2022
DOI: 10.3390/vetsci9110635
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The Kitty Microbiome Project: Defining the Healthy Fecal “Core Microbiome” in Pet Domestic Cats

Abstract: Here, we present a taxonomically defined fecal microbiome dataset for healthy domestic cats (Felis catus) fed a range of commercial diets. We used this healthy reference dataset to explore how age, diet, and living environment correlate with fecal microbiome composition. Thirty core bacterial genera were identified. Prevotella, Bacteroides, Collinsella, Blautia, and Megasphaera were the most abundant, and Bacteroides, Blautia, Lachnoclostridium, Sutterella, and Ruminococcus gnavus were the most prevalent. Whil… Show more

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Cited by 9 publications
(17 citation statements)
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“…Using data from Ganz et al 2022 [3] (Table S2), we compared a reference set of 166 healthy cats for comparison with FMT recipients with chronic digestive conditions. These cats had no known health conditions, clinical signs or diagnoses, had a body condition score between 3 and 6 (mean 5.14 ± 0.47) or a calculated BMI of less than or equal to 50, and had taken no antibiotics within the previous 12 months according to information reported by their owners.…”
Section: Preparation Of Fmt Capsulesmentioning
confidence: 99%
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“…Using data from Ganz et al 2022 [3] (Table S2), we compared a reference set of 166 healthy cats for comparison with FMT recipients with chronic digestive conditions. These cats had no known health conditions, clinical signs or diagnoses, had a body condition score between 3 and 6 (mean 5.14 ± 0.47) or a calculated BMI of less than or equal to 50, and had taken no antibiotics within the previous 12 months according to information reported by their owners.…”
Section: Preparation Of Fmt Capsulesmentioning
confidence: 99%
“…Raw 16S rRNA gene amplicon sequences were obtained from fecal samples of recipient animals (68 individuals, 136 fecal samples), donor animals (10 individuals, 20 fecal samples), and healthy reference animals (166 individuals, 166 fecal samples [3]). Sequences were trimmed, quality-filtered and dereplicated using the Divisive Amplicon Denoising Algorithm (DADA2 v1.14.1) pipeline in R (v3.6.2) [34,35].…”
Section: S Rrna Sequence Processingmentioning
confidence: 99%
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