https://orcid.org/0000-0002-8531-7001
Abstract
Urothelial carcinoma (UC) is the tenth most diagnosed cancer in humans worldwide. Dogs are a robust model for invasive UC as tumor development and progression is similar in humans and dogs. Recent studies in humans have revealed alterations in urine microbiota in individuals with UC; however, microbial alterations in dogs with UC have not been evaluated. The objective of this pilot study was to compare the urine and fecal microbiota of dogs with UC and matched healthy controls. DNA was extracted from urine and fecal samples followed by 16S rRNA sequencing and analyses using QIIME2 and R. Dogs with UC had significantly decreased microbial diversity (Kruskal–Wallis; Shannon, p = 0.048) and altered microbial composition (PERMANOVA: Unweighted UniFrac, p = 0.011) in urine, but not fecal samples. The relative abundance of Fusobacterium was also increased, although not significantly, in urine and fecal samples of dogs with UC. A comparison of canine and human urine microbiota further revealed similarities in dominant microbial taxa across both host species. This study supports the value of dogs as a model for studies on bladder cancer and urine microbiota, and it provides a foundation for future work exploring host-microbe dynamics in UC carcinogenesis, prognosis, and treatment.
Introduction
Bladder cancer is the tenth most diagnosed cancer worldwide (World Bladder Cancer Patient Coalition. GLOBOCAN (Citation2020)). In 2020, the International Agency for Research on Cancer estimated over 573,000 new bladder cancer diagnoses would be confirmed worldwide (Sung et al. Citation2021). Urothelial carcinoma (UC), also known as transitional cell carcinoma, is the most common type of bladder cancer. Age (being over age 55), race (white), sex (male), and some heritable mutations (Randi et al. (Citation2007); Aben et al. (Citation2002); Murta-Nascimento et al. (Citation2007); Mueller et al. (Citation2008); Chu et al. (Citation2013); Mucci et al. (Citation2016); Martin et al. (Citation2018); Aveyard et al. (Citation1999)) are established risk factors for bladder cancer (Antoni et al. (Citation2017); American Cancer Society. Key Statistics for Bladder Cancer [Internet] (Citation2021); Wang et al. (Citation2018)). Bladder cancer is also strongly associated with environmental exposures such as smoking (Cumberbatch et al. (Citation2016); Alguacil et al. (Citation2011); Burger et al. (Citation2013); Wu et al. (Citation2018)) or occupational exposure to chemicals like aromatic amines, pesticides, industrial dyes, or diesel fumes (Pesch et al. (Citation2014); Koutros et al. (Citation2016)). However, not all persons exposed to these chemicals develop urothelial carcinoma indicating that there are individualized host-environment interactions that mediate UC risk.
Clear host-environment (diet) interactions mediated through the gut microbiome have emerged in colorectal carcinogenesis (O’Keefe (Citation2016); Sears and Garrett (Citation2014)) and environment-microbiome-carcinogenesis links have also begun emerging in lung cancer (Mao et al. (Citation2018); Ramírez-Labrada et al. (Citation2020)). For example, diets high in animal fat can directly or indirectly impact microbial composition by increasing liver bile acid production and excretion into the intestines. Bile tolerant microbes or microbes that can metabolize primary bile acids expand in this bile-rich environment, and some of these microbes produce pro-inflammatory, cytotoxic, or genotoxic secondary metabolites that can contribute to colorectal carcinogenesis. Work on the gut microbiome has far outpaced and outnumbered studies on the urine / bladder microbiome; however, it has now become apparent that the urine microbiota play a key role in host health and may also be influencing bladder cancer development and progression (Aragon et al. (Citation2018)).
Alterations in urine microbiota have been reported in association with multiple genitourinary diseases including chronic kidney disease (Kramer et al. (Citation2018)), chronic prostatitis, chronic pelvic pain syndrome (Shoskes et al. (Citation2016)), interstitial cystitis (Siddiqui et al. (Citation2012)), sexually transmitted infections (Nelson et al. (Citation2010)), urgency urinary incontinence (Pearce et al. (Citation2014)), urinary tract infections (Magruder et al. (Citation2019)), urinary stone disease (Zampini et al. (Citation2019)), urogenital schistosomiasis (Adebayo et al. (Citation2017)), urogynecologic surgery (Fok et al. (Citation2018)), and vaginosis (Gottschick et al. (Citation2017)). A few recent studies on the urine / bladder microbiome have also revealed subtle but intriguing differences in urine or bladder tissue microbial diversity and composition of individuals with and without UC (Table ) (Wu et al. (Citation2018); Bi et al. (Citation2019); Pederzoli et al. (Citation2020); Liu et al. (Citation2019); Bučević Popović et al. (Citation2018); Chipollini et al. (Citation2020); Mai et al. (Citation2019); Oresta et al. (Citation2021); Xu et al. (Citation2014); Chen et al. (Citation2021); Hussein et al. (Citation2021); Mansour et al. (Citation2020)), but approaches and results in these studies vary widely and require additional examination. The effects of gut microbiota on bladder health is another area that requires further attention, as there are compelling studies demonstrating the links between gut microbiota and urinary tract infections (Magruder et al. (Citation2019); Worby et al. (Citation2022)). Moreover, short chain fatty acids (SCFA) or other metabolites produced by gut microbes may affect inflammation levels in distal organs such as the bladder or lungs (Trompette et al. (Citation2014)). Studies in relevant animal models could advance this research by offering a more controlled environment; however, rodent models of UC have many limitations (Ding et al. (Citation2014)). Canine models of UC, on the other hand, exhibit cancer heterogeneity, molecular features, muscle invasive cancer behavior, and host immunocompetence similar to humans (Connelly et al. (Citation2019); Chaitman et al. (Citation2020); Manchester et al. (Citation2019)). Moreover, humans and dogs share many of the same environmental exposures, and canine UC, like human UC, has been epidemiologically linked to chemical exposures including herbicides and pesticides (De Brot et al. (Citation2018); Glickman et al. (Citation2004)). Notably, the human microbiome is more similar to the dog microbiome compared to other animal models, such as the rodent microbiome (Coelho et al. (Citation2018)), making dogs a more suitable model for studying microbiota in relation to UC.
Table 1. Key findings in 13 publications about the urine / tissue microbiota and urothelial carcinoma.
Here, we used a naturally-occurring canine model of bladder cancer to compare the urine and fecal microbiota of dogs with and without UC. We broadly hypothesize that microbes could be directly or indirectly contributing to bladder cancer pathogenesis by altering the bladder environment (Magruder et al. (Citation2019); Worby et al. (Citation2022); Reid et al. (Citation2001)). Our first step in testing this hypothesis was to characterize the microbiota. We predicted that microbial community profiles would differ between dogs with and without UC.
Materials and methods
Sample collection
Approval for the use of dogs in this study was obtained at Purdue University (IACUC: 1111000169) and Ohio State University (IACUC: 2019A00000005). This study was carried out and reported according to ARRIVE guidelines. All dogs were recruited through Purdue University College of Veterinary Medicine between September 2016 and October 2019. Urine and fecal samples were initially collected from 57 dogs with biopsy-confirmed urothelial carcinoma (UC) and 56 age-, sex-, and breed-matched healthy controls (Figure ). Dogs with active urinary tract infections or other co-morbidities (e.g. diabetes) were excluded. We additionally excluded any dog with a history of chemotherapy (vinblastine, zebularine, vemurafenib, chlorambucil, mitoxantrone, and cyclophosphamide) or a history of antibiotics within the previous three weeks (Montassier et al. (Citation2015); Stringer et al. (Citation2013); Stewardson et al. (Citation2015); Suchodolski et al. (Citation2009); Connelly et al. (Citation2019); Chaitman et al. (Citation2020); Manchester et al. (Citation2019)). We did not exclude dogs on non-steroidal anti-inflammatory drugs (NSAIDs), including piroxicam and deracoxib, which are commonly used in dogs with UC. Healthy dogs underwent physical exams and had no history or indications of gastrointestinal or urogenital disease.
Figure 1. Experimental design.
In healthy dogs, urine was collected via mid-stream free catch. In dogs with UC, a variety of urine collection methods were employed as deemed clinically appropriate including: mid-stream free catch, catheter, or cystoscopy. Free catch urine can include bacteria from the bladder, urethra, periurethral skin, prepuce, or vagina, while urine collected via catheterization or cystoscopy primarily includes microbes from the bladder and limits the presence of genital and skin microbes (Oresta et al. (Citation2021); Wolfe et al. (Citation2012); Bajic et al. (Citation2020); Hourigan et al. (Citation2020)). To determine if collection method could potentially influence our results, we compared samples from dogs with UC collected via mid-stream free catch (n = 8) to samples collected via non-free catch methods (catheterization, cystoscopy) (n = 11) (S1 Table; S1-S3 Figs). We observed significant differences in microbial composition but not diversity by collection method (Bray–Curtis PERMANOVA rarefied: p = 0.008; non-rarefied: p = 0.005; S1f and S2f Figs). Moreover, Staphylococcus and Streptococcus – common skin colonizers - were amongst the top genera in mid-stream free catch urine but not amongst the top genera in non-free catch urine (S2 Table). Based on the compositional differences we observed by collection method and on other studies that have reported differences in urine microbiota due to collection method (Oresta et al. (Citation2021); Wolfe et al. (Citation2012); Bajic et al. (Citation2020); Hourigan et al. (Citation2020); Pohl et al. (Citation2020); Chen et al. (Citation2020)), we opted to limit the remainder of our analyses to samples collected via mid-stream free catch only. This allowed us to compare microbiota in urine from healthy dogs and dogs with UC without introducing collection method as a potential confounder.
As such, after exclusions, we analyzed mid-stream free-catch urine samples from a total 7 dogs with UC and 7 age-, sex-, and breed-matched healthy controls (Table ). To determine if bladder cancer affected fecal microbial diversity and composition, we analyzed fecal samples from a subset of the dogs whose urine samples were selected for analysis (4 dogs with UC and 6 healthy dogs) (Magruder et al. (Citation2019); Flores-Mireles et al. (Citation2015); Paalanne et al. (Citation2018)). We also evaluated fecal microbiota in a larger validation cohort of 30 dogs with UC and 30 healthy dogs. All urine and stool samples were placed on ice immediately after collection and then transferred into a −80°C freezer. Samples were transported on dry ice from Purdue (West Lafayette, IN, USA) to the Ohio State University (Columbus, OH, USA), where they were stored in at −80°C until extraction.
Table 2. Demographics of dogs with and without urothelial carcinoma (UC).
Urine samples were collected and analyzed from all dogs. Stool samples were collected and analyzed from a subset of these dogs including 6 healthy (4 females, 2 males), and 4 with UC (3 females, 1 male).
DNA extraction and quantification
Urine samples were extracted using QIAamp® BiOstic® Bacteremia DNA Isolation Kit (Qiagen, Hilden, Germany) as described previously (Mrofchak et al. (Citation2021a)). Fecal samples were extracted using the QIAamp® PowerFecal® DNA Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. Negative (no sample) controls were run with each kit used for extraction. DNA concentrations were measured using a Qubit® 4.0 Fluorometer (Invitrogen, Thermo Fisher ScientificTM, Carlsbad, CA, USA) and purity was assessed using Nanodrop One (Thermo Fisher ScientificTM, Carlsbad, CA, USA).
16S rRNA sequencing and sequence processing
Library preparation, PCR amplification, and amplicon sequencing was performed at Argonne National Laboratory (DuPage County, Illinois). Likewise, negative controls underwent the full extraction, library preparation, and sequencing process. We amplified the V4 region of the 16S rRNA gene using primers 515F and 806R, and PCR and sequencing were performed as described previously (2 × 250 bp paired-end reads, on an Illumina Miseq (Lemont, IL, USA)) (Mrofchak et al. (Citation2021a); Caporaso et al. (Citation2012); Caporaso et al. (Citation2011)). Raw, paired-end sequence reads were processed using QIIME2 v. 2020.11 and DADA2 (Bolyen et al. (Citation2019); Callahan et al. (Citation2016)). Taxonomy was assigned in QIIME2 using the Silva 132 99% database and the 515F / 806R classifier (Yilmaz et al. (Citation2014); Quast et al. (Citation2013)). In the analysis comparing urine collection method in dogs with UC, we excluded samples with fewer than 1,000 reads and analyzed the data with rarefaction (at 1,000 reads) and without rarefaction. We included both analyses because rarefaction, especially at low read counts, can increase type 1 errors and mask potential differentially abundant taxa between samples (McMurdie and Holmes (Citation2014)). In the analyses comparing urine and fecal microbiota from dogs with and without UC, samples with fewer than 7,000 reads were excluded; this cutoff allowed us to retain all but two urine samples while excluding all negative controls (Figure ). Urine samples from dogs with and without UC were rarefied at 7,000 reads; fecal samples were rarefied at 9,233 reads, which included all fecal samples. Sequencing data for this project is available in SRA BioProject PRJNA763920.
Prior to analyses, we first removed singletons (Amplicon Sequence Variants (ASVs) with only one read in the dataset). ASVs are roughly equivalent to a microbial species or strain. We then applied the R package decontam to identify and filter out putative contaminant ASVs based on their frequency and prevalence (0.5 threshold) as compared to negative controls (R package, v.1.10.0) (Davis et al. (Citation2018)). In total, we identified and removed 13 putative contaminant ASVs from the urine samples and 8 from the fecal samples (S3 Table). We also removed sequences aligned to chloroplasts, eukaryotes, mammalia, and mitochondria. In addition, in the urine samples, we removed taxa within the phylum Cyanobacteria and the class Chloroflexia. All six negative controls, which contained fewer than 7000 reads, were then removed from subsequent analyses.
Comparing human and canine urine microbiota
To compare canine and human urine microbiota, we downloaded publicly available 16S rRNA sequencing data from the Bučvić Popović et al. study on human urine microbiota in individuals with and without bladder cancer (European Nucleotide Archive: PRJEB22327) (Bučević Popović et al. (Citation2018)). Raw reads from the canine and human studies were merged and processed together as described above (See 16S rRNA sequencing and sequence processing). Data were rarefied to 7000 reads to account for differences in read counts between human and dog samples (average read count – humans: 370,586 ± 31,353 reads; dogs: 20,010 ± 7,329). We selected this study for comparison based on its similarities to our canine study: human urine was collected mid-stream free catch and frozen without a buffer prior to DNA extraction. The Bučvić Popović et al. study also utilized the same hypervariable region (V4), primers (515F-806R), and sequencing platform (MiSeq, Illumina, San Diego, CA) for 16S rRNA sequencing. However, different DNA extraction methods were used in both studies.
Statistical analyses
Data were tested for normality using the Shapiro Wilk Normality Test in R version 3.5.2 (R Core Team (Citation2018)). We then compared DNA concentrations and read numbers between groups using Wilcoxon Rank Sum tests and two-sample t-tests, respectively. All alpha and beta diversity metrics were assessed using the R package phyloseq with a p-value cutoff of 0.05 adjusted using the Benjamini & Hochberg False Discovery Rates (McMurdie and Holmes (Citation2013)). Alpha-diversity metrics included Shannon, Simpson, and Observed Features followed by Kruskal–Wallis Rank Sum Tests to compare metrics by group. Beta-diversity metrics included Bray–Curtis, Unweighted UniFrac, and Weighted UniFrac. Permutational Multivariate Analysis of Variance (PERMANOVA) were implemented in QIIME2 v. 2020.11 to compare bacterial community composition by group. An Analysis of Composition of Microbiome (ANCOM) was used to identify differentially abundant taxa by group.
Results
Urine microbiota in dogs with UC
We compared the urine microbiota of 7 dogs with UC to 7 age-, sex-, and breed-matched healthy controls. The total number of reads across all samples ranged from 7,232–36,692 with a mean of 20,010 ± 7,329 reads. Urine samples contained a total of 21 bacterial phyla, 308 genera, and 187 species. Urine DNA concentrations were significantly higher in dogs with UC as compared to healthy dogs (Figure a: Wilcoxon Rank Sum test, p = 0.002), but there was no significant difference in the number of 16S reads between dogs with and without UC (Figure b: two-sample t-test, p = 0.99).
Figure 2. DNA concentrations and number of 16S reads in the urine samples of dogs with and without urothelial carcinoma (UC). (a) DNA concentrations were significantly greater in dogs with UC than in healthy dogs (Wilcoxon Rank Sum test, p = 0.002). (b) The number of 16S reads did not differ significantly between groups (two-sample t-test, p = 0.99). Error bars denote standard error. Statistical significance is represented by stars: * < 0.05, ** < 0.001, *** < 0.0001.
Dogs with UC had significantly lower urine microbial diversity compared to healthy dogs as measured by the Shannon diversity index and Observed Features but not by the Simpson diversity index (Kruskal–Wallis: Shannon, p = 0.048; Observed Features, p = 0.025; Simpson, p = 0.133; Figure a, S4a and S4b Figs). Dogs with UC also had significantly different urine microbial composition than healthy dogs based on an Unweighted UniFrac distance matrix (Figure b; PERMANOVA, p = 0.011); although, no significant differences were observed by Bray Curtis (p = 0.888) or Weighted UniFrac (p = 0.168) distance matrices (S4c and S4d Figs). At the phylum level, Firmicutes (healthy: 61.1%; UC: 79.5%) Proteobacteria (healthy: 18.0%; UC: 15.6%), and Actinobacteria (healthy: 12.5%; UC: 4.26%) were the three most abundant phyla in the urine of healthy dogs and dogs with UC (Figure a). (Note, percentages represent relative abundance of a microbial taxon compared to all taxa.) At the family level, Staphylococcaceae (healthy 42.6%; UC 48.6%) and Streptococcaceae (healthy 5.99%; UC 14.8%) were amongst the most abundant taxa (Figure b; For genus and order level taxa see S5 Fig). Interestingly, Fusobacterium was present in the urine of dogs with UC but not in the urine of healthy dogs (relative abundance of Fusobacterium in healthy dogs: 0%; in dogs with UC: 0.167%). There were no differentially abundant taxa between healthy dogs and dogs with UC at the phylum, genus, or ASV levels.
Figure 3. Microbial diversity and composition in the urine of dogs with and without UC. (a) Healthy dogs had a significantly higher microbial diversity compared to dogs with UC as measured by the Shannon diversity index (Kruskal-Wallis, p = 0.048). (b) Microbial composition between healthy dogs and dogs with UC also differed significantly (Unweighted UniFrac, PERMANOVA, p = 0.011). Error bars denote standard error. Statistical significance is represented by stars: * < 0.05, ** < 0.001, *** < 0.0001.
Figure 4. Phyla and family taxa bar plots of urine samples in dogs with and without UC. (a) Phyla and (b) family relative abundances. At the family level, the taxonomic composition of each sample is shown individually to demonstrate the variability across urine samples.
Fecal microbiota in dogs with UC
We compared the fecal microbiota of a subset of dogs from the urine analyses for which we also had fecal samples: four dogs with and six dogs without UC. The total number of reads across all fecal samples ranged from 9,233–28,345 with a mean of 19,196 ± 6,100 reads. Fecal samples contained a total of 8 bacterial phyla, 92 genera, and 45 species. There was no significant difference in fecal DNA concentrations or number of 16S reads in dogs with UC as compared to healthy dogs (DNA concentration: Wilcoxon Rank Sum Test, p = 0.136; 16S reads: Two-sample t-test, p = 0.322; Figure ).
Figure 5. DNA concentrations and number of 16S reads in the fecal samples of dogs with and without UC. (a) DNA concentrations in dogs with UC as compared to healthy dogs (Wilcoxon Rank Sum Test, p = 0.136). (b) The number of 16S reads did not differ significantly between groups (two-sample t-test, p = 0.322). Error bars denote standard error.
Fecal microbial diversity and composition did not differ significantly in dogs with and without UC (Kruskal–Wallis: Shannon, p = 0.67; Unweighted UniFrac PERMANOVA, p = 0.252; Figure , S6 Fig). The top three most abundant phyla across all fecal samples were Firmicutes (healthy: 72.6%; UC: 32.9%), Bacteroidetes (healthy: 10.6%, UC 31.9%) and Fusobacteria (healthy: 11.3%, UC: 31.1%) (Figure ; S7 Fig). At the family and genera levels, Fusobacterieacea (healthy: 11.4%, UC: 31.7%) and Fusobacterium (healthy: 12.0%, UC: 33.1%) were the most abundant taxa in UC but not healthy samples, respectively; although, these differences were not statistically significant. Only one Bacteroides spp. was significantly increased in relative abundance in dogs with UC compared to healthy dogs (ANCOM, W = 25).
Figure 6. Microbial diversity and composition of fecal samples in dogs with and without UC. (a) Fecal microbial diversity did not differ significantly between dogs with and without UC (Kruskal-Wallis, p = 0.67). (b) Microbial composition also did not differ significantly between healthy dogs and dogs with UC (Unweighted UniFrac, PERMANOVA, p = 0.252). Error bars denote standard error.
Figure 7. Taxa bar plots of fecal samples in dogs with and without UC. (a) Microbial phyla and (b) family relative abundances.
To determine how results from this subset of fecal samples compared to a larger sample set, we then analyzed the fecal microbiota in a validation cohort of 30 dogs with UC and 30 age-, sex-, and breed-matched healthy controls (S4 Table). All of these dogs also met our inclusion/exclusion criteria. Fecal DNA concentrations, 16S reads, and fecal microbial diversity and microbial composition again did not differ significantly between groups (DNA concentration: Wilcoxon Rank Sum test, p = 0.515; 16S reads: two-sample t-test, p = 0.0697; S8 Fig; S5 Table). Firmicutes, Bacteroidetes, and Fusobacteria also remained the most abundant phyla across both groups, and interestingly, Fusobacteriaceae (healthy: 17.4%; UC: 28%) and Fusobacterium (healthy: 18.5%; UC: 29.2%) were still the most abundant family and genus in the fecal samples of dogs with UC (S9 Fig); although, this difference was still not significant. In fact, no taxa were differentially abundant at the phylum, genus, or ASV levels between groups in the larger sample set (S5 Table), suggesting that that Bacteroides spp. identified as differentially abundant in the subset was likely an artifact of small sample size.
Microbiota identified in both fecal and urine samples
As the gut can be a source for microbes in the urinary tract (Magruder et al. (Citation2019); Paalanne et al. (Citation2018)), we then combined urine and fecal data to determine what ASVs were present in both urine and fecal samples. There were a total of 1,204 ASVs across all urine and fecal samples combined. Sixty-six ASVs were identified in both urine and fecal samples from any dog (S6 Table). The most common taxa found in both urine and fecal samples included taxa in the genera Streptococcus and Blautia. Notably, Fusobacterium spp., Porphyromonas spp., Campylobacter spp., Helicobacter spp., and Clostridiodes difficile were also found in both urine and fecal samples. Further, nine ASVs were identified in urine and fecal samples from the same dogs (S7 Table). These ASVs included two Escherichia or Shigella spp., two Streptococcus spp., a Clostridium sensu stricto 1 spp., Actinomyces coleocanis, Streptococcus minor, an Enterococcus spp., and an uncultured Peptoclostridium spp.
Comparing human and canine urine microbiota
To further validate the use of dogs as model organisms for bladder cancer, we compared dog urine microbiota from our study to samples from the Bučvić Popović et al. study, which analyzed urine samples from 12 humans with UC and 11 healthy humans (Bučević Popović et al. (Citation2018)) (Table ). Although overall microbial composition differed significantly between dogs and humans, clear overlap between dog and human samples on a Weighted UniFrac Principal Coordinates Analysis plot (Figure a; S8 Table) demonstrated strong similarity between canine and human urine microbial community profiles. The wider differences observed in Unweighted UniFrac and Bray–Curtis plots indicate the presence of host-species specific microbial taxa (S10 Fig, S8 Table). However, these taxa were rarer compared to more dominant taxa that were similar between dogs and humans (Figure b). Urine microbial diversity was also similar between healthy dogs and humans (Figure c).
Figure 8. Comparing human and dog urine microbiota. (a) Urine microbial composition (Weighted UniFrac) and (b) taxonomic profiles demonstrated clear similarities between canine and human samples despite statistical differences (Wilcoxon Rank pairwise comparisons - all p < 0.003). (c) Urine microbial diversity (Shannon Diversity Index) was similar between healthy dogs and humans.
Discussion
The purpose of our study was to characterize the urine and fecal microbiota in a naturally-occurring canine model of UC. We report decreased urine microbial diversity and altered urine microbial composition in dogs with UC compared to healthy controls. We did not detect significant differences in fecal microbiota between dogs with and without UC; although, Fusobacterium was increased in dogs with UC. We also report on the similarities between human and canine urine microbiota. These results provide a foundation for further exploring the role of microbes in UC in a highly relevant animal model.
Urine and fecal microbiota associated with UC
The higher concentrations of DNA found in urine from dogs with UC is likely host DNA from epithelial or tumor cells being sloughed into the urine. Notably, urine microbial read numbers did not differ significantly between dogs with and without UC indicating similar amplicon sequencing depths despite differences in DNA concentrations. (Notably, efforts to remove host DNA from UC urine samples prior to sequencing may be beneficial in future microbiome studies employing shotgun metagenomics to ensure that the run is not overwhelmed with host sequences.)
Besides DNA concentrations, we also observed significant differences in urine microbial diversity (Shannon) and composition (Unweighted UniFrac) between dogs with and without UC. In this study, urine microbial diversity was greater in healthy dogs as compared to dogs with UC, a finding that aligns with several studies on urine microbiota in humans with UC (Liu et al. (Citation2019); Chipollini et al. (Citation2020)). However, there are also studies in humans that report no differences in microbial diversity or decreased diversity in urine from healthy individuals as compared to those with UC (Wu et al. (Citation2018); Bi et al. (Citation2019); Pederzoli et al. (Citation2020); Bučević Popović et al. (Citation2018); Xu et al. (Citation2014); Hussein et al. (Citation2021); Zeng et al. (Citation2020)). Differences in microbial composition (Unweighted UniFrac) have also been reported in previous human studies on UC (Pederzoli et al. (Citation2020); Bučević Popović et al. (Citation2018); Chen et al. (Citation2021); Hussein et al. (Citation2021)). In this study, the four most abundant phyla in urine were Firmicutes, Actinobacteria, Bacteroides, and Proteobacteria. These phyla also dominate the urine microbiota in humans (Wu et al. (Citation2018); Pederzoli et al. (Citation2020); Bučević Popović et al. (Citation2018); Mai et al. (Citation2019); Hussein et al. (Citation2021); Mansour et al. (Citation2020)) and have been reported in previous studies on healthy dog urine (Burton et al. (Citation2017); Melgarejo et al. (Citation2021)). In humans, taxa associated with UC vary widely across studies, but Acinetobacter and Actinomyces have been found at increased abundances in patients with UC across at least three studies (Bi et al. (Citation2019); Xu et al. (Citation2014); Hussein et al. (Citation2021)). In this study, we did not see Acinetobacter or Actinomyces spp. increased in relation to UC, which may be due to small sample sizes and reduced power to detect differentially abundant taxa, or differences between human and canine urine microbiota, or lack of a true link between these taxa and UC.
In relation to fecal microbiota, we did not observe any significant differences in dogs with and without UC. However, intriguingly, Fusobacterium was increased in relative abundance (although not significantly) in urine and fecal samples of dogs with UC. One previous study on bladder cancer also reported increased Fusobacterium in the urine of individuals (human) with UC (Bučević Popović et al. (Citation2018)). Importantly, taxa in the phyla Fusobacteria are considered normal inhabitants of the canine gastrointestinal tract (Pilla and Suchodolski (Citation2020)); although, they are more typically associated with disease in humans. Studies in colorectal cancer have demonstrated direct links between Fusobacteria (Fusobacterium nucleatum) and carcinogenesis. Specifically, Fusobacterium nucleatum Fap2 protein can bind to host factor Gal-GalNAc which is overexpressed on tumor cells (Abed et al. (Citation2016)) - thereby localizing to tumors where Fap2 can impair host anti-tumor immunity (Abed et al. (Citation2016)). Fusobacterium nucleatum can also induce the host Wnt / beta-catenin pathway resulting in upregulated host cellular proliferation (Rubinstein et al. (Citation2019)). Future studies are needed to elucidate the potential role of Fusobacterium in bladder cancer.
Limitations of the current study include small sample size and varying diets and NSAID use amongst the dogs included in this study. While these factors could impact the microbiota, we limited the influence of many other variables by rigorously controlling for antibiotic use, history of chemotherapy or radiation, and co-morbidities including metabolic, gastrointestinal, and urogenital diseases and infections. We also age-, sex-, and breed-matched the dogs in our analyses to limit the impact of these demographic variables on the microbiota. Future studies with larger sample sizes and evaluating the impact of diet and NSAID use on urine and fecal microbiota will be important.
Microbiota shared between urine and fecal samples
Communication and migration of microbes between the gut and bladder can increase a host’s risk of UTIs and bacteriuria (Magruder et al. (Citation2019)). Microbes may migrate and ascend into the urogenital tract externally from the rectum / anus, or internally via the blood stream (Meštrović et al. (Citation2020); Łaniewski et al. (Citation2020)). In this study, 66 ASVs were shared between urine and fecal samples. Interestingly, ∼ 59% of those ASVs (39 / 66) are likely spore-formers (Bacilli, Clostridia, Negativicutes) suggesting that spore formation may more readily enable exchange of microbes between body niches (Galperin (Citation2013); Tetz and Tetz (Citation2017)). Among the microbes (ASVs) found in both urine and fecal samples, there were multiple potentially pathogenic taxa: Campylobacter spp., Helicobacter canis, Clostridiodes difficile, Clostridium baratii, Escherichia / Shigella spp., and Enterococcus spp. There were also a few taxa that have been associated with tumors or directly linked with tumor development or progression in gastrointestinal, oral, and genital cancers: Fusobacterium spp. and Porphyromonas spp. (Kostic et al. (Citation2013); Hale et al. (Citation2018); Mitsuhashi et al. (Citation2015); Ha et al. (Citation2015); Atanasova and Yilmaz (Citation2014); Walther-António et al. (Citation2016)). The shared presence of two Fusobacterium ASVs between urine and fecal samples is particularly of interest given the role of Fusobacterium in colorectal cancer.
Dogs as a model for studies on human urine microbiota
Dogs are already considered effective models for studies on bladder cancer (Knapp et al. (Citation2014); Knapp et al. (Citation2020)) and on the gut microbiota (Coelho et al. (Citation2018); Ericsson (Citation2019)). Dogs also offer many advantages over rodent models – from immune competency, to naturally-occurring cancer, to gut microbial responses that mirror human responses. Additionally, there is clear evidence that dogs and humans share microbes including microbes that can colonize the gut, and microbies that can cause urinary tract infections in both host species (Johnson and Clabots (Citation2006); Johnson et al. (Citation2008); Johnson et al. (Citation2008); Murray et al. (Citation2004); Song et al. (Citation2013); Misic et al. (Citation2015); Johnson et al. (Citation2000)). However, there is no study, to our knowledge, that has directly compared urine microbial communities between dogs and humans. Our novel results demonstrate that human and dog urine is dominated by similar taxa; although, host-specific taxa are also observed. This key finding bolsters the value of dogs as models for studying urine microbiota, and demonstrates that dogs and humans exhibit similar microbial niches within the urinary tract.
Conclusions
This pilot study is a novel investigation of urine and fecal microbiota in a canine model of UC. The dominant microbial taxa identified in canine urine and fecal samples were similar to those reported in humans, further supporting the value of dogs as a model for studies on bladder cancer and on the microbiota. Also, as in humans, altered microbial diversity and composition were observed in dogs with UC as compared to healthy controls. This suggests that the microbiota could play a role in UC development, progression, prognosis, or response to treatment, as has been observed in other cancers. Moreover, Fusobacterium was increased – albeit not significantly – in both urine and fecal samples of dogs with UC. Fusobacterium ASVs were also shared between urine and fecal samples. Taken together, these results provide support for the use of dogs as a model in UC microbiome studies. Additionally, these findings suggest that future work evaluating the role of Fusobacterium in UC, and the gut as a potential source of this Fusobacterium, may be warranted.
Author contributions
Conceptualization: Vanessa L. Hale, Deborah W. Knapp, and William C. Kisseberth.
Clinical sample collection, clinical care / monitoring, clinical data extraction: Deborah W. Knapp, Deepika Dhawan, and William C. Kisseberth.
DNA extraction: Chris Madden, Ryan Mrofchak, and Morgan V. Evans.
Data processing, analysis: Ryan Mrofchak, Morgan, V. Evans, Chris Madden, and Deborah W. Knapp.
Data interpretation and conclusions: Ryan Mrofchak, Vanessa L. Hale, Chris Madden, and Deborah W. Knapp.
Manuscript writing: Ryan Mrofchak, Vanessa L. Hale, and Chris Madden.
Manuscript editing: Chris Madden, Deepika Dhawan, Deborah W. Knapp, and William C. Kisseberth.
Preprint availability
The article has been published in BioRvix with the title ‘Urine and Fecal Microbiota in a Canine Bladder Cancer Model’ (Mrofchak et al. (Citation2021b)).
Supplemental Material
Download Zip (23.7 MB)Acknowledgements
We are grateful to all individuals involved in sample collection at Purdue University College of Veterinary Medicine (West Lafayette, IN, USA) and to the dogs and dog owners who participated in this study. We also acknowledge the Ohio Supercomputer Center (Columbus, Ohio, USA, established 1987) for computing resources used in this study.
Data accessibility
The data that support the findings of this study are openly available in SRA BioProject PRJNA763920 [https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA763920] and figshare [https://doi.org/10.6084/m9.figshare.19775449.v2].
Additional information
Funding
References
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