Project Details





Project Description:

There is increasing interest in assessing the GI microbiome of cats and dogs in a variety of clinical conditions. Fecal collection from dogs is relatively easy and can be readily performed at home or upon arrival at veterinary clinics. Cats, however, tend to be more difficult to collect stool samples from and are generally reluctant to defecate in veterinary clincis, especially on an outpatient basis.

The objective of this project is to establish best method of collecting and storing fecal samples at home for metabolomic and metagenomic assessment in catsStudies in people have shown that there is wide variety in metagenomic and metabolic outcomes when fecal collection/storage and sampling is altered; particularly when measuring volatile fatty acids such as butyrate.  Fecal collection methods for metabolomics/metagenomics have not been validated in cats.  


Skills Required:

Cats (n=5) will be staff or student owned, older than 1 year and healthy with no known antimicrobial use in the previous 6 months. As each cat will be serving as its own control, we will not need to screen them for underlying or sub-clinical disease. Fecal samples will be collected at home and brought into MSU within 4 hours of collection.

Following the storage protocols, total DNA will be extracted from samples by the student, DNA will be quantified and then submitted through the Genomics core at MSU for quality assessment (TapeStation). Standard 16S rRNA gene sequencing and targeted metabolomics (short chain fatty acid) will be performed through the Genomics and Metabolomics core respectively. Results will then be processed by the bioinformatics core and comparison between the different collection and storage methods made.

Outcome: This project will serve as a benchmark for future cat microbiome studies, allowing for accurate and repeatable research to be performed. The student will become familiar with benchtop DNA extraction, become familiar with concepts of the microbiome and metabolome, but will not be expected to interpret the data completely.


Project Supervision: