About the lab

The Weimer lab uses microbial systems biology to study host/microbe interactions. Our group seeks to understand how bacteria interact with host cells to alter function and persistence. We use epithelial cells to stem cells to examine the molecular signals that are exchanged between the host and the microbe. MetaRNA seq is a common tool that we use to examine bacterial populations that persist in food, host tissues, and the environment that play a role in zoonotic diseases.

Featured projects (7)

Project
The consortium is sequencing 100,000 bacterial pathogens. The goal is to produce and examine microbial evolution, determine pathogenicity genome features, and create information for reference databases used in bacterial identification.
Project
Use growth independent methods to detect bacteria directly from animals, food, and the environment.
Project
Determine hazards that are transmitted via the food supply chain that begins at the farm and ends in consumption. The focus is to establish genomic techniques that enable culture independent methods via metaRNAseq to find pathogenic bacteria.

Featured research (8)

Less invasive rumen sampling methods such as oro-esophageal probes became widely popular to explore the rumen microbiome and metabolome. However, it remains unclear if such methods represent the rumen contents from fluid and particulate fractions well. Herein, we characterized the microbiome and metabolome in rumen content collected by an oro-esophageal probe and fluid, particulate, and the combined fluid-particulate fractions collected by rumen fistula in ten multiparous Holstein dairy cows. The 16S rRNA gene was amplified and sequenced using the Illumina MiSeq platform. Untargeted metabolome was characterized using gas chromatography of a time-of-flight mass spectrometer. Although the pH of oro-esophageal samples was greater than those of fluid, fluid-particulate, and particulate ones, we found no difference in alpha and beta-diversity of their microbiomes. Bacteroidetes, Firmicutes, and Proteobacteria were consistently the top three most abundant phyla representing ~90% of all detected phyla across all samples. The overall metabolome PLS-DA of oro-esophageal samples was similar to the fluid-particulate samples but differed from fluid and particulate. Enrichment analysis pathways revealed few differences between oro-esophageal and fluid-particulate samples, such as the synthesis of unsaturated fatty acids. The results of the current study suggest that oro-esophageal sampling can be a proxy to screen the rumen microbiome with the 16S platform and overall fluid-particulate metabolome for a single-time and diet context. Nonetheless, studies focusing specifically on fluid and particulate metabolomes and specific metabolic pathways should carefully consider the sampling method used.
In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas , and Citrobacter . We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species’ viability from total RNA sequencing.
Sierra Mixe maize is a landrace variety from Oaxaca, Mexico, that utilizes nitrogen derived from the atmosphere via an undefined nitrogen fixation mechanism. The diazotrophic microbiota associated with the plant's mucilaginous aerial root exudate composed of complex carbohydrates was previously identified and characterized by our group where we found 23 lactococci capable of biological nitrogen fixation (BNF) without containing any of the proposed essential genes for this trait (nifHDKENB). To determine the genes in Lactococcus associated with this phenotype, we selected 70 lactococci from the dairy industry that are not known to be diazotrophic to conduct a comparative population genomic analysis. This showed that the diazotrophic lactococcal genomes were distinctly different from the dairy isolates. Examining the pangenome followed by genome-wide association study and machine learning identified genes with the functions needed for BNF in the maize isolates that were absent from the dairy isolates. Many of the putative genes received an 'unknown' annotation, which led to the domain analysis of the 135 homologs. This revealed genes with molecular functions needed for BNF, including mucilage carbohydrate catabolism, glycan-mediated host adhesion, iron/siderophore utilization, and oxidation/reduction control. This is the first report of this pathway in this organism to underpin BNF. Consequently, we proposed a model needed for BNF in lactococci that plausibly accounts for BNF in the absence of the nif operon in this organism.
As extreme droughts become more frequent, dissecting the responses of root-associated microbiomes to drying-wetting events is essential to understand their influence on plant performance. Here, we show that rhizosphere and endosphere communities associated with drought-stressed rice plants display compartment-specific recovery trends. Rhizosphere microorganisms were mostly affected during the stress period, whereas endosphere microorganisms remained altered even after irrigation was resumed. The duration of drought stress determined the stability of these changes, with more prolonged droughts leading to decreased microbiome resilience. Drought stress was also linked to a permanent delay in the temporal development of root microbiomes, mainly driven by a disruption of late colonization dynamics. Furthermore, a root-growth-promoting Streptomyces became the most abundant community member in the endosphere during drought and early recovery. Collectively, these results reveal that severe drought results in enduring impacts on root-associated microbiomes that could potentially reshape the recovery response of rice plants.
In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides , Clostridium , Lactococcus , Aeromonas , and Citrobacter . We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species' viability from total RNA sequencing.

Lab head

Bart C Weimer
Department
  • Department of Population Health and Reproduction (VM)
About Bart C Weimer
  • Dr. Bart C. Weimer is Professor and Chair in the Department of Population Health & Reproduction UC Davis. He holds degrees from the University of Arizona (BS) and Utah State University (PhD) with post-doctoral training at the University of Melbourne. His group studies food, health and the microbiome using bacterial population genomics, systems biology, population genomics and machine learning. He leads the 100K Pathogen Genome Sequencing Project enabling metagenomic solutions.

Members (3)

Carol Huang
  • University of California, Davis
dj Bandoy
  • University of the Philippines
Ashleigh Flores
  • University of California, Davis
Bihua Huang
Bihua Huang
  • Not confirmed yet
peter sebastian
peter sebastian
  • Not confirmed yet
cory schlesener
cory schlesener
  • Not confirmed yet
angel avalos
angel avalos
  • Not confirmed yet
Katie Lee
Katie Lee
  • Not confirmed yet
Claire Shaw
Claire Shaw
  • Not confirmed yet

Alumni (14)

James H Kaufman
  • Altos Labs
Prerak T Desai
  • Janssen Research & Development, LLC
Allison M Weis
  • University of Utah
Nguyet Kong
  • University of California, Davis