Metabarcoding Study of Fecal Bacterial and Eukaryotic Pathogens in Stray and Pet Cats in Seoul,South Korea,2022

Abstract

Objectives:

Zoonotic diseases pose substantial public health risks because of their potential transmission from animals to humans. As pets, cats can harbor these pathogens. The objective of this study was to describe bacterial and eukaryotic pathogens in the feces of stray and pet cats in Seoul, South Korea, using next-generation sequencing techniques.

Methods:

We collected 26 fresh fecal samples (17 from pet cats and 9 from stray cats) in Seoul’s Mapo-gu District in April and May 2022. Amplicon sequencing targeted the V4 region of the 16S rRNA gene for bacterial pathogens and the V9 region of the 18S rRNA gene for eukaryotic pathogens. We used QIIME 2 to conduct bioinformatic analysis, assessing alpha diversity with the Shannon Diversity Index and beta diversity with principal coordinates analysis and permutational multivariate analysis of variance. We used ALDEx2 and an analysis of the composition of microbiomes to analyze differential abundance and χ² tests to assess pathogen prevalence.

Results:

Across all 26 samples, Helicobacter spp (77%; n = 20) and Campylobacter spp (69%; n = 18) were the most prevalent bacterial pathogens. Escherichia-Shigella spp were more common in stray cats (56% [5 of 9]) than in pet cats (12% [2 of 17]) as were Brachyspira spp (stray cats, 44% [4 of 9]; pet cats, 0%). Of eukaryotic pathogens, Giardia spp (19% [5 of 26]) were most prevalent across both groups, with Pentatrichomonas spp significantly more common in stray cats (22% [2 of 9]) than in pet cats (0%).

Conclusions:

This study found distinct fecal microbial communities in stray versus pet cats, with a higher prevalence of potential pathogens in stray cats. These findings emphasize the need for public health planning and effective measures for controlling stray cats.

Keywords

cats feline pathogens zoonotic diseases metabarcoding

Zoonotic diseases are a substantial public health concern worldwide because they can be transmitted to humans through close contact with infected animals.^1,2 Direct transmission occurs as a result of intimate interactions between humans and animals,³ particularly cats, which are common pets.⁴ Increased urbanization has deepened the relationship between humans and their feline companions, resulting in a substantial increase in cat populations, both domesticated pets and stray animals.^5,6

Cats have become popular pets in South Korea, leading to increasing stray cat populations in urban environments. An estimated 200 000 feral cats live in Seoul, the capital of South Korea.^7,8 Close interactions between cats and humans can facilitate the vertical transmission of opportunistic pathogens, including Escherichia coli, Salmonella, and Campylobacter.⁹ Considering the high human population density in Seoul and the potential for zoonotic disease emergence, it is crucial to assess the presence of potential pathogens in cats.¹⁰

The advent of next-generation sequencing (NGS) has revolutionized the identification and understanding of diverse microbial ecosystems.^11,12 This technique can be applied to identify not only bacterial pathogens but also eukaryotic pathogens (eg, fungi, parasites) in animal feces.¹³ However, few attempts have been made to use NGS in companion animals such as cats.¹⁴ Moreover, research is limited on potential pathogens and their composition in cat feces in various living environments in Seoul.

The objective of our study was to comprehensively assess the fungal, parasitic, and bacterial communities residing in the gut of stray and pet cats to detect and characterize various potential pathogens. To our knowledge, our study is the first to use metabarcoding to simultaneously screen for potential bacterial and eukaryotic pathogens in cat feces using NGS to provide a comprehensive view of gut microbial communities and its associated health risks. We focused on investigating bacterial and eukaryotic pathogens (excluding viruses) because of their important roles in the feline gut and the availability of well-established metabarcoding methods for their detection and analysis.

Our findings could provide valuable insights into the presence and potential risks of zoonotic bacterial and eukaryotic pathogens, contributing to effective strategies to manage and mitigate the potential health risks associated with cats.

Methods

We used NGS techniques incorporating amplicons derived from the 16S ribosomal RNA (rRNA) gene to investigate the bacterial community and the 18S rRNA gene to investigate the eukaryotic populations in the feces of stray cats and pet cats.^15,16

Sample Collection and DNA Extraction

We collected 26 fresh fecal samples from cats residing in and around the Mapo-gu District in Seoul in April and May 2022 (eFigure 1 in the Supplement). The sample set consisted of 17 pet cats obtained from 3 pet shops and 9 stray cats from an animal shelter.

We collected the fecal samples in RNAlater (Thermo Fisher Scientific) immediately after defecation and maintained them at room temperature during transportation to the laboratory. Upon arrival, the samples were manually homogenized and divided into 200-mg aliquots, which were immediately frozen at −80°C. We used FastDNA SPIN Kit for Soil (MP Biomedicals) to extract DNA from the fecal samples, following the manufacturer’s instructions, and stored the DNA at −80°C until further analysis.

Illumina Sequencing

We prepared amplicon libraries following Illumina guidelines, as outlined in previous studies.^13,17 We amplified the variable region 4 (V4) of the 16S rRNA gene to generate DNA fragments necessary for subsequent sequencing and analysis. For the bacterial community, we amplified the 16S rRNA gene V4 region by polymerase chain reaction (PCR) using the primers 515F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3′).¹⁷ For the eukaryotic community, we amplified the 18S rRNA gene V9 region by PCR using the primers 1391f (5′-TCGTCGGCAGCGTCAGATG TGTATAAGA GACAGGTACACACCGCCCGTC-3′) and EukBr (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGTGATCCTTCTGCAGGTTCACCTAC-3′).¹³ We performed a limited-cycle amplification step, consisting of 8 cycles, to add multiplexing indices and Illumina sequencing adapters, incorporating the necessary sequencing components. Subsequently, we combined and pooled the amplicons from different samples. For sequencing, we used the iSeq 100 System (reagent v2 kit; Illumina) following Illumina instructions.

Bioinformatics and Statistical Analysis

We used Quantitative Insights Into Microbial Ecology version 2 (QIIME 2), a software platform (https://qiime2.org), to analyze sequencing reads. To remove errors and enhance accuracy, we used the adenosine deaminase 2 (DADA2) algorithm.^18,19 We used the classify–consensus–blast plugin in QIIME2 to taxonomically classify the reads. This process involved using a perc-identity parameter set at 0.95 for comparison against the SILVA 138 reference database for classifying 16S rRNA.²⁰ We used the QIIME 2 taxon plugin to filter out amplicon sequence variants (ASVs) taxonomically classified as mitochondrial or chloroplast sequences. The 16s rRNA sequencing data were rarefied to an even depth of 10 000 sequences across all samples for further analysis.

Alpha diversity testing

Alpha diversity testing is a method for comparing measures of observed richness or evenness of an average sample between groups. We used 3 metrics to conduct alpha diversity testing: the Observed Features Index, the Shannon Diversity Index, and Faith’s Phylogenetic Diversity Index. We then used the Wilcoxon rank-sum test to analyze differences in alpha diversity indices between the 2 cat groups. The Wilcoxon rank-sum test is a nonparametric statistical test that compares 2 independent samples to assess whether their population distributions are different.²¹

Beta diversity testing

Beta diversity testing is a method for comparing variability in community composition between groups. We conducted principal coordinates analysis and permutational multivariate analysis of variance (PERMANOVA) using Bray–Curtis and Jaccard distance indices to analyze beta diversity. PERMANOVA, a permutation test, compares group similarity and evaluates grouping effects on community structure, robustly handling small sample sizes and data variations.²²

Further analysis of taxonomic abundance

For identifying differentially abundant taxa between groups, we used analysis of differential abundance taking sample variation into account (ALDEx2) and analysis of composition of microbiomes (ANCOM). ALDEx2 uses a Bayesian model to estimate differences in abundance while accounting for sequencing depth and sample variation.²³ ANCOM uses a log-ratio transformation to identify differentially abundant taxa in microbiome data.²⁴ For the taxonomic classification of eukaryotic ASV sequences, we used all sequences included in the National Center for Biotechnology Information nucleotide database (www.ncbi.nlm.nih.gov/nuccore) to build a comprehensive database of fungi and parasites. We used this database for the classification of 18S rRNA sequences, facilitating the generation of a taxonomic table for eukaryotic ASVs. Statistical analysis comparing potential pathogens involved the exclusion of sequences with an ASV count of ≤6, as determined by predefined thresholds. We used the Pearson χ² test to assess differences between groups in the prevalence of potential pathogens.²⁵ We applied the Yates continuity correction to adjust for any overestimation of significance in the χ² test.²⁶ We considered P < .05 to be significant.

Ethics Approval

This study followed the guidelines of South Korea’s Institutional Animal Care & Use Committee under the Food and Drug Administration and the Ministry of Agriculture, Food and Rural Affairs. The Department of Animal Protection reviewed the study and confirmed that no permissions were needed, because it involved no direct contact with animals or experimental treatment, in line with national guidelines.

Results

Overview of Illumina Sequencing Data

Our methods generated 1 351 077 reads (median [range] reads per sample = 56 175 [19 384-69 126]). Most reads were classified as members of the phyla Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, and Fusobacteria (eTable 1 in the Supplement).

Composition of Bacterial Community

Across both groups of cats, Bacteroidetes was the most abundant phylum (Figure 1A), with an average relative abundance of 36.3%, followed closely by Firmicutes, accounting for 34.4% of total relative abundance (eFigure 2A in the supplement). Prevotella, Bacteroides, Collinsella, Fusobacterium, Sutterella, Faecalibacterium, and Alloprevotella were among the most abundant genera across both groups (Figure 1B).

Figure 1.

Fecal prokaryotic communities of samples from pet cats (n = 17) and stray cats (n = 9), South Korea, 2022. The relative abundance for each sample was determined at 2 levels: (A) phylum and (B) genus. Data were collected from 26 fresh fecal samples in Seoul’s Mapo-gu District in April and May 2022.

In our analysis of the relative abundances of potential pathogens, Helicobacter was the most prevalent pathogen in pet cats, constituting a substantial proportion (69.2%), followed by Campylobacter (11.8%) (eFigure 2B in the Supplement). Escherichia-Shigella (4.0%), Clostridium sensu stricto 1 (4.4%), Streptococcus (3.6%), and Enterococcus (7.0%) were present, and Brachyspira and Mycoplasma were absent in pet cats. In stray cats, Escherichia-Shigella (42.4%) had the highest abundance, followed by Helicobacter (24.2%) and Streptococcus (12.1%). Brachyspira (1.9%) and Mycoplasma (1.2%) were also detected in stray cats.

Helicobacter spp had the highest total positivity and was present in 20 of 26 samples (77%) (Table 1). Campylobacter spp closely followed and was present in 18 of 26 samples (69%). In addition, Streptococcus spp were detected in 9 samples (35%), Escherichia-Shigella spp in 7 samples (27%), Brachyspira spp in 4 samples (15%), and Mycoplasma spp in 1 sample (4%). We found significant differences in pathogen distribution between the 2 groups. Campylobacter was present in 9 (53%) pet cat samples and 9 (100%) stray cat samples (P = .01). Escherichia-Shigella was present in 2 (12%) pet cat samples and 5 (56%) stray cat samples (P = .02). Brachyspira was absent in pet cat samples but was present in 4 (44%) stray cat samples (P = .003).

Table 1.

Potential pathogens identified by bacterial 16S rRNA gene amplification at the genus level in a sample of pet cats (n = 17) and stray cats (n = 9), South Korea, 2022^a

Taxonomy (genus level)	Pet cats, no. (%)	Stray cats, no. (%)	Total positivity, no. (%)	P value^b
Helicobacter	12 (71)	8 (89)	20 (77)	.29
Campylobacter	9 (53)	9 (100)	18 (69)	.01
Clostridium sensu stricto 1	10 (59)	6 (67)	16 (62)	.70
Enterococcus	10 (59)	4 (44)	14 (54)	.48
Streptococcus	5 (29)	4 (44)	9 (35)	.44
Escherichia-Shigella	2 (12)	5 (56)	7 (27)	.02
Brachyspira	0	4 (44)	4 (15)	.003
Mycoplasma	0	1 (11)	1 (4)	.16

Data were collected from 26 fresh fecal samples in Seoul’s Mapo-gu District in April and May 2022.

The difference in values between pet cats and stray cats was determined by the Yates-corrected Pearson χ² test; P ≤ .05 was considered significant.

Differences in Alpha and Beta Diversity Between Groups

In alpha diversity tests, we found comparable microbial diversity in the stray and pet cat groups (Figure 2), with no significant differences between the 2 groups. In beta diversity testing, PERMANOVA showed a significant difference in fecal microbial composition between the 2 groups of cats (Figure 3).

Figure 2.

Box and whisker plot for the alpha diversity between the fecal samples of pet and stray cats, South Korea, 2022. Three alpha diversity metrics were calculated for pet cats (n = 17) and stray cats (n = 9): (A) the Observed Features Index, (B) Faith’s Phylogenetic Diversity Index (Faith’s PDI), and (C) the Shannon Diversity Index. Data were collected from 26 fresh fecal samples in Seoul’s Mapo-gu District in April and May 2022. The horizontal bar inside the boxes indicates the median, the lower and upper ends of the boxes are first and third quartiles, and the whiskers indicate 95% CIs.

Figure 3.

Difference in microbial composition between the fecal samples of pet and stray cats, shown by beta diversity testing, South Korea, 2022. Shown are principal coordinate analysis plots of (A) Jaccard distances and (B) Bray–Curtis distances. Each point represents the composition of an individual sample; points that are closer together indicate greater similarity in microbial composition. P values were determined by permutational analysis of variance tests. Data were collected from 26 fresh fecal samples in Seoul’s Mapo-gu District in April and May 2022.

Differences in Taxonomic Abundance Between Groups

In further analysis of taxonomic abundance using ANCOM, the relative abundance of the Lactobacillus genus was significantly higher in the stray cat group (1.1%) than in the pet cat group (0.003%), with a W value of 183 (eTable 2 in the Supplement). Both ANCOM and ALDEx2 tests showed that Lactobacillus was significantly more abundant in the stray cat group (1.1%) than in the pet cat group (0.003%) (eTable 2 and eTable 3 in the Supplement), indicating consistency between the 2 methods.

Composition of Eukaryotic Community

The data for eukaryotic organisms with the V9 region metabarcoding illustrated that the genus Giardia dominated the samples, with an average relative abundance of 35.5%. Tritrichomonas accounted for approximately 27.2% of the genera, followed by Pentatrichomonas at 17.9% and Saprochaete at 7.6% (eTable 4 in the Supplement).

In the pet cat group, the most prominent genus was Giardia, accounting for 46.3% of the total abundance (eFigure 4 in the Supplement). Saprochaete had a relative abundance of 10.5%, while Tritrichomonas accounted for approximately 36.7% of the genera. Among the stray cats, Pentatrichomonas was dominant, representing 65.5% relative abundance.

Among the detected taxa, Giardia was the most prevalent, present in 5 of the 26 samples (19%). Tritrichomonas was the second most frequently detected taxon, found in 4 of 26 samples (15%). Pentatrichomonas, Cryptosporidium, and Candida were also identified, although at lower frequencies. Pentatrichomonas was detected in 2 of 26 samples (8%), whereas Cryptosporidium, Candida, and Hydatigera were each found in 1 sample, accounting for 3.8% of the microbial community for each taxon (Table 2). Pentatrichomonas was absent in all pet cat samples but was detected in 2 of 9 stray cat samples (22%), and this difference was significant (P = .04).

Table 2.

Potential pathogens identified by the eukaryotic 18S rRNA gene amplification at the genus level in a sample of pet cats (n = 17) and stray cats (n = 9), South Korea, 2022^a

Taxonomy (genus level)	Pet cats, no. (%)	Stray cats, no. (%)	Total positivity, no. (%)	P value^b
Giardia	4 (24)	1 (11)	5 (19)	.44
Tritrichomonas	3 (18)	1 (11)	4 (15)	.66
Pentatrichomonas	0	2 (22)	2 (8)	.04
Cryptosporidium	1 (6)	0	1 (4)	.46
Candida	1 (6)	0	1 (4)	.46
Hydatigera	0	1 (11)	1 (4)	.46

Data were collected from 26 fresh fecal samples in Seoul’s Mapo-gu District in April and May 2022.

The difference in values between pet cats and stray cats was determined by the Yates-corrected Pearson χ² test; P ≤ .05 was considered significant.

Discussion

Understanding the presence of zoonotic bacteria in the gut microbial communities of cats is critical for public health initiatives, particularly those focused on the One Health approach.²⁷ Fecal–oral transmission is a major pathway for zoonotic pathogens, with humans at risk from direct contact with animal waste or contaminated soil.²⁸ This risk extends beyond direct contact, as zoonotic pathogens can indirectly reach humans through the contamination with fecal matter of objects in the environment, food sources, or even water supplies.²⁹ Interestingly, Helicobacter spp emerged as the most prevalent potential zoonotic genus, with a detection rate of 77% (identified in 20 of 26 samples).^30
-32 In addition, Helicobacter heilmannii, a bacterium known to colonize the gastric mucosa of animals, has also been implicated in similar health concerns in humans.³³ Notably, sequencing analysis of stray cat samples from Iran revealed a high prevalence (4 of 6 positive samples) of Helicobacter canis, which aligns with our own findings.³⁴ Collectively, these studies highlight the potential zoonotic risk posed by cats, particularly regarding infections with Helicobacter species other than Helicobacter pylori, in humans.

Campylobacter spp emerged as the second most prevalent potential bacterial pathogen, identified in 18 of 26 samples (69%). Notably, Campylobacter was detected in all 9 samples collected from stray cats, a significantly higher positivity rate than in the pet group. This disparity may be attributed to several factors. A relevant study conducted in Poland aligns with these findings, demonstrating that animals with access to small water basins, exposed to food sources, or in contact with wild birds, poultry, or their feces had a higher incidence of Campylobacter species.³⁵

Samples obtained from stray cats contained elevated proportions of other potentially zoonotic bacteria. Specifically, Escherichia-Shigella spp were detected in 5 of 9 samples (56%), and Brachyspira spp were found in 4 samples (44%), a significantly higher prevalence than that in the pet group. The higher incidence of Escherichia-Shigella spp in stray cats could be linked to the presence of some genera of Escherichia, such as E coli, which can be found in raw meat, poultry, dairy products, contaminated water, and soil.³⁶ Stray cats are susceptible to infection through the consumption of these items or contact with infected animals or their feces.³⁷ The Brachyspira genus encompasses multiple bacterial species known to cause diseases in animals, including pigs, chickens, and humans.³⁸ However, investigation of Brachyspira in cat feces is limited.

Our analysis showed no significant differences in alpha diversity between the 2 cat groups, which is consistent with the findings of a previous study.³⁹ However, our result for beta diversity was different from that found in the previous study, which could be due to different sequencing methods and different controlling conditions of the experimental design.^40,41 The difference could also be due to other factors, such as diet, environment, medication use, developmental stage, genetics, and health status, that influence the composition of the gut microbial communities.^41,42

Differential abundance analysis using ANOVA and ALDEx revealed a notable finding: stray cats have a higher proportion than pet cats do of Lactobacillus spp in their gut microbial composition. The higher prevalence of Lactobacillus in stray cats could be attributed to their diverse dietary habits, as they scavenge food from various sources, leading to an enrichment of the microbial population compared with pet cats fed kibbled diets.^43,44

Our analysis revealed that Giardia was the most prevalent potential eukaryotic pathogen, detected in 5 of 26 samples (19%). G intestinalis, a common Giardia species, causes global gastrointestinal illness in humans, including diarrhea, abdominal pain, and malabsorption.⁴⁵ Our study found pet and stray cats harboring Giardia spp, indicating cats may be reservoirs for zoonotic strains. Further research should identify the strains in cat feces and assess their zoonotic risks.

Stray cats had a high proportion of Pentatrichomonas (22%), whereas pet cats had no detectable presence of this parasite. This difference underscores the potential effect of environmental factors and living conditions on parasite prevalence. Pentatrichomonas hominis, although generally considered a commensal protozoan in humans, has been implicated in various adverse health effects, including diarrhea, pulmonary infections, and even rheumatoid arthritis.^46
-48 It is known to cause gastrointestinal disorders in humans, with clinical symptoms similar to irritable bowel syndrome.⁴⁹ In addition, research suggests a correlation between P hominis infection and colorectal cancer through microbiome alteration.⁵⁰ These findings highlight the importance of considering Pentatrichomonas infections in both human and feline populations. The potential risk for zoonotic transmission of P hominis should not be overlooked.

Limitations

This study had several limitations. First, the Illumina iSeq 100 sequencing method provided informative compositional data but lacked species- and strain-level resolution insights. Second, the relatively small sample size, especially the fewer samples from stray cats than from pet cats, along with the inability to control environmental factors, may limit the generalizability of our findings. Third, this study did not include viruses, limiting its scope and preventing a more comprehensive understanding of feline fecal microbiomes. Future research should address these limitations by increasing the sample size and better controlling environmental factors to enhance the robustness and generalizability of the findings.

Conclusions

To our knowledge, this is the first study to use metabarcoding to screen potential pathogens and analyze their prevalence based on cat habitat (home, shelter) in Korea. By using NGS, we revealed distinct fecal microbiomes between the 2 groups. Stray cats harbored a higher prevalence of Campylobacter spp, Escherichia-Shigella spp, Brachyspira spp, and Pentatrichomonas spp, highlighting their unique microbial signatures and potential zoonotic risks to humans. This study showed a possible link between changes in environmental conditions and putative pathogen compositions. These findings contribute substantially to our understanding of zoonotic pathogen presence in cats, emphasizing the importance of stray cat management and further research to address health implications for cats in diverse environments.

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Footnotes

Acknowledgements

Maria Gloria Ojeda Ayala and Singeun Oh contributed equally to this work.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Research Foundation of Korea, funded by the Korea government (MSIT) (no. RS-2024-00456300), and by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (no. RS-2024-00406488 and RS-2023-KH139971).

ORCID iDs

Singeun Oh, MBA

Ju Yeong Kim, MD, PhD

Availability of Data and Materials

Raw sequence data are available in National Center for Biotechnology Information GenBank under BioProject PRJNA992120.

Supplemental Material

Supplemental material for this article is available online. The authors have provided these supplemental materials to give readers additional information about their work. These materials have not been edited or formatted by Public Health Reports’s scientific editors and, thus, may not conform to the guidelines of the AMA Manual of Style, 11th Edition.

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