Joshua Garcia is a third year PhD student in the School of Integrative Plant Science at Cornell University studying rhizosphere microbiomes. In this post, he talks about his recent Perspective paper “Can dynamic network modelling be used to identify adaptive microbiomes?”
About the research
What’s your paper about?
Our paper is a perspectives piece in which we discuss the application of dynamic network modelling to microbiome studies. We were interested in finding more meaningful ways to analyze microbiome data that links to multicellular host or ecosystem traits. This linkage is important, given the influence microbiomes can have on host traits, such as a plant’s ability to tolerate drought or a human’s ability to fight off infections. While the connections between a host and its associated microbiome have been extensively studied in recent years, we still lack an understanding of the microbial community behaviors that influence development of these host traits.
As an example, plants rely heavily on microorganisms in the soil to cycle nutrients, such as nitrogen and phosphorus. Microorganisms do this by producing extracellular enzymes that are released into the soil environment and help release nutrients for plant uptake. However, the production of these enzymes is energetically costly for an individual microbial cell, and in fact, relies on the presence of other enzyme producing microorganisms in the area (i.e., a quorum sensing activity). Classic methods to study community composition, such as 16S rRNA gene sequencing, may give us information about different bacterial taxa in soil at a given level of extracellular enzymes detected, but they do not offer any information about the microbial group dynamics that determine enzyme production levels. For example, there may be specific bacterial taxa associated with cheating behavior or cooperation in nitrogen-limited soils, but typical ordination methods will not reveal these dynamics. Instead, we need tools that allow us to see changes in community interactions over time that are associated with increased nitrogen mineralization. In our paper, we discuss these types of scenarios, along with some background on network models and the limitations and challenges of these methods.
How did you come up with the idea for this paper?
The idea for this paper came from lab discussions about the need for deeper analysis of microbiome data. For a while, we have been utilizing methods, such as 16S rRNA gene sequencing, which is used to compare bacterial community composition between samples. While the technique is useful for our studies, it only yields information about “who is there” and not so much about things like “what are they doing together” or “what are they doing without each other,” and how these interactions change over time under altered environmental factors. We realized that this lack of discussion on community dynamics in the microbiome science field was a problem, given that group and community dynamics have been well studied in ecology featuring insects and mammals as influencers of many important ecosystem traits.
To get a better picture of the microbiome, we have been looking into applying tools such as networks to microbiome studies. A network is a visual representation of the interactions between members of a complex system. Networks originated in mathematics and computer science and since then have been adapted for use in other fields. For example, networks have been utilized in molecular biology to study molecular processes within an organism and in ecology to study large ecological communities. In recent years, microbial ecologists have adapted networks to their studies to obtain snapshots of microbial community interactions and structures in environmental samples. Such snapshots are referred to as static networks.
While static networks are definitely useful in developing a deeper understanding of microbial community interactions and structures, a major pitfall is that they do not capture changes in the microbiome dynamics over time, which may be important in studying host associated microbiomes. Thus, we proposed utilizing dynamic network modelling as a way to not only understand microbial community interactions and structures, but also to comprehend how community dynamics may change over time.
Dynamic network modelling is a technique involving the creation of functions to generate a predictive fluid model. Dynamic models have been used in ecological studies to understand the progression of population dynamics in response to certain variables, such as the abundance of other species. A classic example of a dynamic model is Lotka-Volterra, which has been used to predict prey abundance in response to predator abundance. In our paper, we discuss the potential to apply dynamic modelling to the creation of networks, which would give us a more fluid representation of microbiome group dynamics and interactions. In studying microbiome adaptation over time, such models can be useful in understanding the forces that shape microbiome composition and function.
What is the relevance of perspective papers like this in science?
Although less common, we believe perspective papers are of great importance in science because they are an opportunity to present novel, and sometimes “out there,” ideas and methodologies that may be worth pursuing. The discussion of such ideas is important in viewing science from different perspectives and potentially making important discoveries. This could sometimes be harder to do in more traditional research and review papers, given their typical guidelines. We believe perspective papers are a great way to push scientific conversations further.
About the author
How did you get into ecology?
I initially wanted to become a veterinarian when I started my undergraduate studies, but realized it was not a good fit for me. I still had an interest in life sciences though, and decided to explore what sort of work I could do as a researcher. I love being outside and being among living things like plants and animals, so I decided to look into research opportunities in environmental science. After working in a soil lab, a forest ecology lab, and an agroecology lab, I realized my passion lay in ecological research and especially in agricultural settings.
What are you currently working on?
I am working on many different research projects that will all come together as my PhD dissertation. In one project, I am studying multilevel selection in plant rhizospheres and investigating if we can select for robust plant growth promoting microbiomes through artificial selection, which is like breeding for microbiomes. In another project, I am studying how rhizosphere microbiomes alter tomato developmental processes using transcriptomics, and in another, I am studying the links between aboveground and belowground biodiversity in tomato cropping systems.