for systems where multiple traits interact. Here, we provide a much needed discussion and synthesis of the most recent research to reveal the widespread and diverse nature of multi-trait interactions and their consequences for predicting and controlling microbial community dynamics. Importantly, we argue that synthetic microbial communities and multi-trait mathematical models are powerful tools for managing the beneficial and detrimental impacts of microbial communities, such that past mistakes, like those made regarding the stewardship of antimicrobials, are not repeated.

Multi-stable bacterial communities exhibit extreme sensitivity to initial conditions

Microbial communities can have dramatically different compositions even among similar environments. This might be due to the existence of multiple alternative stable states, yet there exists little experimental evidence supporting this possibility. Here, we gathered a large collection of absolute population abundances capturing population dynamics in one- to four-strain communities of soil bacteria with a complex life cycle in a feast-or-famine environment. This dataset led to several observations: (i) some pairwise competitions resulted in bistability with a separatrix near a 1:1 initial ratio across a range of population densities; (ii) bistability propagated to multi-stability in multispecies communities; and (iii) replicate microbial communities reached different stable states when starting close to initial conditions separating basins of attraction, indicating finite-sized regions where the dynamics are unpredictable. The generalized Lotka-Volterra equations qualitatively captured most competition outcomes but were unable to quantitatively recapitulate the observed dynamics. This was partly due to complex and diverse growth dynamics in monocultures that ranged from Allee effects to nonmonotonic behaviors. Overall, our results highlight that multi-stability might be generic in multispecies communities and, combined with ecological noise, can lead to unpredictable community assembly, even in simple environments.

Predicting microbial growth dynamics in response to nutrient availability

Developing mathematical models to accurately predict microbial growth dynamics remains a key challenge in ecology, evolution, biotechnology, and public health. To reproduce and grow, microbes need to take up essential nutrients from the environment, and mathematical models classically assume that the nutrient uptake rate is a saturating function of the nutrient concentration. In nature, microbes experience different levels of nutrient availability at all environmental scales, yet parameters shaping the nutrient uptake function are commonly estimated for a single initial nutrient concentration. This hampers the models from accurately capturing microbial dynamics when the environmental conditions change. To address this problem, we conduct growth experiments for a range of micro-organisms, including human fungal pathogens, baker's yeast, and common coliform bacteria, and uncover the following patterns. We observed that the maximal nutrient uptake rate and biomass yield were both decreasing functions of initial nutrient concentration. While a functional form for the relationship between biomass yield and initial nutrient concentration has been previously derived from first metabolic principles, here we also derive the form of the relationship between maximal nutrient uptake rate and initial nutrient concentration. Incorporating these two functions into a model of microbial growth allows for variable growth parameters and enables us to substantially improve predictions for microbial dynamics in a range of initial nutrient concentrations, compared to keeping growth parameters fixed.

To give or not to give antibiotics is not the only question

In a 1945 Nobel Lecture, Sir Alexander Fleming warned against the overuse of antibiotics, particularly in response to public pressure. In the subsequent decades, evidence has shown that bacteria can become resistant to almost any available molecule. One key question is how the emergence and dissemination of resistant bacteria or resistance genes can be delayed. Although some clinicians remain sceptical, in this Personal View, we argue that the prescription of fewer antibiotics and shorter treatment duration is just as effective as longer regimens that remain the current guideline. Additionally, we discuss the fact that shorter antibiotic treatments exert less selective pressure on microorganisms, preventing the development of resistance. By contrast, longer treatments associated with a strong selective pressure favour the emergence of resistant clones within commensal organisms. We also emphasise that more studies are needed to identify the optimal duration of antibiotic therapy for common infections, which is important for making changes to the current guidelines, and to identify clinical biomarkers to guide antibiotic treatment in both hospital and ambulatory settings.

Repeatable ecological dynamics govern the response of experimental communities to antibiotic pulse perturbation

In an era of pervasive anthropogenic ecological disturbances, there is a pressing need to understand the factors that constitute community response and resilience. A detailed understanding of disturbance response needs to go beyond associations and incorporate features of disturbances, species traits, rapid evolution and dispersal. Multispecies microbial communities that experience antibiotic perturbation represent a key system with important medical dimensions. However, previous microbiome studies on this theme have relied on high-throughput sequencing data from uncultured species without the ability to explicitly account for the role of species traits and immigration. Here, we serially passage a 34-species defined bacterial community through different levels of pulse antibiotic disturbance, manipulating the presence or absence of species immigration. To understand the ecological community response measured using amplicon sequencing, we combine initial trait data measured for each species separately and metagenome sequencing data revealing adaptive mutations during the experiment. We found that the ecological community response was highly repeatable within the experimental treatments, which could be attributed in part to key species traits (antibiotic susceptibility and growth rate). Increasing antibiotic levels were also coupled with an increasing probability of species extinction, making species immigration critical for community resilience. Moreover, we detected signals of antibiotic-resistance evolution occurring within species at the same time scale, leaving evolutionary changes in communities despite recovery at the species compositional level. Together, these observations reveal a disturbance response that presents as classic species sorting, but is nevertheless accompanied by rapid within-species evolution.

Predicting community dynamics of antibiotic-sensitive and -resistant species in fluctuating environments

Microbes occupy almost every niche within and on their human hosts. Whether colonizing the gut, mouth or bloodstream, microorganisms face temporal fluctuations in resources and stressors within their niche but we still know little of how environmental fluctuations mediate certain microbial phenotypes, notably antimicrobial-resistant ones. For instance, do rapid or slow fluctuations in nutrient and antimicrobial concentrations select for, or against, resistance? We tackle this question using an ecological approach by studying the dynamics of a synthetic and pathogenic microbial community containing two species, one sensitive and the other resistant to an antibiotic drug where the community is exposed to different rates of environmental fluctuation. We provide mathematical models, supported by experimental data, to demonstrate that simple community outcomes, such as competitive exclusion, can shift to coexistence and ecosystem bistability as fluctuation rates vary. Theory gives mechanistic insight into how these dynamical regimes are related. Importantly, our approach highlights a fundamental difference between resistance in single-species populations, the context in which it is usually assayed, and that in communities. While fast environmental changes are known to select against resistance in single-species populations, here we show that they can promote the resistant species in mixed-species communities. Our theoretical observations are verified empirically using a two-species Candida community.

Antithetic population response to antibiotics in a polybacterial community

Much is known about the effects of antibiotics on isolated bacterial species, but their influence on polybacterial communities is less understood. Here, we study the joint response of a mixed community of nonresistant Bacillus subtilis and Escherichia coli bacteria to moderate concentrations of the β-lactam antibiotic ampicillin. We show that when the two organisms coexist, their population response to the antibiotic is opposite to that in isolation: Whereas in monoculture B. subtilis is tolerant and E. coli is sensitive to ampicillin, in coculture it is E. coli who can proliferate in the presence of the antibiotic, while B. subtilis cannot. This antithetic behavior is predicted by a mathematical model constrained only by the responses of the two species in isolation. Our results thus show that the collective response of mixed bacterial ecosystems to antibiotics can run counter to what single-species potency studies tell us about their efficacy.

Physiologically Relevant Alternative Carbon Sources Modulate Biofilm Formation, Cell Wall Architecture, and the Stress and Antifungal Resistance of Candida glabrata

Flexibility in carbon metabolism is pivotal for the survival and propagation of many human fungal pathogens within host niches. Indeed, flexible carbon assimilation enhances pathogenicity and affects the immunogenicity of Candida albicans. Over the last decade, Candida glabrata has emerged as one of the most common and problematic causes of invasive candidiasis. Despite this, the links between carbon metabolism, fitness, and pathogenicity in C. glabrata are largely unexplored. Therefore, this study has investigated the impact of alternative carbon metabolism on the fitness and pathogenic attributes of C. glabrata. We confirm our previous observation that growth on carbon sources other than glucose, namely acetate, lactate, ethanol, or oleate, attenuates both the planktonic and biofilm growth of C. glabrata, but that biofilms are not significantly affected by growth on glycerol. We extend this by showing that C. glabrata cells grown on these alternative carbon sources undergo cell wall remodeling, which reduces the thickness of their β-glucan and chitin inner layer while increasing their outer mannan layer. Furthermore, alternative carbon sources modulated the oxidative stress resistance of C. glabrata as well as the resistance of C. glabrata to an antifungal drug. In short, key fitness and pathogenic attributes of C. glabrata are shown to be dependent on carbon source. This reaffirms the perspective that the nature of the carbon sources available within specific host niches is crucial for C. glabrata pathogenicity during infection.

Glyoxylate cycle gene ICL1 is essential for the metabolic flexibility and virulence of Candida glabrata

The human fungal pathogen Candida glabrata appears to utilise unique stealth, evasion and persistence strategies in subverting the onslaught of host immune response during systemic infection. However, macrophages actively deprive the intracellular fungal pathogen of glucose, and therefore alternative carbon sources probably support the growth and survival of engulfed C. glabrata. The present study aimed to investigate the role of the glyoxylate cycle gene ICL1 in alternative carbon utilisation and its importance for the virulence of C. glabrata. The data showed that disruption of ICL1 rendered C. glabrata unable to utilise acetate, ethanol or oleic acid. In addition, C. glabrata icl1∆ cells displayed significantly reduced biofilm growth in the presence of several alternative carbon sources. It was also found that ICL1 is crucial for the survival of C. glabrata in response to macrophage engulfment. Disruption of ICL1 also conferred a severe attenuation in the virulence of C. glabrata in the mouse model of invasive candidiasis. In conclusion, a functional glyoxylate cycle is essential for C. glabrata to utilise certain alternative carbon sources in vitro and to display full virulence in vivo. This reinforces the view that antifungal drugs that target fungal Icl1 have potential for future therapeutic intervention.