Mitigating Climate Change through Microbiome Engineering
New Research by Michael Silverstein, Bioinformatics PhD Candidate
and Hariri Institute Graduate Student Fellow
The past decade has been the hottest in human history since record-keeping began 140 years ago. Burning fossil fuels, deforestation, and increased livestock farming are all major contributors to increasing the greenhouse effect, and thus global warming. To help combat climate change, Hariri Institute researchers at Boston University are turning towards microbiome engineering as a potential solution.
Microbes – invisible to the naked eye and found nearly everywhere such as in soil, oceans, air and more – impact the environment as they are capable of consuming and producing greenhouse gases. Microbiome engineering may help mitigate climate change by enabling changes to the composition or activity of microbial populations to achieve specific goals.
“The motivation for my research is to investigate the potential of environmental microbiome engineering,” says Michael Silverstein, a Hariri Institute Fellow and Bioinformatics PhD candidate, advised by Professors Daniel Segrè (Biology, Bioinformatics, and Biomedical Engineering), and Jennifer Bhatnagar (Biology). “The idea is to introduce a new microbial community to a natural environment where it will hopefully establish. By introducing a microbial community with climate change mitigating features, such as the ability to efficiently sequester greenhouse gases, the activity of the whole environment could then shift.”
“Our research is increasingly motivated by the urgency of helping figure out how to limit global warming and create a sustainable bioeconomy, and I think that microbes, with their powerful and diverse metabolic capabilities, can play an important role in addressing this challenge,” says Segrè, an affiliated faculty of the Institute. “It is crucial to understand whether we can engineer microbial communities to perform specific tasks. I am excited that Michael’s work provides a contribution in this direction, while at the same time suggesting that there may still be new quantitative principles of biological organization waiting to be discovered and understood.”
In earlier work, Silverstein explored the potential and challenges of environmental microbiome engineering to mitigate climate change. His most recent paper, published in Nature Ecology & Evolution, charts a roadmap as to which environments may be most suitable to microbiome engineering, which requires the successful integration of a new community into an existing environment.
The paper, titled “Metabolic complexity drives divergence in microbial communities,” outlines how community dynamics differ under simple versus complex conditions. The study analyzed multiple communities with a range of different environments. This difference led to the communities either converging or diverging, based on the complexity of conditions in the environment.
In the study, the researchers grew these communities for 33 days and analyzed the demographics of the communities through 16S sequencing, establishing demographic data for the communities. This allowed them to identify the diversity in each microbial community and measure the differences between the various communities. The sequencing allowed the researchers to see how and when the communities converge or diverge.
“What we found is that as metabolic complexity increases, the microbial communities diverge more from each other,”says Silverstein. “This result opens up a new hypothesis which is that environments with more complex metabolites might be better suited to microbiome engineering. This is because in those environments, multiple types of communities can exist stably and these communities may perform different functions.
Computational Ecological Modeling
To understand what is responsible for this “divergence-complexity effect”, the researchers used a computational, ecological model. The model had two entities: organisms and resources. Organisms, which are consumers, are defined by what they eat, and resources are defined as what a consumed resource becomes, or the transformation of the resource as it is consumed. The model was able to reproduce the research team’s results under certain assumptions, however, under other assumptions, it did not reproduce the results.
“We performed simulations using the structures that exist in nature, where groups of organisms prefer groups of resources and complex resources successively break down into smaller ones,” says Silverstein. “It turns out that to reproduce our effect, the structure in the resource transformation has to involve big resources becoming smaller and then becoming smaller. That says something about how the structure of these biochemical transformations of these resources in nature is inherently related to the ecology of these microbial communities.”
Future Implications
The future implications for this finding involve analyzing the activities and functions that the microbiomes are performing. Understanding the demographic breakdown of an environment doesn’t tell you the functions that are being performed there, just like understanding the population breakdown in a city doesn’t tell you the number of plumbers or mechanics working there. The next step is understanding task divergence in microbial communities and whether communities that differ in demographics in the same environment also perform different functions. In the context of environmental microbiome engineering, microbial communities could be established in natural environments and have the ability to change the regular activity in these environments to one that better mitigates climate change.
Michael Silverstein is a 2022 Hariri Institute for Computing Graduate Student Fellow and in the fourth year of his PhD program. His research focus is on the role of soil microbial interactions in regulating climate change. Learn more about his work here.
Access his most recent paper here: Silverstein, M.R., Bhatnagar, J.M. & Segrè, D. Metabolic complexity drives divergence in microbial communities. Nat Ecol Evol 8, 1493–1504 (2024). https://doi.org/10.1038/s41559-024-02440-6
Microbial communities may diverge in environments with increasing metabolic complexity.
a–d, Hypothesis of microbial community divergence in theoretical simple (b) and complex (c) metabolic conditions. Microbial communities A, B and C are initially composed of different compositions of the same three microbial species (a; blue, red and yellow). Over time, communities grown on a simple substrate (b) converge, while these same communities grown on a complex substrate (c) diverge. The lines in b and c show the trajectory of each community from the initial composition (circles) to final compositions (squares). d, Quantification of divergence (distance between communities in the same condition) at the final timepoint (trajectories arriving at squares shown in dashed circles above each bar for each condition) for hypothetical scenarios in a–c. e–g, Divergence observed in two independent experimental studies, one where microbial communities were sourced from soils or leaves and grown on glucose (e; a relatively simple metabolic environment from Goldford et al., N = 11 communities) and another where communities were sourced from pitcher plants and grown on acidified cricket media (f; a more complex metabolic environment from Bittleston et al., N = 10 communities). Each coloured line in e and f represents the trajectory of a community’s composition over time in separately computed MDS projections. The circles indicate the initial community composition, and the squares indicate the final community composition. g, The divergence for each metabolic environment, calculated as the pairwise distances between all communities within a given condition at each timepoint. Each point is the mean pairwise distance within condition at each timepoint, and shading represents the 95% confidence interval over all pairwise distances within each environment at each timepoint.