Pyrophilous (fire-loving) microbes
The frequency of large, high severity wild fires is increasing in the western US and in regions around the world due to long-term fire suppression strategies and climate change. These fires have direct, negative effects on soil carbon stocks through combustion, but they have indirect and potentially positive effects on soil carbon stocks through the production of pyrogenic organic matter (PyOM) that has a long residence time and constitutes a major pool of C in fire-prone ecosystems. Soil microbes are likely to be involved with the degradation of all of these compounds, yet little is currently known about the organisms or metabolic processes involved. In a DOE-funded project with Tom Bruns (UC Berkeley), Matt Traxler (UC Berkeley), and Igor Grigoriev (JGI), we are dissecting the effects of microbes on post-fire soil carbon dynamics by using a systems biology approach that couples small experimental “pyrocosms”, highly controlled production of 13C-labeled pyrolyzed substrates, genomics, transcriptomics, stable isotope techniques, and mass spectrometry to address the following objectives:
- Develop improved genomic and other -omic resources for the dominant microbes of fire-affected soils
- Determine the temporal response of soil microbes to fire and to PyOM additions
- Characterize the temporal patterns of degradation of different sub-fractions of PyOM
- Determine how dominant post-fire soil microbes affect the fate of PyOM
- Assess the interaction between N availability and PyOM mineralization by post-fire microbial communities and individual pyrophilous microbes
- Define the network of microbial interactions that facilitate PyOM breakdown over time, and the key genes involved
Our team’s work on this project includes isolating and characterizing bacteria that can use PyOM as their sole C source and tracing PyOM through soil microbial communities using stable isotope flux tracing and stable isotope probing of DNA (DNA-SIP). Some of the fun things we are building in order to carry out this research include a13C labeling chamber to grow labelled biomass, which will be pyrolyzed in a custom-built “charcoalator” that allows for highly-controlled production of PyOM, and an automated custom gas-sampling “multiplexer” connected to a cavity ringdown spectrometer, through which we can measure the efflux and isotopic composition of microbially-respired CO2. Team members on this project include Dr. Tim Berry, Nayela Zeba, and some of our undergrads.
Fire effects on soil microbial communities
Boreal forest soils are among the richest stocks of terrestrial carbon (C) in the world, primarily as a result of their low temperatures and slow decomposition rates. The fate of these large C stocks in the face of climate change is an area of critical concern, particularly when considered in the context of predictions of increasing wildfire. Although soil microbes are the core drivers of the soil organic C cycle, the effects of wildfire on boreal soil microbial communities remain poorly characterized.
The 2014 fires in the Northwest Territories were exceptional: they were the largest recorded burn in a single fire season, with some fires burning unusually intensely, leaving essentially no living vegetation, and others resulting in “fire refugia” of unburned or only lightly burned areas. We are collaborating with researchers from the Canadian Forest Service and the University of Alberta, who, in addition to linking remote-sensing data to a comprehensive on-the-ground site characterization and measurements of burn severity, will also collect an unprecedented set of soil samples from these fires. Characterizing the microbiome of these soils will offer us a profound level of insight into the effects of fire on soil microbial communities, and leverage an extensive field campaign to bridge the scale from satellites to microbes. Jamie Woolet and Dana Johnson are key lab members working on this project.
Microbial ecology of microhabitats
Despite the billions of microbial cells found in a gram of soil, soil microbes are estimated to inhabit only 1% of total soil volume, and are unevenly distributed, forming colonies and biofilms. Thus, the biogeochemical processes driven by these microbes are often limited to relatively small volumes of soil, or “hotspots”. The different soil conditions that develop in each microhabitat support different microbial communities – e.g., rhizosphere vs. bulk soil; microaggregate interiors vs. whole aggregates; micropores vs. macropores; detritosphere (decomposing organic matter) vs. bulk soil. However, the genetic and ecological mechanisms driving these differences in microbial community composition across different microhabitats are not well understood, nor are their implications for soil microbial diversity and functioning. In addition, the prevalence of each microhabitat changes over time and under different soil management strategies, so the relative importance of these mechanisms would also be expected to change over time. Understanding how soil microhabitats structure soil microbial communities will allow us to better predict how changes to the environment will affect the soil microbial community and its biogeochemical functioning. Jaimie West and Michael Braus are working in this area of the lab. Jaimie’s work focuses on determining the mechanisms that generate and maintain microbial diversity in the soil, and Mike’s work focuses on pH from a soil microbe’s perspective.
Addressing misclassification in the microbiome: Propagating uncertainty in microbial community composition
This project is a collaboration with Dr. Amy Willis and Dr. Karl Broman, and is being led by Dr. Jacob Price. Misclassification of taxa is one of the biggest barriers in sequence-based microbiome studies. This project addresses that barrier by developing algorithms that account for uncertainties in taxa classification and incorporating them into microbiome analysis pipelines. A common first step in microbiome analyses is to obtain a census of the microbes that are there, typically done for bacteria using the universal biomarker 16S rRNA gene. Current approaches to utilizing these data classify a given sequence by matching it to its closest taxonomic neighbour in the dataset or in a reference database, but this approach falls short of accurate classification due to the fact that the sequence is often not a 100 percent match with any given sequence in the dataset or reference database.
This project will overcome this limitation by defining membership as a probability, rather than relying on the all or none approach that is currently employed. By using this method, concerns over incorrect assessment of diversity and abundance can be overcome. Fixing this problem has implications for research in human health, agriculture and understanding of the environment, as well as any systems where there are discrete data with some degree of misclassification.