We are interested in developing and utilizing new experimental and computational methods in order to understand microbial communities.

Broadly, we are interested in the following areas of research:

  • developing and using experimental and bioinformatics tools that better characterize microbial diversity from high-throughput sequencing data
  • understanding the genomic determinants of ecosystem-scale processes within microbial communities
  • understanding genomic diversity, molecular biology, and evolutionary history of microbes from shotgun metagenomic sequencing

We work with a variety of world-class collaborators on specific experimental systems of interest. Some of these projects are listed below. Please also read our publications for more information.

Improved bioinformatics methods for inference of microbial protein function, metabolisms, and traits

Advances in DNA sequencing and associated genome-enabled high-throughput technologies have made the assembly of microbial genomes and partial genomes recovered from the environment routine. In theory, computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should allow for the accurate prediction and modeling of key microbial traits, organismal interactions, and ecosystem processes that drive biogeochemical cycles. In practice, however, a lack of scalable computational annotation tools means these outcomes are rarely achieved without expert manual curation that scales extremely poorly. We are developing scalable annotation tools to better predict protein function, and better understand and model microbial community metabolism. These tools are made available in the The Department of Energy Systems Biology Knowledgebase, Kbase

Collaborators: Farnoush Banaei-Kashani, University of Colorado Denver; Kelly Wrighton, Colorado State University; Chris Henry, Argonne National Lab

The role of the microbiome in modulating effects of environmental pollutant exposure

Many toxic compounds enter the environment from wastewater, legacy waste sites, and other sources. These compounds have the potential to alter the gut microbiome in fish, other wildlife, companion animals, and humans. The nature of these alterations and the potential interactions with host health are poorly understood. Using high-throughput DNA and RNA sequencing, we are attempting to characterize pollutant-mediated disruption in the fish gut microbiome, and to integrate this data with other complex omics and chemical data.

Collaborators: Alan Vajda, CU Denver; Chris Martyniuk, University of Florida; David Bertolatus, Adams State University

Understanding bioremediation by complex microbial communities

We are working to establish a mechanistic understanding of how microbial communities remediate groundwater pollution at an EPA superfund site in the Denver metropolitan area. By using metagenomic and metatranscriptomic sequencing, we are elucidating the organisms, pathways, and enzymes responsible for degradation of 1,4-dioxane at a pump-and-treat bioremediation facility. Our knowledge advances in part because of the hard work of thousands of first-year undergraduate General Biology students at CU Denver, who participate in high-throughput sequencing data generation via a Course-based Undergraduate Research Experience (CURE).

Collaborators: Timberley Roane, CU Denver

Determinants of microbial contributions to methane emissions in freshwater wetlands

Of the three most potent greenhouse gases, methane emissions are the most difficult to predict. Despite being overall carbon sinks, wetlands represent the largest single source of atmospheric methane. Yet, relatively little is known about the microbial taxa and pathways responsible for methane flux in freshwater wetlands. Together with the Wrighton lab at Colorado State University and many other great collaborators, we are generating a high-resolution census of freshwater wetland microbial communities, reconstructing the major metabolisms of microbial communities in freshwater wetlands sediments, and aim to understand and ultimately predict how these communities and metabolisms respond to changing climate, hydrology, and variations in available carbon. This project is producing a large amount of marker gene, metagenomic, and transcriptomic sequencing across all domains of life. We are working on methods to simultaneously integrate disparate data types from Bacteria, Fungi, Viruses, and especially the understudied Archaea, in order to understand and predict ecosystem-scale processes in freshwater wetlands.

Collaborators: Kelly Wrighton, Colorado State University, and many others!