Modeling of Syntrophic Communities

Learning objectives 

Upon successful completion of this assignment, you will be able to: 

1) Describe metabolic modeling and syntrophic communities and how metabolic modeling can be used to further our understanding of syntrophic communities 

2) Identify gaps and limitations in existing metabolic modeling of syntrophic communities 3) Examine future areas of research at the intersection of metabolic modeling and syntrophic communities 

Syntrophic communities are responsible for a number of known waste conversion processes, but the community metabolisms are not yet well understood. Metabolic modeling of syntrophic communities can generate testable hypotheses, inform us of unknown interactions, and provide an easily manipulable setting in which to explore syntrophy. Elucidation of syntrophic microbial communities will improve downstream applications of these microbial communities in settings such as anaerobic digestion bioreactors, which are responsible for converting biosolid waste to energy. 

Introduction 

Metabolic modeling 

Metabolic modeling is a mathematical representation of the metabolism of an organism or organisms based on the genomes of the organism(s). The genomes are sequenced and then annotated using sequences that are known to correspond to a metabolic function as well as predicting metabolic functions based on other known sequences. Reactions that are missing are gapfilled, a flux balance analysis is then run, these two steps may be iterated a few times to find the optimal model. The result is a metabolic model that can be used to research organism or community-level metabolic analyses and flux simulations (Zhang 2016). 

Syntrophic communities 

Syntrophic communities are groups of microbial communities that perform obligately mutualistic metabolism (Morris 2013). These communities are able to survive and even thrive even when single organisms cannot (Libby 2019). Syntrophy was first explained as microbial cross-feeding and is sometimes used interchangeably with symbiosis or mutualism, though these terms are not synonymous (Morris 2013). Schink described syntrophic communities more precisely as: “cooperations in which both partners depend on each other to perform the metabolic activity observed and in which the mutual dependence cannot be overcome by simply adding a cosubstrate or any type of nutrient” (Schink 1997). Though this description specifies only two community members, syntrophic communities can be made up of multiple different taxa. These types of communities are relatively common, but aren’t yet well understood (Libby 2019). 

Metabolic modeling of syntrophic communities can help us understand these interactions better and provide a method to develop hypotheses for future testing in wet lab settings. Compared to wet lab work, computer generated metabolic modeling is less resource intensive, including money and time. Researchers have the ability to explore many different ideas before deciding on a path to pursue further with wet lab experiments. 

Example study 

A 2007 study by Stolyar et al. was the first to produce and analyze a multispecies stoichiometric metabolic model of Desulfovibrio vulgaris and Methanococcus maripaludis (Stolyar 2007). Together these two microbes convert lactate to acetate and then to methane for energy. D. vulgaris is a sulfur-reducing anaerobic bacteria while M. maripaludis is a hydrogenotrophic methanogenic archaeon (Stolyar 2007). Previous related efforts have characterized syntrophic associations in terms of bulk system properties but not as integrated metabolic networks as this study did (Stolyar 2007). This model was the first of its kind to model an association of two syntrophic species (Stolyar 2007). This model has the potential to predict key features of community dynamics (Stolyar 2007) and provides a way to study future interactions. It lays the framework to further develop syntrophic metabolic models and justifies the usefulness of such a model. 

Limitations and future work 

Limitations 

To date, the number of functional and useful metabolic models of syntrophic communities is limited. Those that exist are of relatively simple interactions, such as the one outlined in Example study. More complex microbial communities, such as those responsible for anaerobic digestion have yet to be modeled in a full metabolic model. This limitation is partially due to the 

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Metabolic modeling of syntrophic communities Savanna Smith 

sparse annotation of known genomes and is further exacerbated by a lack of full genomic sequencing of all known microorganisms involved in anaerobic digestion. Despite these limitations, there have been recent noteworthy developments such as a flux balance analysis of the anaerobic digestion microbiome (Basile 2020). 

Future work 

To develop more robust and additional metabolic models of syntrophic communities, first it is necessary to sequence the full genomes of as many microorganisms present in anaerobic digestion and other known syntrophic microbiomes as possible. This step has numerous challenges to overcome including isolation of each organism and the cost of full genome sequencing. Beyond initial full genome sequencing, it is also necessary to better annotate the genes that are part of these genomes. It is essential to know what roles the genes present in anaerobic digestion microbes fulfill. To overcome these obstacles, engineers and microbiologists must work in collaboration to bridge the gaps and eventually develop more robust and additional syntrophic metabolic models. 

References 

Basile, Arianna, et al. "Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis." Metabolic Engineering 62 (2020): 138-149. 

Libby, Eric, et al. "Syntrophy emerges spontaneously in complex metabolic systems." PLoS computational biology 15.7 (2019): e1007169. 

Morris, Brandon E.L., Ruth Henneberger, Harald Huber, Christine Moissl-Eichinger, Microbial syntrophy: interaction for the common good, FEMS Microbiology Reviews, Volume 37, Issue 3, May 2013, Pages 384–406, https://doi.org/10.1111/1574-6976.12019 

Stolyar, Sergey, et al. "Metabolic modeling of a mutualistic microbial community." Molecular systems biology 3.1 (2007): 92. 

Zhang, Cheng, and Qiang Hua. "Applications of genome-scale metabolic models in biotechnology and systems medicine." Frontiers in physiology 6 (2016): 413. 

Resources 

Powerpoint presentation: Shared_Metabolic modeling of syntrophic communities.pptx PDF of slides: Metabolic modeling of syntrophic communities_pdf.pdf 

Video recording of presentation: 

https://drive.google.com/file/d/1pQJ9gfujcS7tXnF73pZJWuTX4c-BoUjW/view?usp=sharing 

Questions (3-5) 

1. Why are syntrophic communities important? 

a. Syntrophic communities are very important. They are capable of substrate conversions that singly-feeding microorganisms are incapable of. While we don’t yet know the full capabilities of all syntrophic communities, it’s possible that we can create synthetic syntrophic communities capable of breaking down all sorts of environmental contaminants that single microorganisms cannot. 

2. List gaps and limitations in existing metabolic models of syntrophic communities. 3

Metabolic modeling of syntrophic communities Savanna Smith 

a. Limited number of full-sequenced genomes of microorganisms present in syntrophic relationships available 

b. Genome annotation of existing sequenced genomes varies in completeness and accuracy 

c. Small amount overall of existing and functional metabolic models of syntrophic communities upon which future work can be developed from or learned from 3. If you had the unlimited abilities to develop a robust, functional, and fully descriptive metabolic model of any known syntrophic interaction which would you pick and why? a. If I had the abilities and knowledge to develop a robust, functional, and fully descriptive metabolic model of any known syntrophic interaction I could choose the anaerobic digestion microbiome. Anaerobic digestion relies on a syntrophic group of microorganisms to convert organic waste into methane gas. However, the details of the metabolic interactions are not well understood and development of a fully descriptive model would improve bioreactor design and operation which would ultimately improve microbial community function and performance.