Mathematical modelling of microbiomes

Workshop: 14 - 16 September 2022
Deadline for application: 12 July (23:59 CEST)


Most multi-cellular organisms do not live in isolation, but cohabit with a set of micro-organisms in a nested ecosystem called the microbiome. Host-associated microbiomes can be essential to host health, and attempts have been made to manipulate them for therapeutic purposes. Yet, we still have little understanding of the ecological and evolutionary processes involved in microbiomes dynamics. Mathematical modeling is a powerful tool to address the biological questions that arise:
  • What determines microbiome composition?
  • How do microbiomes evolve with their hosts?
  • How is microbiome diversity maintained?
  • How do microbiomes help the host adapt to its environment?
  • What are the physical and immunological constraints imposed by the host in various anatomical compartments?

In this workshop, we aim at discussing advances in microbiome modeling, identifying open questions and specificities, as well as framing these topics in the larger eco-evolutionary theory. The emphasis will be on the technical questions that emerge from these works, in particular how to identify relevant assumptions in the building of models. To this effect, we will invite the presenters to adopt a chalk talk format. We encourage the application of researchers at any career stage, working with mathematical tools on any microbiome system. We will strive to create an inclusive and interactive environment with a restricted number of participants (~40), reserving some time to discuss the pressing open questions of this research field. 

Organizers: Florence Bansept, Michael Sieber, and Román Zapién-Campos (from MPI-Plön).


Jonas Cremer (Stanford)

Isabel Gordo (Instituto Gulbenkian de Ciência)

Jacopo Grilli (The Abdus Salam International Centre for Theoretical Physics)

Christoph Kaleta (Christian-Albrechts-University Kiel)

Claude Loverdo (Sorbonne Universite / CNRS)

Simon van Vliet (University of Basel)

Nicole Vega (Emory University)

    • 09:00 09:45
      Welcome (Check-In) 45m
    • 09:45 10:00
      Introduction 15m
    • 10:00 11:00
      Keynote: The tempo and mode of evolution of a strain in the mammalian gut microbiota
      Convener: Isabel Gordo (Instituto Gulbenkian de Ciência )
      • 10:00
        The tempo and mode of evolution of a strain in the mammalian gut microbiota 1h

        Bacteria live in highly diverse ecosystems inside the intestines of many organisms. How and at what pace they evolve in that ecosystem is not yet well understood. Here we address these questions using the power of mouse models and the wealth of functional knowledge on a human gut commensal, Escherichia coli. We demonstrate that the colonization success of a new invader E. coli strain depends on the microbiota diversity. We then study the evolutionary changes occurring in the invader strain over 7000 generations and map its adaptive evolution by genomic analysis combined with functional and fitness assays. Our main finding is that following colonization, two modes of evolution occur: one in which diversifying selection leads to the emergence and long-term coexistence of ecotypes and another in which directional selection propels selective sweeps. The directional selection mode is characterised by continuous selective sweeps, while the ecotype formation mode is governed by negative-frequency dependent selection. We identify metabolic functional adaptations as the main drivers of the evolutionary dynamics in both modes, while adaptation to phage integration is specific to the directional selection mode. Our results contribute to a better understanding of bacterial evolution in species rich ecosystems, such as human guts.

        Speaker: Isabel Gordo (Instituto Gulbenkian de Ciência)
    • 11:00 11:30
      Stabilization of microbiomes by stochastic and responsive phenotypic switching 30m

      Authors: Pierre A. Haas, Maria A. Gutierrez, Nuno M. Oliveira, and Raymond E. Goldstein

      In complex microbial communities such as microbiomes, clonal bacteria switch between different phenotypes. This switching can be stochastic, but switching in response to other species is beginning to be appreciated as a feature of microbial populations because of the importance of competitive interactions in microbiomes [1] and attack responses such as the recently reported "suicidal chemotaxis" of Pseudomonas [2]. In this talk, I will analyze the surprising ecological consequences of phenotypic variation for microbiome stability and diversity theoretically: even though such a subpopulation structure increases the effective number of species and might therefore be expected to be destabilizing, I will show that stochastic switching to a rare phenotype is stabilizing on average [3]. Extending this statistical analysis to responsive phenotypic switching, I will emphasize the importance of non-steady-state attractors for coexistence [4]. Finally, I will address the mechanisms by which responsive phenotypic switching can stabilize coexistence in a minimal two-species model which reveals how responsive switching can stabilize coexistence even when stochastic switching on its own has no effect on stability [4]. [1] K. Z. Coyte, J. Schluter, and K. R. Foster, Science 350, 663 (2015) [2] N. M. Oliveira, J. H. R. Wheeler, C. Deroy, S. C. Booth, E. J. Walsh, W. M. Durham, and K. R. Foster, biorXiv:2021.12.21.473623v3 (sub judice, 2022) [3] PAH, N. M. Oliveira, and R. E. Goldstein, Phys. Rev. Research (Rapid Communications) 2, 022036(R) (2020) [4] PAH, M. A. Gutierrez, N. M. Oliviera, and R. E. Goldstein, arXiv:2112.06256v2 (sub judice, 2022)

      Speaker: Pierre Haas (Max Planck Institutes for the Physics of Complex Systems & of Molecular Cell Biology and Genetics )
    • 11:30 12:00
      Plasmidome multilayer networks reveal potential pathways of gene transmission across microbiomes 30m

      Authors: Julie Teresa Shapiro, Alvah Zorea, Aya Brown Kav, Itzik Mizrahi, Shai Pilosof

      The microbiome is predominantly treated as the collection of microbes limited to a given space. However, all microbiomes are open communities that include, beyond microbes, mobile genetic elements. Of these, plasmids are major agents of microbial evolution, horizontally spreading genes within and between microbes. A hallmark example is genes of antimicrobial resistance - a major threat to public health, many of which are transmitted to humans from cattle such as cows. The cow rumen microbiome hosts a diverse community of plasmids (plasmidome). One way to identify pathways of gene transmission, inspired by disease ecology, is using plasmidome sequence similarity networks. However, studies of plasmid sequence similarity networks have only used published sequences of plasmids from disparate systems, rendering this approach irrelevant. To investigate potential transmission between cow hosts and genetic exchange between plasmids, we constructed a multilayer network based on pairwise genetic similarity composed of 1344 plasmids (nodes) from 21 cow plasmidomes originating in a single population of dairy cows (layers). The network was dominated by interlayer connectivity, suggesting that gene exchange is more likely between plasmids from different cows than within a cow. By analyzing network modularity compared to shuffled networks we further detect non-random major pathways of transmission. Specifically, we find clusters of cows sharing many transmission pathways -- a signature of super-spreading at the cow level. Plasmid functions influenced network structure: plasmids containing mobility genes were more connected. In addition, plasmids with the same AMR genes, though rare in our data set, formed independent clusters. Finally, via analysis of link weights we show that gene exchange between plasmids in major transmission pathways is dominated by plasmid dispersal rather than HGT. Overall, our results provide insights into the mechanisms by which genes can spread across animal hosts, shaping microbiome diversity.

      Speaker: Shai Pilosof (Ben Gurion University of the Negev )
    • 12:00 13:00
      Lunch 1h
    • 13:00 13:45
      Discussion: Discussion and Walk
    • 13:45 14:45
      Keynote: Talk Jacopo Grilli
      Convener: Jacopo Grilli (The Abdus Salam International Centre for Theoretical Physics )
      • 13:45
        What is typical in microbial communities? True statistical patterns and wrong macroecological models in microbiome dynamics 1h

        Microbial communities are highly dimensional, with many species and many variable environmental factors. Macroecology, which studies communities as statistical ensembles, is a promising way to connect these complex data to mechanistic models. In this talk, I will discuss a minimal set of macroecological patterns that characterize the statistical properties of species abundance fluctuations across communities and over time. A mathematical model based on environmental stochasticity --- the Stochastic Logistic model (SLM) --- quantitatively predicts these three macroecological laws, as well as non-stationary properties of community dynamics. I will then use the SLM as a lens to unveil non-trivial statistical properties of microbiomes dynamics, with particular emphasis on stability and reproducibility. In particular, I will show that the variability of community composition is characterized by (at least) two timescales. The understanding of these two timescales allows to characterize, and quantitatively reproduce, the variability of composition across hosts. I will conclude with a discussion of how to include in the
        model species' inter-dependencies.

        Speaker: Jacopo Grilli (The Abdus Salam International Centre for Theoretical Physics)
    • 14:45 15:15
      Stochastic Lotka-Volterra model reproduce macroecological patterns 30m

      Authors: J Camacho-Mateu, Aniello Lampo, Matteo Sireci, Miguel A Muñoz, Jose A Cuesta

      Microbial communities are ubiquitous in the entire biosphere, from seawaters and soils to animals’ guts, and have a great impact in many biological processes, for instance those involved in human health. Herein, a crucial role is played by interactions among species. It has been shown, indeed, that these may underlie the critical features associated with some disorders, such as Crohn’s disease and other forms of inflammatory bowel syndrome, and actually many medical treatments work by acting on competition among bacteria. Accordingly, characterizing the interaction network of microbial communities, as well the resulting dynamical behavior, constitutes a challenging task. In the present work we propose a method relying on Monte Carlo Markov chain (MCMC) aimed to build the microbial interaction network that best fits the abundance correlation distribution of a real biome. This has the advantage of dealing with an object – the correlation distribution – that is usually much less noisy compared to abundance time series, on which are based many of the current inference techniques. Importantly, we analyze the dynamics induced by the obtained interaction network in the generalized Lotka-Volterra framework, and find that it reproduces the experimental laws detected in the previous literature – the Gamma distribution of abundance fluctuations over samples, the lognormal distribution of its mean value over species, and the linear relationship with the related variance – that fully characterize the macroecological behavior of microbial communities. Our MCMC method, supported by the agreement with experimental observations, allows to get insight about the main features of the microbial interactions. In particular, we look into their spectral properties and unveil the existence of outliers eigenvalues, suggesting the existence of main modes related to species which pivot the general dynamics.

      Speaker: José Manuel Camacho Mateu (Carlos III University of Madrid )
    • 15:15 15:45
      Coffee Break 30m
    • 15:45 16:15
      Microbial cooperation in public good games under environmental flows 30m

      Authors: João Valeriano, Ricardo Martínez-García

      Cooperation is a social behavior that, despite being easily encountered in nature, is usually hard to explain, as it frequently appears to be evolutionarily unstable. Microbial communities provide a very rich playground to study the evolution of cooperation, as one can model these behaviors through simple rules and compare theoretical predictions to experiments in controlled conditions. Microorganisms, for example, produce all sorts of public goods, substances that are released to the environment and provide benefits for all individuals with access to them. Public good production is energetically costly and can be exploited by individuals that do not share the production cost but still share their benefits. In this scenario, non-producers have a higher relative fitness than producers and will outgrow them, leading to the extinction of cooperative behavior. But data tells us this cannot be the end of the story and that such inevitable extinction of cooperation may be the result of an oversimplified set of model assumptions. Microbes, for example, are usually found in aqueous media in which flows can create spatial structures — even though possibly transient — that reduce the rate of interspecific interactions and might facilitate coexistence. Despite these potential effects, the environmental context has been largely neglected in models of social evolution. In this work, we study whether and how environmental flows can affect the maintenance of cooperation in a population of public good producers and non-producers. Individuals of both strains are advected by an environmental flow and interact with each other via a public good game, where cooperators produce molecules that provide benefits to its consumers. We conduct intensive numerical simulations of this advection-reaction-diffusion process with increasingly complex velocity fields. We consider differents flows, so that we can understand how the dynamics and the possible outcomes of the game change as the environmental mixing transitions from laminar to turbulent. Lastly, we try to connect the effect of flows on the ecological outcome on the public good game to the spatial structure of the population in the absence of flow.

      Speaker: João Pedro Valeriano Miranda (Institute for Theoretical Physics, State University of São Paulo, Brazil )
    • 16:15 16:45
      Investigating the eco-evolutionary tunnels for establishing two species cooperative communities 30m

      Authors: Seyfullah Kotil, Kalin Vetsigian

      Diversity is abundant among microbial communities. Understanding the assembly of diverse microbial communities is a significant challenge. One of the recent plausible explanations for the assembly involves eco-evolutionary tunnels, where species interact in the same timescale with the mutational rate. A common framework to understand such tunnels is done by agent-based simulations and analyzing the generated data. However, modelling the theoretical aspects by simple mathematical biology is lacking. Here, we present the modelling and the characterization of eco-evolutionary tunnels that gives rise to two-species evolutionary stable communities (ESC). We find that higher-order but common interactions are sufficient for eco-evolutionary tunnels. Biological interpretations of the models span from self-cooperation and division of labor to cross-feeding.

      Speaker: Seyfullah Kotil (Bahcesehir University )
    • 16:45 18:15
      Poster: Session 1
    • 18:15 19:15
      Discussion: Panel 1
    • 19:15 20:15
      Dinner 1h
    • 20:15 21:15
      General Discussion 1h
    • 09:00 10:00
      Keynote: Generating and Dealing With Microbiome Data: The Bad, The Weird, and The Ugly
      Convener: Nicole Vega (Emory University)
      • 09:00
        Generating and Dealing With Microbiome Data: The Bad, The Weird, and The Ugly 1h

        Using models to understand microbiomes requires good data, generated from experiments designed to capture relevant information and having structure suitable for model fitting, analysis capable of dealing with the idiosyncracies in these data, and models capable of recapitulating the sometimes-weird features that we actually observe. Using small host-microbiome models, we can generate data well-suited for illustrating some of these features and for demonstrating the errors that occur when there is a disconnect between assumptions in experiments, analysis, and models. In this talk, we will discuss the effects of "weirdness" (skewed, kurtotic, and/or bumpy underlying distributions of states) and "badness" (sampling errors, batch effects) on "ugliness" of model predictions (failure to recapitulate data features, particularly in the tails), with assistance from low-dimensional data sets of host-microbe association in Caenorhabditis elegans.

        Speaker: Nicole Vega (Emory University)
    • 10:00 10:30
      Amino acid auxotrophies are ubiquitous in the human gut microbiome 30m

      Authors: Svenja Busche, Danielle Harris, Konrad Aden, Silvio Waschina

      Auxotrophies are defined by the incapability of an organism to synthesize essential nutrients resulting in a dependence on the nutritional environment. Amino acids are vital nutrients for the human host and auxotrophic bacteria within the gut microbiome, which could result in competition for specific amino acids. However, the prevalence of bacterial auxotrophies and the impact on host physiology remains obscure. Here, we applied genome-scale metabolic modelling to predict amino acid auxotrophies in 3652 common human gut bacteria and evaluated statistically the influence of auxotrophies on the metabolic profile. Furthermore, the frequency of auxotrophies was estimated based on microbiome data from a large population cohort study and statistically tested for associations with health parameters and metabolome data. Among all proteinogenic amino acids, tryptophan auxotrophies were predicted to have the highest abundance in the human gut microbiome. Overall, auxotrophies for amino acids essential to the host are abundant in the gut microbiome. Branched-chain amino acid auxotrophic bacteria were more prone to produce lactate. Further, the data analysis revealed the distribution of auxotrophies as a major determinant of the stool metabolome in chronic inflammatory diseases. In short, the results show that auxotrophic bacteria are common in the human gut microbiome and indicate a potential influence on human health.

      Speaker: Svenja Busche (Institute of Human Nutrition and Food Science, Kiel University)
    • 10:30 11:00
      Coffee Break 30m
    • 11:00 12:00
      Keynote: The role of multilevel selection in host microbiome evolution
      Convener: Simon Van Vliet (SNSF Ambizione fellow Biozentrum | University of Basel Infection Biology )
      • 11:00
        The role of multilevel selection in host microbiome evolution 1h

        Most animals have a microbiome that affects their reproductive success. It is, therefore, important to understand how a host and its microbiome coevolve. An open question is to what extent host-microbiomes can evolve through selection acting at the host-level. I will present a quantitative framework based on multi-level selection theory that addresses this question. Our model shows that host-level selection can favor microbes that altruistically help their hosts, but only when stringent conditions are met. Host-level selection requires that microbiome composition is heritable, which in turn requires that vertical transmission is stronger than horizontal transmission. Moreover, the host generation time must be short compared with the timescale of the evolutionary dynamics of the microbes. Evolution of host-level traits could potentially help hosts to meet these conditions, for example by changing the balance between vertical and horizontal transmission or by changing the relative timescales of host and microbe level dynamics, thereby potentiating host-level selection. Using our framework, we can thus investigate under which conditions host-level selection can play a role in host-microbiome evolution.

        Speaker: Simon Van Vliet (SNSF Ambizione fellow Biozentrum | University of Basel Infection Biology)
    • 12:00 13:00
      Lunch 1h
    • 13:00 13:30
      Scale-dependent signatures of microbial co-occurrence revealed via multilayer network analysis 30m

      The composition of a microbial community (microbiome) affects its stability and function and is shaped by selective and neutral processes that operate at different scales. For instance, environmental filtering typically operates at non-local scales while interactions (e.g., competition) operate locally. One way to study community composition is via microbe co-occurrence networks. The underlying assumptions of all such studies are: (i) co-occurrence is a prerequisite, yet not evidence for interactions; (ii). Co-occurrence network structures contain observable patterns that may reveal the processes and factors that generated the network. Despite the many insights gained from co-occurrence network analysis, how such networks vary in space and at which spatial scales they are shaped is unclear. One reason is that controlling for environmental factors in natural communities is challenging. We used the rumen microbiome---a highly controlled environment in which we know the host genetics and diet. We worked with core microbes that inhibit cows at seven farms in Europe. We constructed a spatial multilayer network to represent, for the first time, multiple local co-occurrence networks as a single mathematical object. Each layer contained a co-occurrence network in a given farm. Via comparison to shuffled networks, we discover that, although microbes appear in all seven farms, there are strong, non-random signatures of local co-occurrence patterns. Specifically, co-occurrence is transitive (when A occurs with B and B with C, then A occurs with C), and microbes have a significant tendency to maintain phylogenetically-similar co-occurring partners across farms. In addition, the network was partitioned at two levels. At the top level, farms were separated into modules corresponding to cow diet and genetic breed, and at the second level, each farm was in its own cluster. In contrast, a monolayer description of the same system and results of shuffled networks show a single module that encompasses all farms. Our multilayer approach unravelled a strong effect of environmental filtering on co-occurrence patterns at non-local scales, followed by local effects within the same farm likely representing interactions. In addition, a multilayer approach is a valuable method for analyzing microbial networks with a strong potential to discover new patterns and processes.

      Speaker: Geut Galai (Ben-Gurion University )
    • 13:30 14:00
      Quantification of metabolic niche occupation over time in a Baltic Sea bacterial community using a diffusion map approach 30m

      Authors: Jana C. Massing, Ashkaan Fahimipour, Carina Bunse, Jarone Pinhassi, Thilo Gross

      Progress in molecular methods has enabled us to monitor bacterial community composition over time. Nevertheless, understanding community dynamics and its impact on ecosystem functioning is challenging due to the tremendous diversity. This highlights the need for conceptual frameworks to make sense of the time-series of diverse bacterial taxa, regarding their strategies and function. A key concept for such synthesis is the niche, the set of capabilities that enables a population to persist and that defines its impacts on the surroundings. Here we use diffusion maps to re-construct the metabolic niche space of a bacterial community from a long-term time-series, the Linnaeus Microbial Observatory (LMO) in the Baltic Sea. Using manifold learning, we propose a framework to organize genomic information into potentially occupied metabolic niches over time. The results reveal a broad spectrum of metabolic strategies of the bacterial community. Their combined niche-space has a tree-like structure comprising clusters of taxa featuring localized traits and continuous branches. Time patterns of potentially occupied niches seem to be strongly driven by seasonality, a key feature in the Baltic Sea. We find some functional strategies clearly dominated by one bacterial group and others that are divided between bacterial groups depending on season. These results illustrate the power of the diffusion map approach to advance our understanding of community dynamics and ecosystem function.

      Speaker: Jana Massing (Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB))
    • 14:00 16:45
      Walk to Prinzeninsel 2h 45m
    • 16:45 17:45
      Discussion: Panel 2
    • 17:45 19:15
      Discussion: Discussion Groups
    • 19:15 20:15
      Dinner at Restaurant 1h
    • 20:15 21:15
      General Discussion 1h
    • 09:00 10:00
      Keynote: Talk Christop Kaleta
      Convener: Christoph Kaleta (CAU Kiel)
      • 09:00
        Constraint-based modeling of microbial communities 1h

        Constraint-based approaches are key concepts in modeling of metabolism. As key ingredient these approaches require the stoichiometric matrix of the metabolic network of an organism that can be readily derived from its genome. Using this stoichiometric matrix along with physiological constraints and an evolutionary objective, fluxes within such a network can be predicted using flux balance analysis. In the context of microbial community simulations, either stoichiometric matrices of individual microbial species in a community are combined in a community-level metabolic network or individual species’ microbial networks can be simulated individually in a common environment. In my talk I will outline the basic methodological principles underlying these modeling approaches covering the basics of constraint-based modeling and then moving over to different community modeling techniques. I will close by providing several examples how these modeling approaches can be used to answer questions about the ecology of microbial communities and the role of microbial communities in human health.

        Speaker: Christoph Kaleta (CAU Kiel)
    • 10:00 10:30
      Stepwise evolution of genome-scale metabolic networks in complex microbial communities 30m

      Authors: Ghjuvan Grimaud, Thomas Koffel, Elena Litchman, Christopher Klausmeier

      Genome-scale metabolic models (GEMs) are a powerful tool to understand and predict the metabolic status of bacterial species in different environmental conditions (Terzer et al., 2009). GEMs simulated using constraint-based models such as Flux Balance Analysis (FBA) (Orth et al., 2010a) or Dynamical Flux Balance Analysis (dFBA) (Mahadevan et al., 2002) make testable predictions and are used to answer different metabolic engineering, bioprocessing or ecological questions. Here we ask the question: can GEMs be used to model the evolution of metabolic networks in realistic ecological conditions, taking the cost and number of mutations into account? We propose a new approach combining FBA/dFBA and the adaptive dynamics theory (Geritz et al., 1998) to simulate the evolution of GEMs in an eco-evolutionary framework. We start with a GEM in a defined environment, called the resident. At each evolutionary time step, we create a mutant by adding one or several reaction(s) to the resident GEM corresponding to one or several mutation(s) chosen from a matrix containing all the known metabolic reactions in prokaryotes or a subset of this matrix, in line with Zomorrodi et al. (2014) and Szappanos et al. (2016). Then, we perform dFBA on the competing mutant/resident system. The strain(s) (resident or mutant) with the higher growth rate or winning the competition becomes the new resident(s). We then proceed to the next evolutionary time-step. By doing this evolutionary cycle iteratively, we can study the evolution of metabolic networks until an evolutionary equilibrium is reached, taking ecological feedbacks into account. Here we present the results of this method applied to Escherichia coli core model, a simplified version of the genome-scale metabolic model of E. coli iAF1260 with only 95 reactions and 72 metabolites (Orth et al., 2010b). We investigate whether the evolutionary outcome (e.g. number of strains and “functional” profile of the community at the evolutionary equilibrium) depends on the number of reactions added to the mutant at each evolutionary time step (i.e., number of mutations) or on the cost of adding new reactions. The results show that the optimal number of reactions added at each time step depends on the ecological conditions, thus revealing what conditions favor Horizontal Gene Transfer (when many mutations are optimal). When new reactions are costly, a trade-off between the number of reactions and their associated cost emerges, and the final complexity of the metabolic network ultimately depends on this trade-off and the environmental conditions. References Cited Geritz, S.a.H., Kisdi, E., Meszena, G., and Metz, J.a.J. (1998). Evolutionary singular strategies and the adaptive growth and branching of the evolutionary tree. Evolutionary Ecology 12, 35-57. Mahadevan, R., Edwards, J.S., and Doyle, F.J. (2002). Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophysical journal 83, 1331-1340. O’brien, E.J., Monk, J.M., and Palsson, B.O. (2015). Using genome-scale models to predict biological capabilities. Cell 161, 971-987. Orth, J.D., Thiele, I., and Palsson, B.Ø. (2010a). What is flux balance analysis? Nature biotechnology 28, 245-248. Orth, J. D., Fleming, R. M., & Palsson, B. O. (2010b). Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide. EcoSal plus. Szappanos, B., Fritzemeier, J., Csörgő, B., Lázár, V., Lu, X., Fekete, G., Bálint, B., Herczeg, R., Nagy, I., Notebaart, R.A., Lercher, M.J., Pál, C., and Papp, B. (2016). Adaptive evolution of complex innovations through stepwise metabolic niche expansion. 7, 11607. doi: 10.1038/ncomms11607. Terzer, M., Maynard, N.D., Covert, M.W., and Stelling, J. (2009). Genome‐scale metabolic networks. Wiley Interdisciplinary Reviews: Systems Biology and Medicine 1, 285-297. Zomorrodi, A.R., Islam, M.M., and Maranas, C.D. (2014). d-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities. ACS synthetic biology 3, 247-257.

      Speaker: Ghjuvan Grimaud (APC Microbiome/Teagasc)
    • 10:30 11:00
      Coffee Break 30m
    • 11:00 11:30
      Designing novel microbial therapies for atopic dermatitis using mechanistic modelling 30m

      Authors: Jamie Lee, Takuya Miyano, and Reiko J. Tanaka

      Atopic dermatitis (AD) is a chronic inflammatory skin disease of high socio-economic impact. An imbalanced skin microbiome is a common feature of AD patients, with Staphylococcus (S.) aureus colonising the skin. S. aureus damages the skin barrier, and its killing has been hypothesised as a promising treatment for AD. However, clinical trials that reduce S. aureus density on AD skin have shown conflicting efficacies to date. A potential explanation for this is that killing S. aureus may exacerbate the growth of commensal microbes, which may have detrimental effects. For example, S. epidermidis is beneficial and abundant in skin, but can cause barrier damage in AD patients when present at high densities. In this study, we apply a mathematical modelling approach to understand the mechanisms behind the conflicting efficacies observed in clinical trials. We develop a mechanistic model to test a hypothesis that S. aureus killing may lead to an increase in S. epidermidis density and result in treatment failure in some AD patients. Our mathematical model describes the interactions between S. aureus, S. epidermidis and barrier integrity. Our model simulations showed that a hypothetical microbial therapy that kills S. aureus alone within eight weeks fails to improve disease severity in some virtual AD patients due to the skin barrier damage caused by the increase in S. epidermidis density. A better treatment response was achieved if the hypothetical microbial therapy inhibits S. aureus virulence factor production in addition to killing S. aureus. These results suggest that a microbial therapy that strongly inhibits S. aureus virulence factor production and kills S. aureus weakly may be more effective than one that potently kills S. aureus. This study contributes to the design of promising S. aureus-targeted therapies by generating testable predictions.

      Speaker: Jamie Lee (Imperial College London)
    • 11:30 12:00
      Annotation-free discovery of functional microbiomes units 30m

      Authors: Xiaoyu Shan, Otto X. Cordero

      Recent studies have shown that microbiomes are composed of groups of functionally cohesive taxa, whose abundance is more stable and better associated with metabolic fluxes than that of any individual taxon. However, identifying these functional groups in a manner that is independent from error-prone functional gene annotations remains a major open problem. Here, we develop a novel approach that identifies groups of taxa that when combined result in strong and stable statistical associations with ecological variables, despite weak association at the individual taxon level. We leverage this approach on three distinct data sets: i) on data of replicate microcosm, our unsupervised algorithm reproduces experimentally validated functional groups that divide carbon utilizing communities into glycolytic and gluconeogenic groups; ii) when leveraged against the oceans microbiome data, our approach discover groups of aerobic and anaerobic ammonia oxidizers which collectively are strongly correlated with the abundance of nitrate in the water column, and finally, iii) we show that our framework enables detection of minimal assemblages strongly predictive of metabolites level in animal gut microbiomes. This work advances our understanding of structure-function relationship in complex microbiomes and provided a powerful computational tool to discover functional groups across different ecosystems.

      Speaker: Xiaoyu Shan (Massachusetts Institute of Technology )
    • 12:00 13:00
      Lunch 1h
    • 13:00 14:00
      Keynote: Talk Claude Loverdo
      Convener: Claude Loverdo (Sorbonne Universite / CNRS )
      • 13:00
        Bacteria in the digestive tract : stochastic models of population dynamics, for inferring colonization probability and immunity-induced clustering; and evolution in a flow. 1h

        Our work on gut microbiota centers on two main themes. The first topic is about quantitative inference from experimental data of the dynamics of the bacterial gut population. One tool to study the populations which dynamics cannot be directly observed is to use tagged neutral subpopulations. The distribution of the different tags in the population are measured at several points, and from the distribution changes, relevant parameters are inferred, using stochastic calculations, e.g. the computation of generating functions in branching processes. We will discuss results on the impact of the microbiota diversity on colonization resistance; and on the action mode of antibodies secreted in the gut lumen on bacteria. Second, to investigate the impact of the gut spatial structure on evolution, we calculate theoretically the fixation probability of neutral bacterial mutants within a minimal model that includes hydrodynamic flow and resulting gradients of food and bacterial concentrations. We find that this fixation probability is substantially increased compared to an equivalent well-mixed system, in the regime where the profiles of food and bacterial concentration are strongly spatially-dependent. Fixation probability then becomes independent of total population size. We show that our results can be rationalized by introducing an active population, which consists of those bacteria that are actively consuming food and dividing. The active population size yields an effective population size for neutral mutant fixation probability in the gut.

        Speaker: Claude Loverdo (Sorbonne Universite / CNRS)
    • 14:00 14:30
      Trade-offs between colonization and survival enable E. coli coexistence 30m

      Authors: Thibaut Morel-Journel, François Blanquart

      Despite the extensive literature on the epidemiology, pathogenesis and virulence of Escherichia coli, much less is known about the ecological interactions between non-pathogenic strains within the gut microbiome. In general, strains are considered in terms of `residency', i.e. the ability of certain strains, once established, to remain part of the microbiome for very long periods. However, recent studies indicate that the stability of microbiome composition observed at a high taxonomic level may not hold true when looking at the strain level, and that stability is at least partially dependent on genetic background. Furthermore, the ability of different strains to colonise new hosts has not been measured. Using data from longitudinal sampling of 8 healthy patients over periods ranging from 250 to 850 days, we assessed the residency time and colonisation ability of 9 different E. coli phylogroups. These analyses support the existence of a trade-off between the two traits. Phylogroups exhibit different strategies on a continuum ranging from frequent colonisation to long residency within the gut. With a model describing colonisation and extinction events in a host population, we identified the conditions under which coexistence is possible between different strains adopting different strategies. These results confirm the existence of complex interactions between the E. coli strains creating a dynamic microbiota composition, while maintaining diversity at the host population level.

      Speaker: Thibaut Morel-Journel (CIRB, CNRS )
    • 14:30 15:00
      Coffee Break 30m
    • 15:00 16:00
      Keynote: Grow with the flow - How gut motility and intestinal fluid turnover shape the accumulation of bacterial biomass along the human large intestine
      Convener: Jonas Cremer (Stanford University)
      • 15:00
        Grow with the flow - How gut motility and intestinal fluid turnover shape the accumulation of bacterial biomass along the human large intestine 1h

        The human gut harbors a highly dynamical microbiota shaped by the rapid turnover of bacterial biomass: While food intake by the host regularly supports fast growth of new bacteria, a substantial fraction of the bacterial population is also lost with every major bowel movement. The dynamics of the turnover depend strongly on the consumed diet and the speed with which bacteria grow. But the turnover is also tightly controlled by the hosts itself which regulates muscle contractions and the movement of intestinal content depending on digestion status and the local abundance of bacteria. We here present a mathematical modeling framework to investigate how these factors and their interplay shape the spatio-temporal densities of bacteria along the human large intestine. Our analysis shows how rapid changes in densities are strongly coupled to meal intake and largely shaped by the gastrocolic reflex which triggers the emptying of the proximal colon before large amounts of luminal fluids and nutrients enter from the small intestine. Because of these dynamics, bacterial densities within the proximal colon are often very low and bacteria present in the appendix can act as an important reservoir which shapes the bacterial population along the large intestine. Our results thus also highlight possible physiological roles of the appendix: the promotion of efficient bacterial growth and the stabilization of the microbiota composition which prevents pathogens or other bacteria entering from the small intestine from taking over the population.

        Speaker: Jonas Cremer (Stanford University)
    • 16:00 16:30
      Evolutionary modeling of microbiome community assembly in the context of human pre-cancer progression 30m

      Authors: Caitlin Guccione, Cameron Martino, Antonio Gonzalez, Rob Knight, Kit Curtius

      Microbes are abundant in human cancers and specific cancer types have unique microbiomes. Understanding the role of microbes in cancer development, progression, and metastasis could transform clinical cancer diagnostics and prognostics.(1) Although research on the cancer microbiome is expanding, previous studies mainly focus on static differences in microbial abundances. Alternatively, mathematical modeling of microbial evolution within human hosts can improve our understanding of the expected changes in microbiome composition over time and contextualize important dynamics throughout carcinogenesis. We first created a robust bioinformatics pipeline that extracts information on microbial taxa present within whole genome sequencing (WGS) data of human tissue samples. This pipeline can be applied to the large number of such datasets publicly available, enabling joint analysis of host genomic alterations and the surrounding microbial communities. To quantify microbial community assembly in the stages of progression to cancer, we considered esophageal adenocarcinoma (EAC) as a case study. EAC arises in patients with the metaplastic precursor Barrett’s esophagus (BE), but the natural history of genomic changes in both host and microbial DNA is not well understood.(2) There is a clinical need to understand these changes occurring in patients as they progress to EAC, and use this to tailor prevention strategies. After extracting microbial taxonomic information with our pipeline, we applied mathematical models of neutral evolution(3) to WGS data from patient samples representative of the sequential stages of progression to EAC: normal esophagus, esophageal tissue from patients with GERD (gastroesophageal reflux disease), BE and finally EAC. Intriguingly, we found evidence for neutrality across all stages of pre-cancer progression (goodness of fit to the neutral model R^2 > 0.85 for normal, GERD and BE) but not for EAC (R^2 = 0.47). We also analyzed a recent case-control WGS dataset of samples from BE patients who later progressed to EAC (‘progressors’) versus samples from BE patients who did not progress (‘non-progressors’).(4) We found that the occurrence vs. abundance of Helicobacter pylori (H. pylori), a bacteria associated with decreased risk of EAC, did not follow patterns predicted using the neutral model in non-progressor BE samples. To address the potential for non-neutral behavior, we then performed model simulations that included selection(5) by assuming a higher birth or lower death rate for H. pylori versus other neutrally evolving microbial species. Identifying relevant assumptions for such evolutionary models in human cancer progression is ongoing work and will yield important insights when applied to large cancer datasets in future studies. 1. G. D. Sepich-Poore, C. Guccione, L. Laplane, T. Pradeu, K. Curtius, R. Knight, Cancer’s second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process. Bioessays. 44, e2100252 (2022). 2. C. Guccione, R. Yadlapati, S. Shah, R. Knight, K. Curtius, Challenges in determining the role of microbiome evolution in Barrett’s esophagus and progression to esophageal adenocarcinoma. Microorganisms. 9, 2003 (2021). 3. M. Sieber, L. Pita, N. Weiland-Bräuer, P. Dirksen, J. Wang, B. Mortzfeld, S. Franzenburg, R. A. Schmitz, J. F. Baines, S. Fraune, U. Hentschel, H. Schulenburg, T. C. G. Bosch, A. Traulsen, Neutrality in the metaorganism. PLoS Biol. 17, e3000298 (2019). 4. T. G. Paulson, P. C. Galipeau, K. M. Oman, C. A. Sanchez, M. K. Kuhner, L. P. Smith, K. Hadi, M. Shah, K. Arora, J. Shelton, M. Johnson, A. Corvelo, C. C. Maley, X. Yao, R. Sanghvi, E. Venturini, A.-K. Emde, B. Hubert, M. Imielinski, N. Robine, B. J. Reid, X. Li, Somatic whole genome dynamics of precancer in Barrett’s esophagus reveals features associated with disease progression. Nat. Commun. 13, 2300 (2022). 5. R. Zapién-Campos, M. Sieber, A. Traulsen, The effect of microbial selection on the occurrence-abundance patterns of microbiomes. J. R. Soc. Interface. 19, 20210717 (2022).

      Speaker: Caitlin Guccione (University of California, San Diego )
    • 16:30 18:00
      Poster: Poster Session 2
    • 18:00 19:00
      Discussion: Panel 3
    • 19:00 19:15
      Conclusion 15m
    • 19:15 21:15
      Good-Bye Barbecue 2h