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Case Report

Case Report: Inflammation-Driven Species-Level Shifts in the Oral Microbiome of Refractory Feline Chronic Gingivostomatitis

by
Claire A. Shaw
1,
Maria Soltero-Rivera
2,*,
Rodrigo Profeta
1 and
Bart C. Weimer
1,*
1
Department of Population Health and Reproduction, 100K Pathogen Genome Project, University of California, Davis, CA 95616, USA
2
Department of Surgical and Radiological Sciences, University of California, Davis, CA 95616, USA
*
Authors to whom correspondence should be addressed.
Bacteria 2025, 4(1), 1; https://doi.org/10.3390/bacteria4010001
Submission received: 25 September 2024 / Revised: 18 November 2024 / Accepted: 29 November 2024 / Published: 2 January 2025
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Abstract

:
The cat oral microbiome plays an important role in maintaining host health, yet little is known about how to apply microbial data in a clinical setting. One such use of microbiome signatures is in cases of feline chronic gingivostomatitis (FCGS), a severe debilitating complex disease of the oral cavity. FCGS-afflicted cats have limited treatment options, and individual patient responses to treatment are needed. In this work, we used deep sequencing of total RNA of the oral microbiome to chronicle microbial changes that accompanied an FCGS-afflicted cat’s change from treatment-non-responsive to treatment-responsive within a 17-month span. The oral microbiome composition of the two treatment-non-responsive time points differed from that of the treatment-responsive point, with notable shifts in the abundance of Myscoplasmopsis, Aspergillus, and Capnocytophaga species. Intriguingly, the presence of the fungal groups Aspergillus and Candida primarily differentiated the two non-responsive microbiomes. Associated with responder status were multiple Capnocytophaga species, including Capnocytophaga sp. H2931, Capnocytophaga gingivalis, and Capnocytophaga canimorsus. The observation that the oral microbiome shifts in tandem by response to treatment in FCGS suggests a potential use for microbiome evaluations in a clinical setting. This work contributes to developing improved molecular diagnostics for enhanced efficacy of individualized treatment plans to improve oral disease.

1. Introduction

The cat oral cavity contains a complex set of microbial residents, termed the oral microbiome, that work in concert with one another and the host [1,2,3]. In healthy cats, the oral microbiome is typically dominated by diverse commensal organisms that coexist in a delicate balance with the host’s tissues and immune system. These include primarily taxa from Bacteroidetes and Proteobacteria, along with Firmicutes and Actinobacteria, among others [4,5,6]. These beneficial microbes play a crucial role in maintaining oral health, preventing the overgrowth of pathogenic organisms, and modulating the host’s immune response [7,8]. The composition and function of the healthy feline oral microbiome are essential for maintaining a commensal relationship between the host and its resident microbes, as well as preventing the development of severe oral diseases, including feline chronic gingivostomatitis (FCGS).
FCGS is a debilitating chronic inflammatory disease of the oral mucosa. While this disease affects up to 26% of cats, little is known about the etiology of the disease or how best to treat it [9,10,11]. Current treatments involve more palliative and empirical approaches that address clinical signs but often fail to treat the underlying source of the inflammation and tissue deterioration [12]. The lack of effective treatments available to clinicians is partly due to the disease’s unknown origin, the complexity of disease presentation, and the inability to predict how different patients will respond. Thus, the development of effective treatments requires additional research to identify the origin of FCGS but will also likely necessitate an approach that considers individual variations that affect response to treatment.
In individualized treatment approaches, which integrates personalized treatment plans, understanding the microbiomes associated with disease state and treatment response will be imperative as the importance of the oral microbiome in regulating host health is becoming increasingly apparent [2,8,13,14,15,16,17]. Disruptions to this delicate equilibrium, whether due to dietary changes, stress, antibiotics, or other factors, can lead to a dysbiotic community [18,19]. This microbial imbalance can trigger an inflammatory response characterized by increased levels of proinflammatory cytokines and infiltration of immune cells [20,21]. Dental inflammatory diseases left unmanaged in humans can ultimately result in the development or exacerbation of various oral diseases, such as periodontal disease, resorptive lesions, and other systemic changes, which is a likely case for cat dental health as well [15,22,23]. Several microbial species have been implicated in developing these oral pathologies in cats. For example, periodontal pathogens like Peptostreptococcaceae spp., Porphyromonas spp., Fillifactor villosus, Fusobacterium nucleatum, Moraxella spp., and Neisseria have been associated with the progression of periodontal disease, which is characterized by inflammation, gingival recession, and alveolar bone loss. However, in diseases without identified periodontal pathogens, like FCGS [17], a more holistic survey of the oral microbiota is required as the best treatment approach.
This study presents a longitudinal examination of the oral microbiome in a cat with FCGS that received full-mouth extractions as treatment. Notably, the cat initially showed partial improvement in oral inflammation following full-mouth extraction (FME), experienced a recurrence of the disease, and then achieved remission after corticosteroid treatment over a 17-month period. Using deep sequencing of total RNA from buccal swabs collected during routine visits, we investigated changes in the oral microbiome at the species level. This provided an unprecedented look into how the complex microbial community may mark the shift between refractory and responder status. Uniquely, this work applied deep-sequencing approaches to analyze total RNA from the cat’s oral microbiome. The use of RNA ensured the identification of actively growing microbes, while deep sequencing allowed for detecting microbes at the species and strain level and in low abundance. Together, these methods provided a vibrant picture of how the oral microbiome changed with treatment status. Additionally, the non-invasive swab sampling technique used here lends itself to clinical application, suggesting that the diagnostic use of microbial signatures may be feasible on a broader scale. In summary, we investigated the species-level microbial shifts that accompanied the change from refractory to responder in a cat with FCGS. While further validation is needed with larger populations, this work supports the idea that changes in the oral microbiome may serve as markers of treatment response or as predictors of patient outcomes.

2. Case Description

A 4.5-year-old male castrated domestic shorthair was presented for dental care at a Northern California Veterinary Center with a nine-month history of stomatitis. The patient had tested positive for feline calicivirus (FCV) but was negative for feline immunodeficiency (FIV) and leukemia (FeLV) viruses. A diagnosis of feline chronic gingivostomatitis (FCGS) was initially made by the primary care veterinarian via visual inspection of severe inflammation of the oral mucosa lateral to the palatoglossal folds (Figure 1). The FCGS diagnosis was later confirmed with histopathology at our institution, which noted chronic plasmocytic and pleocellular stomatitis with segmental epithelial hyperplasia erosion and ulceration. No other systemic diseases or evidence of oral neoplasia were noted. Before the first dental visit, the cat had been treated with clindamycin and amoxicillin clavulanate potassium (the dose, route, and duration of these were not indicated). The patient was kept indoors, with one other cat in the household that did not have FCGS, was offered a free choice of dry food as well as wet food and Greenies® as a treat.
Full-mouth extractions (FME) were performed, and post-operative radiographs confirmed vacated alveoli. Three months after this procedure, the client noted redness and swelling in the back of the mouth while the patient yawned. At that time, the patient had some sneezing, though appetite was good. Erythema was noted lateral to the palatoglossal folds bilaterally (Figure 1B). Moderate proliferative erythema was also noted bilaterally in the sublingual area. The inflammation was noted to be symmetrical but markedly reduced in extent as compared to before FME treatment (Figure 1A). The determination to start immunosuppressive therapy was made eight months post FME as the patient started having difficulty swallowing wet and dry food. Oral evaluation during this visit showed severe proliferative erythematous tissues seen bilaterally in the sublingual area and lateral to the palatoglossal folds, with the left side being worse, as evidenced by the presence of serosanguinous discharge (pictures of this recheck unavailable). Despite these findings, weight gain from 5.1 to 5.5 kg was reported over that time. Prednisolone was started at 0.5 mg/kg by mouth every 12 h. Four months after the start of prednisolone, the patient was no longer dysphagic, had further weight gain of 0.3 kg, and only mild erythema lateral to the palatoglossal folds, which was bilateral and symmetrical, was noted on oral examination; thus, we started tapering steroid treatment. Inflammation plateaued six months later and was very mild in focal areas (Figure 1C); consequently, tapering was continued, and prednisone was completely discontinued six months later.
A swab of the caudal buccal mucosa was taken during each of three visits. The cat initially presented with FCGS and was non-responsive to tooth extraction during the collection of the first and second swabs; however, the cat showed signs of symptomatic resolution at the third examination and swab collection. The first swab was collected three months post full-mouth tooth extraction when there was some resolution of clinical signs, the second swab was collected eight months post full-mouth tooth extraction and was followed by prednisolone treatment due to regression in clinical improvement, with the third swab collected 10 months after initiation of prednisolone treatment and six months into prednisolone tapering. The collection and study design were reviewed and approved by the University of California-Davis Institutional Animal Care and Use Committee (IACUC) and signed owner consent was obtained before sampling.
The same method was applied to all three swabs (Figure 2). In brief, the oral mucosa lateral to the palatoglossal folds was swabbed using a cytobrush (FLOQSwabs, Coplan, Italy, EU). The swab was then placed in a sterile conical tube that contained 500 μL of DNA/RNA Shield (Zymo, Irvine, CA, USA), vortexed, and stored at −20 °C until processing. Bacterial cells were enzymatically lysed according to the protocol used by the 100K pathogen project [24], and then RNA was isolated using Trizol LS (Ambion, Austin, TX, USA) according to manufacturer instructions. RNA sequencing libraries were prepared as described previously [25,26,27], with RNA purity and integrity confirmed using TapeStation (Agilent Technologies Inc., Santa Clara, CA, USA). Sequencing libraries were constructed using the enzymatic-based KAPA HyperPlus Library Preparation kit (KK8514) (Kappa Biosystems, Wilmington, MA, USA) on a PerkinElmer Sciclone G3 (PerkinElmer Inc. Waltham, MA, USA) and sequenced on an Illumina NovaSeq S4 (Illumina, San Diego, CA, USA).
Shotgun metatranscriptomics sequence data were processed as described previously [27]. Trimmomatic (version 0.39) [28] was first used to remove low-quality sequences and sequencing adapters. Subsequently, sequence data quality was reviewed with FastQC (version 0.11.9) [29]. Kraken2 with a microbial reference database, using standard settings (k-mer size = 35), was used to assign taxonomy. Bracken (version 2.6.1) [30] was then used to estimate the relative proportion of respective taxa at the species level according to previously used methods [25]. Expression of antimicrobial resistance (AMR) genes was determined by running Trinity (v2.15.1) [31] assembled reads through the Comprehensive Antimicrobial Resistance Database (CARD, built 10 August 2023) [32]. Virulence factor expression was evaluated using the Virulence Factor Database (VFDB, built 10 August 2023) [33]. Plots were made using R (Version 4.2.3) in tandem with Inkscape (Version 1.0) and accessed via GitHub (https://github.com/inkscape/inkscape).

3. Results

Sampling of the oral microbiome across three time points revealed changes in the microbiome composition were associated with both times between sampling and treatment response status (Figure 3). A total of 5228 species were found, with 362 of these species shared amongst all surveyed time points and treatment statuses (Figure 3). The initial sample (Refractory_1) and final sample (Responder) had the most species in common, at 1129 shared, while the second refractory sample (Refractory_2) had just 20 species in common with the responder microbiome and 190 with the initial refractory sample. Perhaps most intriguingly, the microbiome associated with being responsive to treatment had the most unique organisms, at 897 species.
To investigate the microbial shifts that accompanied treatment response, we examined the microbiome at both the genus and species taxonomic level and further compared the aggregated refractory microbiomes to the microbiome associated with responder status (Figure 4). In the initial sample, where the patient was refractory, the oral microbiome was dominated by Mycoplasmopsis (Figure 4A). Capnocytophaga, Fusobacterium, Pasteurella, Porphyromonas, and Ureaplasma were also among the genera found, each of which accounted for 10% or more of the microbial population in the microbiome of the refractory samples. Beyond bacterial members, the fungal group Aspergillus was also identified as a relatively large proportion of the initial refractory microbiome. Aspergillus displayed a notable expansion in the second refractory measurement. Other fungal genera, including Candida and Kluveromyces, also increased between the first and second refractory point.
The potential importance of fungal species in treatment response status was further supported by directly comparing the refractory microbiomes to the responder microbiome on a correlation plot (Figure 4B). Aspergillus oryzae, Candida dublienesis, and Psilocybe cuebensis all stand out as fungal species that were present in both refractory and responder-associated microbiomes but were present in different proportions. All three fungal species were highly abundant in the refractory microbiome and conversely lowly abundant in the responder microbiome without anti-fungal treatment. Additionally supporting a role for fungal species is the presence of Kluyveromyces marxianus and Drechmeria coniospora as high-abundance organisms unique in the refractory microbiome. Unique to the responder microbiome in high abundance were primarily bacterial species like Neisseria animalis and Kingella dentrificans. The diffuse pattern of organisms around the shared abundance diagonal line and the relatively large number of species unique to each microbiome, though all sampled in the same cat, indicate notable shifts in community composition between being non-responsive to treatment and becoming responsive.
To understand whether the shifts in microbial abundance across the different refractory and responder states were accompanied by clinically relevant changes in microbial gene expression, the expression of AMR and virulence factors was investigated. Assembled transcripts for both refractory samples and the responder samples were examined with AMR and virulence factor databases, but no genes emerged with significant expression across any of the three samples. The lack of significantly expressed AMR or virulence-related genes in these longitudinal samples pointed to the potential importance of broader community interactions accompanied with metabolic functional shifts associated with disease changes.
Considering the potential clinical importance of broader microbial shifts, the bacterial versus eukaryotic load in each microbiome was plotted to further investigate whether the carriage of bacteria versus fungi in the oral microbiome differentiated the microbiome refractory from that of treatment response situations. The two refractory microbiomes were averaged to deduce any major differences between responder status microbial signatures and limit time’s effect on community composition (Figure 5). The aggregated refractory microbiomes carried eukaryotic species, primarily fungal species, at 34%, while the responder microbiome comprised 22% eukaryotic organisms. The noted difference between eukaryotic populations in the different treatment response-associated microbiomes within a single cat is a notable finding from this case study and warrants further work in a broader population.

4. Discussion

Periodontal pathogens such as P. gingivalis are increasingly implicated in the onset and exacerbation of dental diseases. While targeted investigations and interventions of periodontal pathogens may alleviate select diseases, not all diseases of the oral cavity have been connected directly to the outgrowth of a pathogen. In diseases without clear pathobionts, as with FCGS, a broader investigation of the oral microbiome composition is necessary to understand disease etiology and clinical responses. Elucidating the role of the microbiome and how it may be used to predict treatment responses in FCGS is an essential goal of veterinary dentistry.
Cats with FCGS face a decreased quality of life, as many display inflammation so severe it impedes normal eating and drinking. Complicating the treatment of these cats is the limited number of therapies coupled with varying responses across individual patients, with no obvious pattern to predict treatment response. To provide a potential roadmap for predicting cat response to FCGS treatment, we sampled the oral microbiome of a single cat that switched from refractory to treatment-responsive over 17 months. Deep sequencing of total RNA improved our ability to mine microbial signatures of disease and treatment response in this cat. This approach captured active microbial members and resolved community membership to the species and strain taxonomic level. This unique in-depth view of a changing oral microbiome of FCGS in a cat revealed that shifts in community membership parallel treatment response, indicating a potential use for microbial signatures in veterinary dentistry.
In healthy cats, the oral microbiome works synergistically to degrade dietary components, regulate oral pH, and communicate with host tissues and immune cells. This active microbial community comprises Pasteurella, Porphyromonas, Moraxella, and Fusobacterium, as well as some dominant species like Capnocytophaga canimorsus [2,34]. Primary colonizers of the oral cavity attach to host structures like teeth and recruit other microorganisms into a co-operative biofilm community, allowing the interspecies and interkingdom exchange of metabolites [35]. In healthy oral cavities, these biofilms work with host tissues to dampen proinflammatory cascades and prevent pathogenic overgrowth via colonization resistance [20]. When the delicate balance of host–microbe and microbe–microbe interactions is disrupted, as in antibiotic administration or disease onset cases, the microbial community shifts in response [36]. The shift away from homeostatic regulation of the microbial community invites the outgrowth of opportunistic pathogens and can contribute to the perpetuating inflammatory cycles in host tissues [37,38].
The microbes identified in the course of this work are some of these opportunists known to prey on the weakened defenses of the host and that capitalize on dysbiotic microbiota. Mycoplasmopsis (Mycoplasma) felis, for instance, is a known pathogen of the upper respiratory tract in cats [39,40,41] and was a dominant organism in this study’s first non-responsive sampling point. In addition to respiratory diseases, M. felis has also been associated with arthritis and conjunctivitis [42,43]. While the incidence of M. felis in the oral cavity of FCGS-afflicted cats is unknown, its notable abundance correlated to being non-responsive to treatment. This suggests the potential involvement of a respiratory pathogen in negating the positive effects of tooth extraction in FCGS. The presence of M. felis in the first sampling point may also indicate the opportunistic growth of a pathogenic organism after an invasive dental procedure.
This cat’s microbiome displayed a defined composition shift between the first non-responsive sampling point and the second. Interestingly, the second refractory-associated microbiome had an increase in fungal species, including Aspergillus oryzae and Candida albicans. The dominant existence of Aspergillus in a FCGS-afflicted cat’s oral microbiome may be related to the fungus’ ability to secrete large amounts of hydrolytic enzymes [44,45]. Hydrolytic enzymes in the oral cavity have been shown to break down biofilm matrices and are readily found in salivary secretions from the host [45,46]. The marked abundance of hydrolytically active A. oryzae in the second refractory suggests interkingdom interaction is driving microbial shifts, perhaps even towards a community that pushes host tissues to be more malleable to treatment. In general, fungal species stood out as an altered kingdom between refractory and responder status. The finding here that fungi like Aspergillus, Candida, and Kluveromyces were enriched in refractory status as compared to responder status supports previous studies suggesting a role for fungus in FCGS symptoms [2] and further suggests the potential use of these species as predictors of patient outcomes pending further validation [47,48,49]. Steroid treatment has shown complete or marked improvement in 54% of FCGS patients, as demonstrated in a double-blind study comparing cats on recombinant feline interferon omega (n = 24) and prednisolone (n = 15) at a tapering anti-inflammatory dose [50].
Though exploration of the microbiome in a cat with FCGS across treatment response status provides insight into potential microbial signatures of clinical outcome, the impact of individual host differences cannot be overlooked. Previously, we characterized the differing responses in host tissue from healthy cats compared to FCGS-afflicted ones undergoing tooth extractions or stem cell therapy [10]. This study’s results indicate that differential regulation of the PI3K/AKT and SAP/JNK signaling pathways may be linked to treatment response outcomes. As highlighted in this current work, shifts in the microbiota can likewise be correlated to treatment response and further studies with a larger samples size are warranted. Taken together, it is possible that a curated combination of host and microbe signatures will be necessary to formulate effective personalized treatment plans best.

5. Conclusions

Shifts in the oral microbiome accompanying changes in disease status and treatment response can serve as critical clinical markers for predicting patient outcomes. As revealed in this work, the oral microbiome of a cat with FCGS shifted in conjunction with the cat’s responder status to treatment. At the genus level, Mycoplasmopsis and Aspergillus dominated the microbiome when non-responsive to treatment, while Capnocytophaga species were significantly increased when the cat was treatment-responsive. While further exploration is needed to confirm whether these microbial changes are unique to an individual cat, the identification of unique microbial signatures in refractory and responder states suggests microbiome markers will help clinicians predict patients’ responses to treatment before application. Integrating microbiome assessments in clinical care may pave the way for more personalized and effective care in cases of FCGS and other dental diseases.

Author Contributions

C.A.S. analyzed the data, created the visualizations, and wrote and edited the original manuscript. M.S.-R. conceptualized the work, collected samples, and wrote and edited the manuscript. R.P. analyzed the data, contributed to the visualizations, and edited the manuscript. B.C.W. conceptualized the work and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All study procedures were reviewed and approved by the University of California-Davis Institutional Animal Care and Use Committee 19881 (4 May 2017), 19170 (28 January 2016), 18476 (21 November 2014).

Informed Consent Statement

We received the owner’s consent for all sampling procedures.

Data Availability Statement

Please contact the corresponding authors for data presented in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Clinical pictures of the oral cavity across three visits spanning 17 months. Panel (A) depicts the patient at initial diagnosis of feline chronic gingivostomatitis (FCGS). Panel (B) depicts substantial improvement seen three months post FM extraction; however, inflammation recurred 5 months later, requiring the start of immunosuppressive therapy. Panel (C) depicts the transition to responsive and adequately managed with prednisolone treatment 10 months into the treatment and 6 months into tapering with further tapering recommended.
Figure 1. Clinical pictures of the oral cavity across three visits spanning 17 months. Panel (A) depicts the patient at initial diagnosis of feline chronic gingivostomatitis (FCGS). Panel (B) depicts substantial improvement seen three months post FM extraction; however, inflammation recurred 5 months later, requiring the start of immunosuppressive therapy. Panel (C) depicts the transition to responsive and adequately managed with prednisolone treatment 10 months into the treatment and 6 months into tapering with further tapering recommended.
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Figure 2. Overview of study methods. A single cat was seen during three clinical examinations across three years. During the first two examinations, the cat was classified as non-responsive or refractory for tooth extractions. The third visit saw the cat reclassified as responsive due to remission of inflammatory symptoms. Total RNA was extracted from each of the three swab samples, sequenced, and assigned taxonomy at the species level.
Figure 2. Overview of study methods. A single cat was seen during three clinical examinations across three years. During the first two examinations, the cat was classified as non-responsive or refractory for tooth extractions. The third visit saw the cat reclassified as responsive due to remission of inflammatory symptoms. Total RNA was extracted from each of the three swab samples, sequenced, and assigned taxonomy at the species level.
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Figure 3. Oral microbiome composition shifts both across sampling time and by treatment response status. Venn diagram comparing the shared and unique species across the three sampling points which encompassed two refractory points and a shift to responder status.
Figure 3. Oral microbiome composition shifts both across sampling time and by treatment response status. Venn diagram comparing the shared and unique species across the three sampling points which encompassed two refractory points and a shift to responder status.
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Figure 4. Microbial population shifts in the microbiome correlate to refractory or responder status in the oral microbiome of a cat with FCGS. (A) Alluvial plot displaying genus composition on the strata and species changes on the alluvia. Organisms with less than 10% proportion in the microbiome were collapsed into the ‘Mixed’ box. (B) Correlation plot illustrating the shared and unique organisms in the aggregated refractory microbiome compared to the responder microbiome. Each dot represents a single organism, with the center diagonal indicating equal abundance in the two groupings. The red line on the top indicates organisms that are unique to the responder condition. The teal line to the right indicates the organisms unique to refractory condition. The boxes on each of those lines are expansions to provide names of top 10 individual organisms in that condition.
Figure 4. Microbial population shifts in the microbiome correlate to refractory or responder status in the oral microbiome of a cat with FCGS. (A) Alluvial plot displaying genus composition on the strata and species changes on the alluvia. Organisms with less than 10% proportion in the microbiome were collapsed into the ‘Mixed’ box. (B) Correlation plot illustrating the shared and unique organisms in the aggregated refractory microbiome compared to the responder microbiome. Each dot represents a single organism, with the center diagonal indicating equal abundance in the two groupings. The red line on the top indicates organisms that are unique to the responder condition. The teal line to the right indicates the organisms unique to refractory condition. The boxes on each of those lines are expansions to provide names of top 10 individual organisms in that condition.
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Figure 5. Refractory and responder microbiomes differ in the proportion of eukaryotic and bacterial microorganisms. Kingdom labels from the Kraken2 assignment were used to plot the relative abundance of bacteria and of eukaryotic organisms in the aggregated refractory associated microbiome (left) and in the responder microbiome (right). Cat host reads were removed prior to taxonomic assignment of microbes and thus are not represented in the eukaryotic load plotted here.
Figure 5. Refractory and responder microbiomes differ in the proportion of eukaryotic and bacterial microorganisms. Kingdom labels from the Kraken2 assignment were used to plot the relative abundance of bacteria and of eukaryotic organisms in the aggregated refractory associated microbiome (left) and in the responder microbiome (right). Cat host reads were removed prior to taxonomic assignment of microbes and thus are not represented in the eukaryotic load plotted here.
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MDPI and ACS Style

Shaw, C.A.; Soltero-Rivera, M.; Profeta, R.; Weimer, B.C. Case Report: Inflammation-Driven Species-Level Shifts in the Oral Microbiome of Refractory Feline Chronic Gingivostomatitis. Bacteria 2025, 4, 1. https://doi.org/10.3390/bacteria4010001

AMA Style

Shaw CA, Soltero-Rivera M, Profeta R, Weimer BC. Case Report: Inflammation-Driven Species-Level Shifts in the Oral Microbiome of Refractory Feline Chronic Gingivostomatitis. Bacteria. 2025; 4(1):1. https://doi.org/10.3390/bacteria4010001

Chicago/Turabian Style

Shaw, Claire A., Maria Soltero-Rivera, Rodrigo Profeta, and Bart C. Weimer. 2025. "Case Report: Inflammation-Driven Species-Level Shifts in the Oral Microbiome of Refractory Feline Chronic Gingivostomatitis" Bacteria 4, no. 1: 1. https://doi.org/10.3390/bacteria4010001

APA Style

Shaw, C. A., Soltero-Rivera, M., Profeta, R., & Weimer, B. C. (2025). Case Report: Inflammation-Driven Species-Level Shifts in the Oral Microbiome of Refractory Feline Chronic Gingivostomatitis. Bacteria, 4(1), 1. https://doi.org/10.3390/bacteria4010001

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