This week’s BEACON Researchers at Work blog post is by University of Idaho postdoc Mitch Day.
Many labs in BEACON and beyond study microbial communities. There are many ways to approach the problem, but the first is always deciding what fundamental questions you will focus on. Without trying to sound too glib, the main question I focus on is “What is a community”? You might see this as a simplistic question because the obvious dictionary.com answer is “a collection of species found living together in the same locale.”
When we zoom in to the microbial scale, this definition falls apart because the every-day concept of a species doesn’t apply down there. We commonly think of a species as organisms that can reproduce together sexually. Bacteria don’t have sex, but they are promiscuous. They often exchange small parts of their genomes with each other, sometimes between very unrelated organisms. Microbiologists use the small differences in a single gene needed by all living things (called “16S“) as an imperfect proxy for species. It is easy these days to extract DNA from a microbial community, such as a soil sample, and sequence all the different versions of the 16S gene. We can then sort the different versions of 16S we find into bins based on their similarity to each other. Daniel Beck, my colleague in the lab headed by James Foster, has just published a BioConductor package called OTUbase that makes this kind of data analysis much easier.
This approach has been very useful but it is misleading to think of these operational taxonomic units (OTU) as being the same as species we describe in vertebrate animals. Why? The main reason is the recent discovery of the pangenome. In every bacterial “species” studied so far, environmental isolates that have the exact same 16S sequence can actually have very different genomes. There is a core of genes that is common among all isolates, but each isolate also has a set of genes that are unique to that isolate. Since only a few genes are needed to turn a harmless or helpful bacterium into a deadly pathogen, it shouldn’t be hard to convince you that there are serious limitations to studying microbial communities using only the 16S approach.
So how would we answer our own question as asked at the beginning? For us, a microbial community is a single discrete unit that produces an effect on the environment. You can even call us “species agnostics” because we are exploring ways to study communities that don’t depend on the concept of species at all. How do you do this? One approach is to treat a 16S data set as if it were a collection of genes in a single organism. In genetics, epistasis is the term used to describe the effect of the interaction among a large number of different genes.
Daniel Beck is exploring different ways to detect epistasis among different OTU. He’s even throwing evolution at the problem. He is using a large data set gathered from the microbial communities found in patients suffering from bacterial vaginosis (BV) and from those without BV. This data includes the OTU composition of each individual community and some information about the health and behavior of the host human. Daniel will test classical analyses and new evolutionary algorithms to see which is better at predicting whether a given patient has BV from just the 16S composition.
Our “agnosticism” can be taken even further. My own research treats microbial communities as experimental units. What does that mean? My main experimental approach right now is determining the impact of artificial ecosystem selection on microbial communities. Artificial ecosystem selection is simply an extension of the concept of artificial group selection to include groups composed of a vast number of different populations. Even though biologists still debate the role of group selection in nature, it most definitely works in the lab.
I am performing experiments on a number of different communities to answer a few different questions that follow from the idea of a community as a discrete unit. The first is obviously “Do microbial communities respond to artificial selection in the lab?” Some early results show that they can. According to the essential definition of evolution, they certainly should be able to. Of course, we are scientists so we will do the experiments and find out for sure.
We have finished an artificial ecosystem selection experiment using a natural consortium of bacteria and yeasts as our community. This community is called the Ginger-beer “plant” because it has been used to make fizzy, low-alcohol beverages historically. It takes the form of durable gelatinous particles that float around in the sugary medium. These granules vary in size up to about the size of a small grape. The traditional way of creating the beverage uses a kitchen sieve to catch the particles and separate them from the foamy drink. In essence, the Ginger-beer “plant” has already been artificially selected by humans for a long time. It has been selected for particles that are big enough to catch in the sieve. The losers go down the drain to their separate fates.
We extended this long history of selection by using a set of sieves of increasingly larger mesh-size instead of a homely kitchen strainer. Bryanna Larraea performed most of the hard work in this experiment. She grew 15 jars of Ginger-beer “plant” for 9 weeks. Every week she would sieve each jar separately, weigh out the particles of each size-class and then select larger or smaller particles to found the next “generation.” Our experimental design has introduced some tricky statistical questions, but fortunately, we are collaborating with a statistician, Wade Copeland, who actually enjoys hanging out around biologists. Wade has already made a huge contribution to the study.
The rationale behind this experiment is the same as any artificial selection experiment. Artificial selection for animal husbandry formed the basis of modern genetics that came long before we knew anything much about DNA. The patterns of inheritance gave strong clues as to the genetic basis of physical traits. If we can show a response to selection on a community trait, we can analyze our archived samples to find patterns in either the 16S gene variations or in the metagenomic sequences. We think our ‘top-down’ study of natural, captive microbial communities is a necessary complement to other lab approaches that build artificial communities in the lab from the bottom up.
For more information on Mitch’s work, you can contact him at mday at uidaho dot edu.