WO2025166347A1

WO2025166347A1 - Canine skin microbiome assessment and treatment

Info

Publication number: WO2025166347A1
Application number: PCT/US2025/014317
Authority: WO
Inventors: Michaella WHITTLE; Juan CASTILLO-FERNANDEZ; Gregory AMOS; Phillip Watson
Original assignee: Mars Inc
Current assignee: Mars Inc
Priority date: 2024-02-02
Filing date: 2025-02-03
Publication date: 2025-08-07
Anticipated expiration: 2026-08-02

Abstract

Methods for determining the skin health status of an animal through the compositional analysis of the skin and gut microbiome are disclosed herein. The abundance or relative abundance of one or more microbial taxa is assayed using PCR, quantitative PCR, DNA sequencing, or shotgun metagenomics sequencing, or 16S amplicon sequencing.

Description

CANINE SKIN MICROBIOME ASSESSMENT AND TREATMENT

CROSS REFERENCE

This application claims priority to U.S. Provisional Application No. 63/549,288 filed February 2, 2024 and U.S. Provisional Application No. 63/706,992 filed October 14, 2024, the content of which are incorporated by reference in their entireties.

SEQUENCE LISTING

A Sequence Listing conforming to the rules of WIPO Standard ST.26 is hereby incorporated by reference. Said Sequence Listing has been filed as an electronic document via PatentCenter encoded as XML in UTF-8 text. The electronic document, created on January 28, 2025, is entitled “069269.0729_ST26.xml”, and is 5,943 bytes in size.

FIELD

The presently disclosed subject matter relates to the compositional analysis of the canine skin and gut microbiome as a monitoring and diagnostic tool for canine skin disease. The presently disclosed subject matter further relates to methods of improving the skin microbiome of a subject in need thereof.

BACKGROUND

The skin provides an interface between the external environment and an individual. It acts as a physical, immunological, microbial barrier; a sensory organ; plays an important role in body temperature regulation; and protects the body from dehydration. The skin microbiome is the collection of microorganisms living on the skin and recent research has demonstrated across a range of hosts that it is important for maintaining health, such as through modulating the innate immune response, preventing colonisation from pathogens, and ensuring optimal skin function. Many factors influence the microbial composition of the skin, such as host genetic variation, lifestyle, hygiene, and the environment. In both humans and dogs, alterations in the skin microbial composition have been associated with skin conditions, such as psoriasis and atopic dermatitis, although it remains unclear whether these changes are cause or effect. There are links between altered epidermal barrier function and dysbiosis of the normal skin microbiome.

In dogs, skin sites can be categorized into two groups based on physiology, haired skin sites and mucosal surfaces/mucocutaneous junctions. However, as with the skin of most animals, the canine skin is covered mostly by dense fur with apocrine glands distributed throughout their bodies. The eccrine glands which produce sweat are found only on a dog’s paw and, in comparison to humans, dogs have a more even distribution of sebaceous glands throughout their body. Sebaceous glands produce lipids, such as cholesterol, which are involved in hair coat sheen and softness.

To date, studies of the canine skin microbiota have focused on 16S rRNA high- throughput sequencing to describe the taxonomic profile of healthy dogs and those with skin conditions, such as atopic dermatitis. Across these studies, the most dominant phyla observed across different skin sites are the Proteobacteria, with Firmicutes, Actinobacteria and Bacteroidota observed to a lesser extent. Studies have described the skin microbiota of multiple different skin sites including interdigital region of the paw, axilla, concave pinna, ear canal, dorsal lumber, conjunctiva, groin, perianal skin, chin, nasal and abdomen. Studies comparing the skin microbial communities of healthy dogs with those diagnosed with atopic dermatitis, report a reduction in Shannon diversity and increased proportions of Staphylococcus pseudintermedius in dogs with atopic dermatitis in comparison to healthy dogs. In dogs with atopic dermatitis a reduction in the relative abundance of S. pseudintermedius has been observed in correlation with decreased severity of atopic dermatitis clinical signs.

Canine skin microbiota differences between individuals and differences between skin sites, particularly those between mucosal surface and mucocutaneous junctions have been observed throughout these studies. Descriptions of the canine skin fungal communities have also been conducted, using internal transcribed spacer region 1 or 2 (ITS1 or ITS2) high throughput sequencing, with the most common fungi present Alternaria and Cladosporium regardless of health status or body site.

A limited number of studies have investigated factors influencing shifts in the microbial composition of the skin of healthy dogs. Seasonal changes and cohabitation with other dogs have been shown to significantly alter skin bacterial communities with dogs living together, in the same household, having a more similar bacterial profile than that of dogs living separately. In addition to season of sampling, geographical origin, hygiene status of living environment, access to outdoor environments and life stage have been shown to shape the skin microbiota of dogs. Additionally, there is some evidence to suggest that diet can influence skin microbial communities with increased alpha diversity reported when dogs are fed a fresh diet in comparison to a dry diet. Specific bacterial groups were observed to change in abundance with increased abundance of Staphylococcus and decreased abundance of Porphyromonas and Cory neb acterium noted in dogs fed fresh diet in comparison to dry diet.

Studies to date have given a good overview of the taxonomic profile of the canine skin. However, the function and structure of the skin microbiome is still unknown, despite this being critical to understanding the role of the microbiome in skin health. The presently disclosed subject matter addresses this unmet need by providing a comprehensive assessment of the skin microbiome using metagenomic sequencing to give clear insights into the species and strains present on the canine skin, as well as the associated functions that they are fulfilling.

SUMMARY OF THE INVENTION

The presently disclosed subject matter provides a method for determining skin health status of an animal comprising quantifying one or more microbial taxa from a sample to determine abundance or relative abundance of the one or more microbial taxa and determining the skin health status of the animal. In certain embodiments, the method further comprises providing to the animal a topical treatment or shampoo which is formulated to improve the skin health status when the health status is “not health” or “skin disease”. In certain embodiments, determining the skin health status of the animal comprises comparing the abundance or relative abundance of the one or more microbial taxa with a reference abundance or relative abundance of the one or more microorganism. In certain embodiments, the reference abundance or relative abundance of the one or more microorganism corresponds to the abundance or relative abundance of the one or more microorganisms in one or more healthy animal. In certain embodiments, the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 14. In certain embodiments, the reference abundance or relative abundance of the one or more microorganism corresponds to the abundance or relative abundance of the one or more microorganisms in one or more animal with skin disease. In certain embodiments, the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 9. In certain embodiments, the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 17. In certain embodiments, the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 24. In certain embodiments, the sample exhibits an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa selected from the group consisting of Firmicutes A sp., Clostridia class, Phocaeicola vulgatus, Ruminococcus B genus, Escherichia genus, Bacteroides slercoris. Escherichia sp., Bacteroides uniformis, Terrisporobacter genus, and combinations thereof. In certain embodiments, the sample exhibits a reduction, decreased abundance, or decreased relative abundance of one or more microbial taxa selected from the group consisting of Prevotella genus, Prevotella copri. Prevotellamassilia genus, Prevotellamassilia sp000437675, Catenibacterium genus, Catenibacterium sp000437715, Prevotella sp., Acidaminococcales order, Acidaminococcaceae family, Phascolarctobacgerium A genus, and combinations thereof. In certain embodiments, the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 26. In certain embodiments, the sample exhibits an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa selected from the group consisting of Bacteroidia class, Bacteroidota phylum, Staphylococcaceae family, Staphylococcus genus, Actinobacteria class, Actinobacteriota phylum, Staphylococcales order, Bacteroides pyrogenes, Bacillales order, Bergeyella zoohelcum, Kocuria rhizophila, Porphyromonas cangingivalis, Staphylococcus schleiferi, Rhizobiaceae family, Acinetobacter radiore sistens, Actinomycetales order, Actinomycetaceae family, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium genus, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium sp., Capnocytophaga genus, Staphylococcus xylosus, Flavobacterium sp., Flavobacterium genus, Cutibacterium sp., Nocardioidaceae family, Nocardioides genus, Aureimonas sp., Auerimonas genus, Porphyromonas gingivicanis sp., and combinations thereof. In certain embodiments, the sample exhibits a reduction, decreased abundance, or decreased relative abundance of one or more microbial taxa selected from the group consisting of Clostridia class, Enterob acteriaceae family, Streptococcus mitis, Acinetobacter johnsonii, and combinations thereof.

In certain embodiments, the skin health status is “not health” or “skin disease” when the relative abundance of the one or more microbial taxa in the sample is less than the expected minimum relative abundance shown in Figure 9, or greater than the expected maximum relative abundance shown in Figure 9. In certain embodiments, the one or more microbial taxa is measured using PCR, qPCR, DNA sequencing, or shotgun metagenomics sequencing. In certain embodiments, the abundance, presence, or relative abundance of the one or more microbial taxa is determined by amplifying or sequencing 16S rRNA, or variable regions of 16S rDNA. In certain embodiments, the one or more microbial taxa is associated with dermatitis, psoriasis, atopic dermatitis, cutaneous form of food allergy, pruritic diseases, bacterial folliculitis, furunculosis, allergic dermatitis, pyoderma, mange, and immune or auto-immune dermatitis. In certain embodiments, the animal is a domestic animal. In certain embodiments, the domestic animal is a dog. In certain embodiments, the sample is obtained from a conscious animal or from an unconscious animal. In certain embodiments, the animal has or is suspected to have dermatitis, psoriasis, atopic dermatitis, cutaneous form of food allergy, pruritic diseases, bacterial folliculitis, furunculosis, allergic dermatitis, pyoderma, mange, and immune or auto-immune dermatitis. In certain embodiments, the animal is suspected to have a skin disease or disorder due to excessive scratching or licking. In certain embodiments, the skin health status comprises a skin disease or disorder. In certain embodiments, the one or more microbial taxa is present in a sample. In certain embodiments, the method further comprises extracting nucleic acid from the sample. In certain embodiments, the nucleic acid is DNA. In certain embodiments, the nucleic acid is RNA.

The presently disclosed subject matter further provides a method of improving the skin microbiome of a subject in need thereof, comprising administering a topical treatment or shampoo which is formulated to improve the skin microbiome, wherein the skin microbiome of the subject exhibits: a) an abundance or relative abundance of one or more microbial taxa that is less than the expected minimum relative abundance shown in Figure 9; b) an abundance or relative abundance of one or more microbial taxa that is greater than the expected maximum relative abundance shown in Figure 9; c) an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa selected from the group consisting of Bacteroidia class, Bacteroidota phylum, Staphylococcaceae family, Staphylococcus genus, Actinobacteria class, Actinobacteriota phylum, Staphylococcales order, Bacteroides pyrogenes, Bacillales order, Bergeyella zoohelcum, Kocuria rhizophila, Porphyromonas cangingivalis, Staphylococcus schleiferi, Rhizobiaceae family, Acinetobacter radioresislens, Actinomycetales order, Actinomycetaceae family, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium genus, Allorhizobium- Neorhizobium-Pararhizobium-Rhizobium sp., Capnocytophaga genus, Staphylococcus xylosus, Flavobacterium sp., Flavobacterium genus, Cutibacterium sp., Nocardioidaceae family, Nocardioides genus, Aureimonas sp., Auerimonas genus, Porphyromonas gingivicanis sp., and combinations thereof; or d) a reduction, decreased abundance, or decreased relative abundance of one or more microbial taxa selected from the group consisting of Clostridia class, Enterobacteriaceae family, Streptococcus milts, Acinetobacter johnsonii, and combinations thereof. In certain embodiments, the animal is a domestic animal. In certain embodiments, the domestic animal is a dog. In certain embodiments, the one or more microbial taxa is associated with dermatitis, psoriasis, atopic dermatitis, cutaneous form of food allergy, pruritic diseases, bacterial folliculitis, furunculosis, allergic dermatitis, pyoderma, mange, and immune or auto-immune dermatitis.

BRIEF DESCRIPTION OF THE DRAWINGS

The following figures are included to illustrate certain aspects of the present disclosure and should not be viewed as exclusive embodiments. The subject matter disclosed is capable of considerable modifications, alterations, combinations, and equivalents in form and function, without departing from the scope of this disclosure.

Figures 1A-1C show skin microbiome from four distinct skin sites. Figure 1A shows skin microbiome sampling sites. Figure IB shows composition of healthy canine skin at phylum level across skin sites. Figure 1C shows composition of healthy canine skin at family level across skin sites, families with a mean relative abundance of >0.01 across all samples are plotted.

Figures 2A-2C show Bray-Curtis dissimilarity matrix plotting using non-metric multidimensional scaling (NMDS). Figures 2A-2C depict analysis with co-variables breed (Figure 2A), skin site (Figure 2B), and sex (Figure 2C) showing breed and skin site as drivers of variation within the skin microbiome.

Figures 3 A-3D show alpha diversity of the canine skin microbiome. Figure 3 A shows Shannon diversity across skin sites. Figure 3B shows species richness of skin microbiome across skin sites, showing statistical difference (p<0.05) between the ear canal and interdigital sites. Figure 3C shows species richness of skin microbiome across breeds. Figure 3D shows Shannon diversity across breeds.

Figures 4A-4C show the evaluation of taxa identified on canine skin sites. Figure 4A show prevalence of skin microbiome taxa across sites (clockwise from top left; dorsal lumbar, ear canal, interdigital, groin). The dashed line represents a threshold of 80% prevalence. Figure 4B show Venn diagram showing the presence of 375 taxa across skin sites and the overlap of 230 core taxa with greater than 80% prevalence across all sites. Figure 4C show microbial composition of the accessory and core skin microbiome at each skin site at phylum taxonomic level.

Figure 5 shows the core and accessory canine skin microbiomes at phyla level at four different skin sites. Clockwise from top left; dorsal lumbar; ear canal; interdigital region of paw; groin. The fold change of the relative abundance of each phylum detected in the canine skin microbiome was calculated and plotted to show which phyla are more highly abundant in the core microbiome (those with a fold change greater than one), and which are more highly abundant in the accessory microbiome (those with a fold change less than one). Phyla which are plotted with a fold change of zero are present within the accessory microbiome but absent from the core microbiome.

Figure 6 shows average relative abundance, across skin sites, of the top 24 genera present within the core canine skin microbiome.

Figures 7A-7C show evaluation of skin microbiome function. Figure 7A shows prevalence of skin microbiome gene families across sites (clockwise from top left; dorsal lumbar, ear canal, interdigital, groin). The dashed line represents a threshold of 90% prevalence. Figure 7B shows Venn diagram showing the presence of 1,538 gene families across skin sites and the overlap of 1,219 core gene families with greater than 90% prevalence across all sites. Figure 7C shows Relative abundance of the top 20 gene families within the core microbiome, note the remaining gene families present within the core microbiome have been excluded from this figure.

Figures 8A-8B show pathways enriched in the functional core canine skin microbiome. Figure 8A shows enrichment analysis of pathways with hypergeometric test overlaid, pathways with a core ratio: accessory ratio greater than 1 are enriched in the core functional microbiome. Figure 8B shows average relative abundance of the pathways enriched within the core functional microbiome.

Figure 9 shows expected minimum and maximum relative abundance for 230 microbial taxa of the canine skin microbiome.

Figure 10 shows the study design. 88 dogs were enrolled onto the study at Day -1 and randomised into three groups (control, medicated shampoo, or non-medicated shampoo). Two wash interventions were conducted at Day 0 and Day 29 for dogs in the medicated shampoo and everyday use non-medicated shampoo (Groomers Mango and Banana) groups. Dogs within the control group were not exposed to wash interventions.

Figures 11 shows 16S quantitative PCR (qPCR) for all time points and all groups. The microbial load of canine skin was statistically significantly reduced in samples collected after washing interventions on Day 0 and Day 29 (depicted by arrows) in the medicated shampoo and non-medicated shampoo groups compared to baseline (Day -1 and Day 28 respectively). No significant difference was observed at the same time points in the control group.

Figures 12A-12B show recovery of the skin microbiome following two wash interventions. Figure 12A shows Bray Curtis dissimilarity equivalence testing showing statistically equivalent microbial profiles 7 days and 28 days following the first intervention for the medicated group and 7 days following the second wash intervention compared to baseline (day -1 and day 28). For the non-medicated shampoo group, equivalent microbial profiles were observed 28 days after the first wash intervention and 7 days after the second wash intervention, however the microbial profile was not statistically equivalent 7 days after the first wash intervention. Dashed lines show the Bray-Curtis dissimilarity threshold (upper confidence interval) for each time point comparison based on the observed variation in the control group. Figure 12B shows NMDS visualisation of Bray Curtis dissimilarity matrix showing significantly similar microbial profiles between timepoints within the control and non-medicated shampoo groups, whilst showing significantly different microbial profiles between Day 7 and other timepoints within the medicated shampoo group.

Figures 13A-13B show Alpha diversity of the canine skin microbiome before and following two washing interventions. Figure 13 A shows species richness across time for each group. Figure 13B shows Shannon diversity across time for each group. Wash interventions were conducted at Day 0 and Day 29 for the medicated shampoo and non-medicated shampoo groups.

Figure 14 shows core microbiome stability and recovery. Changes within the core genera and species of the canine skin microbiome over time and following wash interventions at Day 0 and Day 29, showing recovery of the microbiome within the study period and consistency in the core microbiome across individual dogs across the groups at Day -1.

Figures 15A-15D show Bray-Curtis dissimilarity visualised using non-metric dimensional scaling (NMDS). Figure 15A shows differences in the microbial profiles of dogs between groups are observed with greater within group similarity observed within the medicated shampoo group. Figure 15B shows that breed is the largest driver of variation in the skin microbiome of dogs with each breed clustering. Figure 15C shows microbial profile differences observed between sexes. Figure 15D shows microbial profile differences observed with neuter status. Significantly different microbial profiles (PERMANOVA p<0.001, R2=0.019; beta dispersion p=0.004,) were observed between the groups (control, medicated shampoo, non-medicated shampoo) likely owing to slightly higher within group similarity within the medicated group. Breed was largest driver for difference in the skin microbiome with significantly different microbial profiles between the four breeds (PERMANOVA p<0.001, R2=0.134; beta dispersion p>0.05). Labrador Retrievers clustered away from Norfolk Terriers and Petit Basset Griffon Vendeens; however, all breeds showed distinct clustering. For sex and neuter status, observed significantly different abundance profiles were observed with this owing to marginally higher within group similar for both female dogs (PERMANOVA p=0.003, R2=0.012; beta dispersion p=0.033) and entire dogs (PERMANOVA p<0.001, R2=0.017; beta dispersion p=0.03). Groups were balanced to account for these confounding variables.

Figure 16 shows subject enrolment and allocation into the three groups: control, medicated shampoo, or non-medicated shampoo.

Figure 17 shows the 172 taxa representing the genus and species of the core microbiome.

Figures 18A-18C shows gut microbial composition of healthy dogs from three different breeds. Figure 18A shows alpha diversity of gut microbial communities across breed. Figure 18B shows non-metric multidimensional scaling (NMDS) of Bray-Curtis dissimilarity matric, overlaid with 95% data ellipses showing empirical distribution of gut microbial taxonomy (left) and functional pathway (right). Figure 18C shows associated beta dispersion plots showing distribution from centroid of gut microbial (top) and functional pathway (bottom) in samples from healthy dogs of three breeds.

Figures 19A-19D show gut microbial composition of dogs with CAD and healthy dogs. Figures 19A-19B show non-metric multidimensional scaling (NMDS) of Bray-Curtis dissimilarity matric, overlaid with 95% data ellipses showing empirical distribution of gut microbial taxa (Figure 19A) and functional pathways (Figure 19B). Figures 19C-19D show linear discriminant analysis effect size (LEfSe) analysis described biomarker taxa (Figure 19C) and functional pathways (Figure 19D) above an LDA threshold of 3.5 and 3.0 respectively. All plots show data associated with healthy dogs in blue and CAD dogs in red.

Figures 20A-20C show skin microbiota composition of two body sites (dorsal lumbar (DL) and abdomen) in healthy dogs across three breeds. Figure 20A shows alpha diversity of skin microbial communities within each body site using paired samples collected from the same individual. Figure 20B shows non-metric multidimensional scaling (NMDS) of Bray- Curtis dissimilarity matric, overlaid with 95% data ellipses showing empirical distribution of skin microbial taxa between body sites. Figure 20C shows stacked bar plot shoring the most abundant four phyla within the skin microbiome at both body sites, with the most abundant five taxonomic families nested within this upper classification.

Figures 21A-21C show biomarkers identified for CAD in the skin microbiome of dogs. Figure 21A shows linear discriminant analysis effect size (LEfSe) analysis described biomarker taxa above an LDA threshold of 3.5 assessed for the dorsal lumbar site. Figure 2 IB shows Spearmans correlation plot showing correlation between the abundance of 29 CAD biomarkers in the dorsal lumbar and in the abdomen of individuals. D1-D9 represent nine individual dogs with “abd” denoting abdominal site and back denoting dorsal lumbar. Figure 21C shows relative abundance of four CAD associated genera within the skin microbiome of CAD and healthy dogs, with all four genera showing significant enrichment in atopic dogs considering sampling site, breed, individual and sample replicates.

Figure 22 shows cohort signalment, body condition score, bodyweight and average feed intake according to study phase.

Figure 23 shows samples collected from the study cohort.

Figure 24 shows LEfSe analysis producing 19 taxonomic biomarkers of differential abundance within the gut microbiome of healthy dogs and dogs with clinical signs of CAD.

Figure 25 shows LEfSe analysis producing 20 functional biomarkers of differential abundance within the gut microbiome of healthy dogs and dogs with clinical signs of CAD.

Figure 26 shows LEfSe analysis producing 33 taxonomic biomarkers of differential abundance within the skin microbiome of healthy dogs and dogs with clinical signs of CAD.

DETAILED DESCRIPTION

The presently disclosed subject matter relates to methods for assessing or monitoring skin or gut microbiome in animals. The presently disclosed subject matter is particularly suited for sampling the skin or gut microbiome of a companion animal, e.g., a domestic dog.

For purposes of clarity of disclosure and not by way of limitation, the detailed description is divided into the following subsections:

1. Definitions;

2. Microorganisms in the Skin and Gut Microbiome;

3. Companion Animals; and

4. Methods.

1. Definitions

The terms used in this specification generally have their ordinary meanings in the art, within the context of this disclosure and in the specific context where each term is used. Certain terms are discussed below, or elsewhere in the specification, to provide additional guidance to the practitioner in describing the compositions and methods of the disclosure and how to make and use them.

As used herein, the use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification can mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” Still further, the terms “having,” “including,” “containing” and “comprising” are interchangeable and one of skill in the art is cognizant that these terms are open ended terms.

The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The present disclosure also contemplates other embodiments “comprising,” “consisting of’, and “consisting essentially of,” the embodiments or elements presented herein, whether explicitly set forth or not.

The term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 3 or more than 3 standard deviations, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and more preferably still up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2-fold, of a value.

The term “taxa” refers to taxonomical groups, for example, kingdom, phylum, class, order, family, genus, and species. The term “abundance” can refer to an absolute amount (including presence or absence) of given bacterial taxa present within a sample. For example, an abundance can refer to the count of bacterial sequences of bacterial taxa after appropriate amplification of nucleic acid e.g,16S ribosomal DNA (rDNA) or 16S ribosomal RNA (rRNA). The term “relative abundance” can refer to a percentage composition of a particular bacterial taxa (e.g., species) relative to the total number of bacteria in the sample. It can be calculated by determining the number of sequences of given bacterial taxa divided by the total number of all bacterial sequences which is then multiplied by 100. For example, the relative abundance can refer to the relative amounts of nucleic acid present in a sample after appropriate amplification or sequencing of 16S rDNA. In certain embodiments, the relative abundance can refer to a binary classification of bacteria taxa. For example, without any limitation, binary classification can include detected versus undetected taxa or presence versus absence of taxa. In certain embodiments, the relative abundance is calculated as odds ratio. As used herein, odds ratio can be a fold change, i.e., it is a measure of how much higher or lower the abundance or relative abundance is when comparing one group to another group.

The term “animal” as used in accordance with the present disclosure refers to a wide variety of animals, such as quadrupeds, primates, and other mammals. For example, the term “animal” can refer to domestic animals including, but not limited to, dogs, cats, horses, cows, ferrets, rabbits, pigs, rats, mice, gerbils, hamsters, goats, and the like. The term “animal” can also refer to wild animals including, but not limited to, wolf, bison, elk, deer, lion, tiger, and the like. In some embodiments, the animal is a companion animal. In certain instances, the animal is a dog or a cat.

The terms “not health” or “skin disease” refer to when the relative abundance of one or more microbial taxa in a sample obtained from a subject is less than the expected minimum relative abundance shown in Figure 9, or greater than the expected maximum relative abundance shown in Figure 9. These terms additionally refer to when one or more samples obtained from the skin of a subject exhibit an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa which are associated with canine atopic dermatitis (CAD) as shown in Figure 26. These terms additionally refer to when one or more samples obtained from the skin of a subject exhibit a reduction, decreased abundance, or decreased relative abundance in one or more microbial taxa which are associated with healthy dogs as shown in Figure 26. These terms additionally refer to when one or more samples obtained from the skin of a subject exhibit an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa which are associated with canine atopic dermatitis (CAD) as shown in Figure 24. These terms additionally refer to when one or more samples obtained from the skin of a subject exhibit a reduction, decreased abundance, or decreased relative abundance in one or more microbial taxa which are associated with healthy dogs as shown in Figure 24. The term “nucleic acid molecule” and “nucleotide sequence,” as used herein, refers to a single or double stranded covalently-linked sequence of nucleotides in which the 3’ and 5’ ends on each nucleotide are joined by phosphodiester bonds. The nucleic acid molecule can include deoxyribonucleotide bases or ribonucleotide bases and can be manufactured synthetically in vitro or isolated from natural sources.

The terms “isolated” or “purified”, used interchangeably herein, refers to a nucleic acid, a polypeptide, or other biological moiety that is removed from components with which it is naturally associated. The term “isolated” can refer to a polypeptide that is separate and discrete from the whole organism with which the molecule is found in nature or is present in the substantial absence of other biological macromolecules of the same type. The term “isolated” with respect to a polynucleotide can refer to a nucleic acid molecule devoid, in whole or part, of sequences normally associated with it in nature; or a sequence, as it exists in nature, but having heterologous sequences in association therewith; or a molecule disassociated from the chromosome. As used herein, the term “biomarker” can refer to a characteristic that is objectively measured and evaluated as an indicator of physiological biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. In certain non-limiting embodiments, the term “biomarker” can refer to any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease.

The word “substantially” does not exclude “completely”, e.g, a composition which is “substantially free” from Y can be completely free from Y.

2. Microorganisms in the Skin and Gut Microbiome

The present disclosure relates to, inter alia, kits and related methods for detecting one or more microbial taxa (e.g., bacteria) in a skin or gut microbiome of an animal. The one or more microbial taxa (e.g., bacteria) can be associated with a skin disease or disorder, e.g., dermatitis, or with good skin health, as disclosed herein.

The skin microbiome associated with good skin health comprises one or more microbial taxa shown in Figure 9. The most abundant functions of the core microbiome are functions involved in DNA, energy and intermediary metabolism, such as basic replication machinery genes such as gyrA and gyrB,' genes involved biosynthesis of nucleotides such as nrdE and nrdE (Figure 7C); genes involved in lipid metabolism, such as fad I),' genes involved in RNA metabolism, such as rpoB and rpoC,' genes associated with the active transport of large receptor molecules, such as cirA, cfrA, hmuR (Figure 7C); genes encoding outer membrane receptors associated with the transport of ferrienterobactin and colicins; genes encoding enterobactin exporter systems, such as entS,' the structural gene for lantibiotic Pep5, pepA genes from the Pep5 gene cluster, such as pepT, pepN, pepB, pepl), which include the genes encoding the transport protein for PepA (pepT) and the serine protease for proteolytic processing of PepA (pepP), genes involved in multidrug resistance, such as proteins belonging to the Resistance Nodulation Cell Division (RND) superfamily; genes encoding multidrug and toxic compound extrusion (MATE) transporters and hydrophobic/amphiphilic exporters (HAE); genes involved in multidrug efflux systems, such as mdtA, mdtB, mdtC, mdtE, mdlK, mdtN, emrA, emrB, acrA, mexA, mexB, tcaB and mepA genes encoding penicillicin binding proteins, mrcB, pbp2A, pbpB, pbpC: and genes that provide colonisation resistance to resident microbes, such as genes associated with adhesins, biofilms and hemolysis, e.g., icaA,fimC,fimD, tlyC, respectively. In certain embodiments, the one or more microbial taxa are one or more bacteria. Functional biomarkers of differential abundance within the gut microbiome of healthy dogs and dogs with clinical signs of CAD are shown in Figure 25. The gut microbiome of dogs with clinical signs of CAD comprise about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, or about

8 functional biomarkers selected from the group consisting of: environmental information processing; membrane transport; two-component system; signal transduction; ABC transporters; xenobiotics biodegradation and metabolism; lipid metabolism; porphyrin and chlorophyll metabolism; and combinations thereof. In certain embodiments, the gut microbiome of dogs with clinical signs of CAD comprise about 1, about 2, about 3, about 4, about 5, about 6, about 7, or about 8 functional biomarkers as shown in Figure 25. The gut microbiome of healthy dogs comprise about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about

9 or more, about 10 or more, about 11 or more, or about 12 functional biomarkers selected from the group consisting of: genetic information processing; amino acid metabolism; protein translation; nucleic acid replication and repair; aminoacyl-tRNA biosynthesis; metabolism of terpenoids and polyketides; RNA degradation; protein folding; genetic information processing (e.g., folding, sorting, and degradation of nucleic acids and/or proteins); lysine biosyntehesis; alanine, aspartate, and glutamate metabolism; valine, leucine, and isoleucine biosynthesis; and peptidoglycan biosynthesis; and combinations thereof. In certain embodiments, the gut microbiome of healthy dogs comprise about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 11, or about 12 functional biomarkers selected from the group consisting of: genetic information processing; amino acid metabolism; protein translation; nucleic acid replication and repair; aminoacyl-tRNA biosynthesis; metabolism of terpenoids and polyketides; RNA degradation; protein folding; genetic information processing (e.g., folding, sorting, and degradation of nucleic acids and/or proteins); lysine biosynthesis; alanine, aspartate, and glutamate metabolism; valine, leucine, and isoleucine biosynthesis; and peptidoglycan biosynthesis; and combinations thereof.

In certain embodiments, the one or more bacteria associated with a skin disease or disorder is selected from the microbial taxa shown in Figure 9, for example, Conchiformibius steedae GCF 000620925.1 GTDB.r202, Unclassified Bacteria, Prevotella copri, Cutibacterium acnes GCF 000376705.1 GTDB.r202, Frederiksenia canicola GCF 011455495.1 GTDB.r202, Bergeyella zoohelcum, Unclassified Acinetobacter genus, Unclassified Allobaculum genus, Bergeyella zoohelcum GCF 000301075.1 GTDB.r202, Phocaeicola sp900546645, Unclassified Ileibacterium genus, Clavibacter californiensis GCF 002931175.1 GTDB.r202, Psychrobacter sp001652315, Rathayibacter sp002930885,

Microbacterium lemovicicum GCF 003991875.1 GTDB.r202, Unclassified

Gammaproteobacteria class, Prevotellamassilia sp000437675, Rhodococcus C sp001426185 GCF 001425985.1 GTDB.r202, Unclassified Bacteroides genus, Unclassified Bacteroidaceae family, Unclassified Pseudomonas E genus, Sphingomonas sp001421415 GCF 001421415.1 GTDB.r202, Unclassified Blautia A genus, Sphingomonas aerolata, Unclassified Sphingomonas genus, Unclassified Actinomycetia class, Sphingomonas aerolata GCF 000733135.1 GTDB.r202, Methylobacterium sp001422985 GCF 001422985.1 GTDB.r202, Sphingomonas aerolata GCF 000732685.2 GTDB.r202, Sphingomonas aerolata GCF 001422525.1 GTDB.r 202, Mycobacterium sp001426545 GCF 001426545.1 GTDB.r202, Unclassified Burkholderiaceae family, Unclassified Nocardioides genus, Rhodococcus B fascians, Unclassified Methylobacterium genus, Unclassified Sphingomonadaceae family, Unclassified Clavibacter genus, Rathayibacter sp002930885 GCF 002931755.1 GTDB. r202, Sphingomonas spOOl 421745

GCF 001421745.1 GTDB.r202, Unclassified Rhodococcus B genus, Unclassified Bacteroidia class, Porphyromonas gingivicanis GCF 000614585.1 GTDB.r 202, Porphyromonas A cangingivalis, Moraxella canis GCF 002014965.1 GTDB.r 202, Porphyromonas A cangingivalis GCF 000766005.1 GTDB.r202, Porphyromonas A cangingivalis GCF 900167355.1 GTDB.r202, Bergeyella zoohelcum GCF 000301095.1 GTDB.r202, Neisseria weaveri GCF 900086555.1 GTDB.r202,

Acinetobacter johnsonii, Cutibacterium acnes, Capnocytophaga canis, Capnocytophaga canimorsus, Micrococcus luteus, Capnocytophaga cynodegmi

GCF 002302475.1 GTDB.r202, Capnocytophaga cynodegmi

GCF 000379185.1 GTDB. r202, Histophilus haemoglobinophilus

GCF 002015075.1 GTDB.r 202, Unclassified Paraprevotella genus, Faecalibacterium sp900540455, Neisseria canis GCF 002108495.1 GTDB.r 202, Eikenella shayeganii

GCF 000226875.1 GTDB. r202, Neisseria zoodegmatis GCF 900187305.1 GTDB.r202,

Unclassified Bacteroidales order, Unclassified Ralstonia genus, Ralstonia insidiosa GCF 001663855.1 GTDB.r202, Blautia A sp900541345, Neisseria animaloris GCF 002108605.1 GTDB.r202, Unclassified Peptacetobacter genus, Ralstonia pickettii B GCF 000020205.1 GTDB.r202, Collinsella intestinalis, Frigoribacterium spOO 1421165 GCF 001421165.1 GTDB.r202, Sphingomonas aurantiaca

GCF 001421685.1 GTDB.r202, Unclassified Enterobacteriaceae family, Neorhizobium soli GCF 001423215.1 GTDB.r 202, Gemella palaticanis GCF 015234765.1 GTDB. r202, Unclassified Lachnospiraceae family, Unclassified Flavobacteriales order, Unclassified Clostridia class, Turicibacter sp002311155, Methylobacterium spOOl 422375 GCF 001422375.1 GTDB.r 202, Paracoccus marcusii, Arthrobacter D sp001422665 GCF 001422665.1 GTDB.r202, Nocardioides glacieisoli GCF 004137245.1 GTDB.r202, Unclassified Actinomycetales order, Unclassified Proteobacteria phylum, Porphyromonas gulae, Acinetobacter pittii GCF 004360215.1 GTDB.r 202, Cutibacterium acnes GCF 001281065.1 GTDB.r202, Bacteroides sp900766005, Unclassified Capnocytophaga genus, Unclassified Phocaeicola genus, Phascolarctobacterium A sp900552855, Unclassified Streptococcus genus, Unclassified Mycobacterium genus, Porphyromonas A canoris GCF 000769115.1 GTDB.r202, Massilia aureaB GCF 014200505.1 GTDB.r202, Methylobacterium spOOl 423085 GCF 001423085.1 GTDB.r 202, Fusobacterium B sp900554885, Fusobacterium A sp900555845, Unclassified Neisseria genus, Unclassified Prevotella genus, Unclassified Escherichia genus, Capnocytophaga cynodegmi, Buchananella hordeovulneris GCF 001907235.1 GTDB.r202, Pasteurella canis, Unclassified Pasteur ellaceae family, Deinococcus marmoris

GCF 000701405.1 GTDB.r202, Blautia sp900556555, Unclassified Moraxellaceae family, Corynebacterium mustelae GCF 001020985.1 GTDB.r202, Rhodococcus B sp002259335, Hymenobacter norwichensis GCF 000420705.1 GTDB.r 202, Unclassified Bacilli class, Parasutterella sp000980495, Ileibacterium

[Strawberry _pl metabat2 low PE.004. contigs GTDB. r202 ], Unclassified

Erysipelotrichaceae family, Unclassified Fusobacterium B genus, Streptococcus minor GCF 000377005.1 GTDB.r 202, Pantoea agglomerans, Unclassified Alphaproteobacteria class, Pasteurella dagmatis GCF 900186835.1 GTDB.r 202, Capnocytophaga canis GCF 000827555.1 GTDB.r 202, Unclassified Prevotellamassilia genus, Paracoccus marcusii GCF 019141545.1 GTDB.r 202, Unclassified Corynebacterium genus, Unclassified Brevundimonas genus, Paenibacillus 0 herberti GCF 002233675.1 GTDB.r202, Blautia hansenii, Pasteurella multocida A GCF 000973525.1 GTDB.r 202, Microbacterium saperdae

GCF 014646775.1 GTDB.r202, Actinomyces bowdenii B GCF 015234535.1 GTDB.r202, Unclassified Psychrobacter genus, Holdemanella sp002299315, Porphyromonas A canoris GCF 000765975.1 GTDB.r202, Unclassified Moraxella A genus, Unclassified Neisseriaceae family, Methylobacterium sp000376345 GCF 001422345.1 GTDB.r 202, Peptacetobacter sp900550335, Unclassified Bifidobacterium genus, Paracoccus marcusii GCF 006151785.1 GTDB.r 202, Unclassified Moraxella genus, Capnocytophaga canimorsus GCF 000220625.1 GTDB.r202, Capnocytophaga canimorsus GCF 002302565.1 GTDB.r202, Porphyromonas A canoris, Curtobacterium flaccumfaciens A, Aliterella sp003003885 GCF 003003885.1 GTDB.r202, Erwinia B gerundensis GCA 001517405.1 GTDB.r202, Capnocytophaga canimorsus GCF 002302655.1 GTDB.r202, Microbacterium foliorum

GCF 000956415.1 GTDB.r202, Capnocytophaga canimorsus

GCF 002302445.1 GTDB.r202, Porphyromonas crevioricanis

GCF 900167225.1 GTDB.r202, Paracoccus marcusii GCF 000967825.1 GTDB.r202,

Agreia spOO 1421485 GCF 002931035.1 GTDB.r202, Faecalimonas umbilicate, Pseudomonas E graminis GCF 900111735.1 GTDB.r202, Unclassified Microbacterium genus, Curtobacterium sp001424385 GCF 001424385.1 GTDB.r 202, Bifidobacterium globosum, Unclassified Microbacteriaceae family, Unclassified Cutibacterium genus, Sphingomonas spOO 1421805 GCF 001421825.1 GTDB.r202, Schaedlerella sp900765975, Porphyromonas gulae GCF 000769385.1 GTDB.r 202, Unclassified Fusobacterium A genus, Amulumruptor sp900539915, Clostridium sp900766315 [Virginia _pl metabat2 low PE.013. contigs GTDB.r 202], Unclassified Porphyromonas genus, Capnocytophaga canis GCF 002302535.1 GTDB.r202, Capnocytophaga canis GCF 002302515.1 GTDB.r202, Faecalibaculum rodentium

GCF 001564455.1 GTDB.r202, Kocuria rhizophila GCF 002861865.1 GTDB.r202, Weissella confusa GCF 018390755.1 GTDB.r202, Clavibacter michiganensis K, Variovorax ginsengisoli GCF 006438845.1 GTDB.r202, Terribacillus saccharophilus GCF 002884435.1 GTDB.r202, Ruminococcus B gnavus, Frondihabitans sp001423105 GCF 001423105.1 GTDB.r202, Turicibacter sp001543345, Porphyromonas gulae GCF 000971515.1 GTDB.r202, Actinomyces GCF 016598775.1 GTDB.r202, Frigoribacterium endophyticum GCF 001423665.1 GTDB.r202, Plantibacter jlavus GCF 900177615.1 GTDB. r202, Plantibacter spOOl 423185

GCF 001421315.1 GTDB.r202, Unclassified Frigoribacterium genus, Porphyromonas gulae GCF 000378065.1 GTDB. r 202, Unclassified Weeksellaceae family, Lactobacillus acidophilus, Dubosiella newyorkensis, Brevundimonas intermedia GCF 004614235.1 GTDB.r202, UBA7173 sp001701135, Anaerobiospirillum succiniciproducens, Clavibacter michiganensis K GCF 002931135.1 GTDB.r202, Bifidobacterium animalis GCF 000612705.1 GTDB.r 202, Fusobacterium A sp900015295, Unclassified Porphyromonadaceae family, Phocaeicola vulgatus, Unclassified Hymenobacter genus, Sanguibacter inulinus GCF 015234745.1 GTDB.r 202, Romboutsia timonensis GCF 900106845.1 GTDB.r202, Methylobacterium sp000372825

GCF 000372825.1 GTDB.r202, Psychrobacter sp001652315

GCF 007280595.1 GTDB.r202, Peptacetobacter hiranonis, Unclassified Mycobacteriaceae family, Frigoribacterium sp000878135 GCF 001421865.1 GTDB.r202, Fusobacterium canifelinum, Mycobacterium sp001428895 GCF 001428895.1 GTDB.r202,

Acinetobacter guillouiae, Deinococcus aquatilis GCF 000378445.1 GTDB.r202, Aeromicrobium fastidiosum GCF 017876595.1 GTDB.r202, Atopobiaceae Jenna _p2 metabat2 low PE.018.contigs GTDB.r202, Eisenbergiella sp900539715, Porphyromonas gulae GCF 000769475.1 GTDB.r202, Unclassified Acidovorax genus,

Muribaculum sp002492595, Curtobacterium flaccumfaciens

GCF 001864905.1 GTDB.r202, Unclassified Curtobacterium genus, Pantoea eucalypti

GCF 900167425.1 GTDB.r202, Curtobacterium flaccumfaciens A

GCF 000349565.1 GTDB. r202, Methylobacterium sp001422815

GCF 001422165.1 GTDB.r202, Microcoleus sp000317475

GCF 000317475.1 GTDB.r202, Porphyromonas macacae

GCF 000379945.1 GTDB. r202, Pararhizobium sp001426685

GCF 001426685.1 GTDB. r202, Pararhizobium sp001424045

GCF 001424045.1 GTDB.r202, Faecalimonas sp900550235, Allobaculum stercoricanis,

Muribaculum sp002492595 GCF 004803695.1 GTDB.r202, Porphyromonas gingivalis, Mycobacterium sp002043095 GCF 002043095.1 GTDB.r202, Nocardioides exalbidus GCF 900105585.1 GTDB.r202, Pseudomonas E sp002843585

GCF 002843585.1 GTDB.r202, Peptostreptococcus canis

GCF ' 014229965.1 GTDB.r202 , and combinations thereof. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 75 or more, about 100 or more, about 125 or more, about 150 or more, about 175 or more, about 200 or more, or about 225 or more, or about 230 microbial taxa shown in Figure 9. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 75, about 100, about 125, about 150, about 175, about 200, or about 225, or about 230 microbial taxa shown in Figure 9. In certain embodiments, the one or more bacteria associated with a skin disease or disorder is selected from the group consisting of microbial taxa shown in Figure 17, for example Acidovorax sp., Acinetobacter sp., Acinetobacter Johnsonii, Acinetobacter _pittii, Acinetobacter guillouiae, Actinomyces bowdenii B, Actinomyces GCF 016598775.1, Aeromicrobium fcislidiosum, Agreia spOOl 421485, Aliterella sp003003885, Allobaculum sp., Allobaculum stercoricanis, Amulumruptor sp900539915,

Anaerobiospirillum succiniciproducens, Arthrobacter D spOO 1422665, Bacteroides sp., Bacteroides sp900766005, Bergeyella zoohelcum, Bifidobacterium sp.,

Bifidobacterium globosum, Bifidobacterium animalis, Blautia sp900556555, Blautia hansenii, Blautia A sp., Blautia A sp900541345, Brevundimonas sp., Brevundimonas intermedia, Buchananella hordeovulneris, Capnocytophaga canimorsus, Capnocytophaga sp., Capnocytophaga canis, Capnocytophaga cynodegmi, Capnocytophaga canimorsus, Clavibacter californiensis, Clavibacter sp., Clavibacter michiganensis K, Clostridium sp900766315, Collinsella intestinalis, Conchiformibius steedae, Corynebacterium mustelae, Corynebacterium sp., Curtobacterium flaccumfaciens A, Curtobacterium sp., Curtobacterium spOOl 424385, Cutibacterium acnes, Cutibacterium sp., Deinococcus mar mor is, Deinococcus aquatilis, Dubosiella newyorkensis, Eikenella shay eganii, Eisenbergiella sp900539715,

Erwinia B gerundensis, Escherichia sp., Faecalibacterium sp900540455, Faecalibaculum rodentium, Faecalimonas umbilicata, Faecalimonas sp900550235, Frederiksenia canicola, Frigoribacterium spOOl 421165, Frigoribacterium endophyticum, Frigobacterium sp., Frigoribacterium sp000878135, Frondihabitans sp001423105, Fusobacterium canifelinum, Fusobacterium A sp900555845, Fusobacterium A sp., Fusobacterium A sp900015295, Fusobacterium B sp., Fusobacterium B sp900554885, Gemella palalicanis, Histophilus haemoglobinophilus, Holdemanella sp002299315, Hymenobacter sp., Hymenobacter norwichensis, Ileibacterium sp., Kocuria rhizophila, Lactobacillus acidophilus, Massilia aurea B, Methylobacterium sp., Methylobacterium spOO 1423085, Methylobacterium spOO 1422985 ,

Methylobacterium spOOO 376345, Methylobacterium spOO 1422375,

Methylobacterium spOOO 372825, Methylobacterium spOO 1422815,

Microbacterium saperdae, Microbacterium lemovicicum, Microbacterium sp., Microbacterium foliorum, Micrococcus luteus, Microcoleus spOOO 317475, Moraxella canis, Moraxella sp., Moraxella A sp., Muribaculum sp002492595, Mycobacterium sp., Mycobacterium sp001426545, Mycobacterium spOO 1428895, Mycobacterium sp002043095, Neisseria weaveri, Neisseria canis, Neisseria animaloris, Neisseria sp., Neisseria zoodegmatis, Neorhizobium soli, Nocardioides sp., , Nocardioides glacieisoli Nocardioides exalbidus, Paenibacillus O herberti,

Pantoea agglomerans, Pantoea eucalypti, Paracoccus mar cusii, Paraprevotella sp., Pararhizobium spOOl 424045, Pararhizobium spOOl 426685, Parasutterella sp000980495, Pasteurella dagmatis, Pasteurella canis, Pasteurella multocida A, Peptacetobacter sp., Peptacetobacter sp900550335, Peptacetobacter hiranonis, Peptostreptococcus canis, Phascolarctobacterium A sp900552855, Phocaeicola sp900546645, Phocaeicola sp., Phocaeicola vulgatus, Plantibacter flavus, Plantibacter sp001423185,

Porphyromonas gingivicanis, Porphyromonas gulae, Porphyromonas sp., Porphyromonas crevioricanis, Porphyromonas gingivalis, Porphyromonas macacae, Porphyromonas A cangingivalis, Porphyromonas A canoris, Prevotella copri, Prevotella sp., Prevotellamassilia sp000437675, Prevotellamassilia sp., Pseudomonas E sp., Pseudomonas E graminis, Pseudomonas E sp002843585, Psychrobacter spOOl 652315, Psychrobacater sp., Ralstonia insidiosa, Ralstonia sp., Ralstonia _pickettii B, Rathayibacter sp002930885, Rhodococcus B sp., Rhodococcus B f asci an s, Rhodococcus B sp002259335, Rhodococcus C spOOl 426185, Romboutsia timonensis, Ruminococcus B gnavus, Sanguibacter inulinus, Schaedlerella sp900765975, Sphingomonas spOO 1421415, Sphingomonas aurantiaca, Sphingomonas aerolata, Sphingomonas sp., Sphingomonas spOOl 421745, Sphingomonas spOOl 421805, Streptococcus sp., Streptococcus minor, Terribacillus saccharophilus, Turicibacter sp002311155, Turicibacter spOOl 543345, UBA 7173 spOOl 701135,

Variovorax ginsengisoli, Weissella confusa, and combinations thereof. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 75 or more, about 100 or more, about 125 or more, about 150 or more, or about 170 or more microbial taxa shown in Figure 17. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 75, about 100, about 125, about 150, about 170 microbial taxa shown in Figure 17. In certain embodiments, the one or more bacteria associated with a skin disease or disorder is selected from the group consisting of microbial taxa shown in Figure 14, for example, Variovorax ginsengisoli, Sphingomonas sp001421805, Sphingomonas sp001421745, Sphingomonas sp001421415, Sphingomonas aurantiaca, Sphingomonas aerolata, Sphingomonas sp, Sanguibacter inulinus, Ruminococcus B gnavus, Rhodococcus C sp001426185, Rhodococcus B sp002259335, Rhodococcus B fascians, Rhodococcus B sp., Pseudomonas E graminis, Psudomonas E sp., Prevotella sp., Phocaeicola vulgatus, Phocaeicola sp900546645, Phocaeicola sp., Phascolarctobacterium A sp900552855, Peptostreptococcus canis, Pararhizobium sp001426685, Pararhizobium sp001424045, Pantoea agglomerans, Nocardioides glacieisoli, Nocardioides exalbidus, Mycobacterium sp002043095, Mycobacterium sp001428895, Mycobacterium sp001426545, Muribaculum sp002492595, Microcoleus sp000317475, Microbacterium saperdae, Methylobacterium spOOl 423085, Methylobacterium sp001422985, Methylobacterium spOOl 422375, Methylobacterium sp000376345, Methylobacterium sp000372825, Massilia aurea B, Hymenobacter norwichensis, Hymenobacter sp., Gamella palaticanis, Fusobacterium B sp900554885, Fusobacgerium B sp., Fusobacterium A sp900555845, Fusobacterium A sp., Frondihabitans sp001423105, Frigoribacterium sp001421165, Frigoribacterium sp000878135, Frigoribacterium endophyticum, Frigoribacterium sp., Faecalimonas sp900550235, Faecalibacterium sp900540455, Eisenbergiella sp900539715, Deinococcus marmoris, Deinococus aquatilis, Curobacterium sp001424385, Curtobacterium flaccumfaciens A, Clavibacter michganensis K, Clavibacter califoriensis, Clavibacter sp., Buchananella hordeovulneris, Brevundimonas intermedia, Brevundimonas sp., Bifidobacerium animalis, Bacteroides sp900766005, Bacteroides sp., Arthrobacter D sp001422665, Anaerobiospirillum succiniciproducens, Agreia sp001421485, Aeromicrobium fastidiosum, Actinomyces GCF 016598775.1, Acinetobacter johnsonii, Acinetobacter guillouiae, Acidovorax sp., and combinations thereof. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, or about 74 or more microbial taxa shown in Figure 14. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 60, about 70, or about 74 microbial taxa shown in Figure 14.

In certain embodiments, the one or more bacteria associated with a skin disease or disorder is selected from the group consisting of microbial taxa shown in Figure 24, for example, Firmicutes A sp., Prevotella genus, Prevotella copri, Prevotellamassilia genus, Prevotellamassilia sp000437675, Catenibacterium genus, Catenibacterium sp000437715, Prevotella sp., Clostridia class, Acidaminococcales order, Acidaminococcaceae family, Phascolarctobacgerium A genus, Phocaeicola vulgatus, Ruminococcus B genus, Escherichia genus, Bacteroides ster coris, Escherichia sp., Bacteroides uniformis, Terrisporobacter genus, and combinations thereof. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, or about 19 microbial taxa shown in Figure 24. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1 or more, about 2 or more, about 3 or more, about 4 or more, about

5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, or about 15 or more microbial taxa shown in Figure 24. In certain embodiments, the one or more bacteria comprise about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, or about 9 microbial taxa selected from the group consisting of Firmicutes A sp., Clostridia class, Phocaeicola vulgatus, Ruminococcus B genus, Escherichia genus, Bacteroides stercoris, Escherichia sp., Bacteroides uniformis, Terrisporobacter genus, and combinations thereof, which are enriched in the gut microbiome dogs with clinical signs of CAD. In certain embodiments, the one or more bacteria comprise about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, or about 9 microbial taxa which are enriched in the gut microbiome dogs with clinical signs of CAD. In certain embodiments, the one or more bacteria comprise about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about

6 or more, about 7 or more, about 8 or more, about 9 or more, or about 10 microbial taxa selected from the group consisting of Prevotella genus, Prevotella copri, Prevotellamassilia genus, Prevotellamassilia sp000437675, Catenibacterium genus, Catenibacterium sp000437715, Prevotella sp., Acidaminococcales order, Acidaminococcaceae family, Phascolarctobacgerium A genus, and combinations thereof, which are enriched in the gut microbiome of healthy dogs. In certain embodiments, the one or more bacteria comprise about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, or about 10 microbial taxa which are enriched in the gut microbiome of healthy dogs. In certain embodiments, the one or more bacteria associated with a skin disease or disorder is selected from the group consisting of microbial taxa shown in Figure 26, for example, Bacteroidia class, Bacteroidota phylum, Staphylococcaceae family, Staphylococcus genus, Actinobacteria class, Actinobacteriota phylum, Staphylococcales order, Bacteroides pyrogenes, Bacillales order, Bergeyella zoohelcum, Kocuria rhizophila, Porphyromonas cangingivalis, Staphylococcus schleiferi, Rhizobiaceae family, Acinetobacter radiore sistens, Actinomycetales order, Actinomycetaceae family, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium genus, Allorhizobium- Neorhizobium-Pararhizobium-Rhizobium sp., Capnocytophaga genus, Staphylococcus xylosus, Flavobacterium sp., Flavobacterium genus, Cutibacterium sp., Nocardioidaceae family, Nocardioides genus, Aureimonas sp., Auerimonas genus, Porphyromonas gingivicanis sp., Clostridia class, Enter obacteriaceae family, Streptococcus mitis, Acinetobacter johnsonii, and combinations thereof. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, or about 30 or more microbial taxa shown in Figure 26. In certain embodiments, the one or more bacteria associated with a skin disease or disorder comprises about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, or about 33 microbial taxa shown in Figure 26. In certain embodiments, the one or more bacteria comprise about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, or about 9 microbial taxa selected from the group consisting of Bacteroidia class, Bacteroidota phylum, Staphylococcaceae family, Staphylococcus genus, Actinobacteria class, Actinobacteriota phylum, Staphylococcales order, Bacteroides pyrogenes, Bacillales order, Bergeyella zoohelcum, Kocuria rhizophila, Porphyromonas cangingivalis, Staphylococcus schleiferi, Rhizobiaceae family, Acinetobacter radioresistens, Actinomycetales order, Actinomycetaceae family, Allorhizobium-Neorhizobium- Pararhizobium-Rhizobium genus, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium sp., Capnocytophaga genus, Staphylococcus xylosus, Flavobacterium sp., Flavobacterium genus, Cutibacterium sp., Nocardioidaceae family, Nocardioides genus, Aureimonas sp., Auerimonas genus, Porphyromonas gingivicanis sp., and combinations thereof, which are enriched in the skin microbiome dogs with clinical signs of CAD. In certain embodiments, the one or more bacteria comprise about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, or about 9 microbial taxa which are enriched in the skin microbiome dogs with clinical signs of CAD. In certain embodiments, the one or more bacteria comprise about 1 or more, about 2 or more, about 3 or more, or about 4 microbial taxa selected from the group consisting of Clostridia class, Enterobacteriaceae family, Streptococcus mitis, Acinetobacter johnsonii, and combinations thereof, which are enriched in healthy dogs. In certain embodiments, the one or more bacteria comprise about 1, about 2, about 3, or about 4 microbial taxa which are enriched in healthy dogs.

The methods and kits of the disclosed subject matter can be used to detect bacteria in the skin and gut microbiome of a wide variety of animals, such as quadrupeds, primates, and other mammals. The methods and kits of the disclosed subject matter are particularly well suited for use with companion animals, such as dogs, cats, and other domesticated animals.

3. Companion Animals

The presently disclosed subject matter focuses on the health assessment of companion animals. In specific embodiments, the companion animal is a domestic dog.

Dog Breeds

The present disclosure relates to, inter alia, methods for assessing health and wellbeing of animals. Characteristics of companion animals can vary, including by size, sex, breed, and species. However, for the most common member within this category, dogs, can in general provide an indication of the efficacy of a method when applied to other animals.

As used herein, the expression “size category” refers to the definition of the animal (e.g., dogs, cats, etc.) in terms of the average weight of the particular animal breed. Animals (e.g., dogs, cats, etc.) of the same breed can have relatively uniform physical characteristics, such as size, coat color, physiology, and behavior, as compared to animals of a different breed. It is noted that the discussion below is focused on dogs, however, other companion animals and wild animals are intended to be covered by the scope of this disclosure and the present disclosure is not intended to be limited to dogs.

The dog can be any breed of dog, including toy/extra-small, small, medium-small, medium, medium-large, large or extra-1 arge/gi ant breeds. Non-limiting examples of toy/extra-small breeds include Affenpinscher, Australian Silky Terrier, Bichon Frise, Bolognese, Cavalier King Charles Spaniel, Chihuahua, Chinese Crested, Coton De Tulear, English Toy Terrier, Griffon Bruxellois, Havanese, Italian Greyhound, Japanese Chin, King Charles Spaniel Lowchen (Little Lion Dog), Maltese, Miniature Pinscher, Papillon, Pekingese, Pomeranian, Pug, Russian Toy, and Yorkshire Terrier. Examples of small breeds include, but are not limited to, French Bulldog, Beagle, Dachshund, Pembroke Welsh Corgi, Miniature Schnauzer, Cavalier King Charles Spaniel, Shih Tzu, and Boston Terrier. Examples of medium dog breeds include, but are not limited to, Bulldog, Cocker Spaniel, Shetland Sheepdog, Border Collie, Basset Hound, Siberian Husky, and Dalmatian. Examples of large breed dogs include, but are not limited to, Great Dane, Neapolitan mastiff, Scottish Deerhound, Dogue de Bordeaux, Newfoundland, English mastiff, Saint Bernard, Leonberger, and Irish Wolfhound. Other non-limiting examples of breeds include those listed in Wallis et al. (2021). Cross-breeds can generally be categorized as toy/extra-small, small, mediumsmall, medium, medium-large, large, and extra-1 arge/gi ant dogs depending on their body weight. In certain embodiments, the dog is a toy/extra-small breed. In certain embodiments, the dog is a small, medium-small, medium, medium-large, large or extra-1 arge/gi ant breed. In some embodiments, the dog is a mix of two or more breeds. In such instances, the mixed- breed dog can still be categorized by size depending on their body weight and can exhibit traits (e.g., behavioral traits, genetic traits, etc.) associated with each of the two or more breeds found in the dog.

The Federation Cynologique Internationale currently recognizes 346 pure dog breeds. The breed of a dog can be identified, for example, either by observing its physical traits or by genetic analysis. A pedigree dog is the offspring of two dogs of the same breed, which is eligible for registration with a recognized club or society that maintain a register for dogs of that description. There are a number of pedigree dog registration schemes, of which the Kennel Club is the most well-known.

Table 1. A list of dog size categories.

In certain embodiments, the dog size categories are selected according to Salt et a 2017 (Table 1). In other embodiments, the dog size categories are selected according to alternative designations. A small breed can correspond with animals that have an average body weight of from about 6.5 kilograms to about 9 kilograms. A medium breed can correspond with an animal that has an average body weight between about 9 kilograms and about 30 kilograms. A large breed can correspond with an animal that has an average body weight of between about 30 kilograms and about 40 kilograms. A giant breed can correspond with an animal that has an average body weight of between over about 40 kilograms.

4. Methods

The present invention provides methods for determining the skin health status of an animal comprising: (a) quantifying one or more microbial taxa from a sample to determine abundance or relative abundance of the one or more microbial taxa, and (b) determining the skin health status of the animal. In certain embodiments, determining the skin health status of the animal comprises comparing the abundance or relative abundance of the one or more microbial taxa with a reference abundance or relative abundance of the one or more microorganism. In certain embodiments, the methods further comprise administering a therapeutically effective amount of a topical treatment, non-medicated shampoo, medicated shampoo, a therapeutic (e.g., an antibiotic), or a combination thereof, when the health status is “not health” or “skin disease”. The amount or frequency of administration can be determined depending on the determined skin health status of the subject. The predicted future health of the animal can also be taken into account when determining the amount of frequency of administration.

The present disclosure further provides methods of improving the skin microbiome of a subject in need thereof, comprising administering a therapeutically effective amount of a topical treatment, non-medicated shampoo, medicated shampoo, a therapeutic (e.g., an antibiotic), or a combination thereof. In certain embodiments, the skin microbiome of the subject comprises an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa shown in Figure 9, Figure 14, Figure 17, Figure 24, and/or Figure 26, in comparison to one or more reference sample. In certain embodiments, the skin microbiome of the subject comprises a reduction or decreased relative abundance in one or more microbial taxa shown in Figure 9, Figure 14, Figure 17, Figure 24, and/or Figure 26, in comparison to one or more reference sample.

Additionally or alternatively, in any of the disclosed methods, the one or more reference sample is obtained from a healthy subject. In certain embodiments, the one or more reference sample is obtained from a subject with skin disease, e.g., CAD.

Additionally or alternatively, in any of the disclosed methods, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 75 or more, about 100 or more, about 125 or more, about 150 or more, about 175 or more, about 200 or more, about 225 or more, or about 230 or more selected from the group consisting of microbial taxa shown in Figure 9, for example, Conchiformibius steedae GCF 000620925.1 GTDB.r202, Unclassified Bacteria, Prevotella copri, Cutibacterium acnes GCF 000376705.1 GTDB.r202, Frederiksenia canicola GCF 011455495.1 GTDB.r202, Bergeyella zoohelcum, Unclassified Acinetobacter genus, Unclassified Allobaculum genus, Bergeyella zoohelcum GCF 000301075.1 GTDB.r202, Phocaeicola sp900546645, Unclassified Ileibacterium genus, Clavibacter californiensis GCF 002931175.1 GTDB.r202, Psychrobacter sp001652315, Rathayibacter sp002930885, Microbacterium lemovicicum GCF 003991875.1 GTDB.r202, Unclassified Gammaproteobacteria class, Prevotellamassilia sp000437675, Rhodococcus C sp001426185 GCF 001425985.1 GTDB.r202, Unclassified Bacteroides genus, Unclassified Bacteroidaceae family, Unclassified Pseudomonas E genus, Sphingomonas sp001421415 GCF 001421415.1 GTDB.r202, Unclassified Blautia A genus, Sphingomonas aerolata, Unclassified Sphingomonas genus, Unclassified Actinomycetia class, Sphingomonas aerolata GCF 000733135.1 GTDB.r202, Methylobacterium sp001422985 GCF 001422985.1 GTDB.r202, Sphingomonas aerolata GCF 000732685.2 GTDB.r202, Sphingomonas aerolata GCF 001422525.1 GTDB.r202, Mycobacterium sp001426545 GCF 001426545.1 GTDB.r202, Unclassified Burkholderiaceae family, Unclassified Nocardioides genus, Rhodococcus B fascians, Unclassified Methylobacterium genus, Unclassified Sphingomonadaceae family, Unclassified Clavibacter genus, Rathayibacter sp002930885 GCF_002931755.l_GTDB.r202, Sphingomonas spOOl 421745

GCF 001421745.1 GTDB.r202, Unclassified Rhodococcus B genus, Unclassified Bacteroidia class, Porphyromonas gingivicanis GCF 000614585.1 GTDB.r202, Porphyromonas A cangingivalis, Moraxella canis GCF 002014965.1 GTDB.r202, Porphyromonas A cangingivalis GCF 000766005.1 GTDB.r202, Porphyromonas A cangingivalis GCF 900167355.1 GTDB.r202, Bergeyella zoohelcum GCF 000301095.1 GTDB.r202, Neisseria weaveri GCF 900086555.1 GTDB.r202, Acinetobacter johnsonii, Cutibacterium acnes, Capnocytophaga canis, Capnocytophaga canimorsus, Micrococcus luteus, Capnocytophaga cynodegmi GCF 002302475.1 GTDB.r202, Capnocytophaga cynodegmi GCF 000379185.1 GTDB.r 202, Histophilus haemoglobinophilus

GCF 002015075.1 GTDB.r202, Unclassified Paraprevotella genus, Faecalibacterium sp900540455, Neisseria canis GCF 002108495.1 GTDB.r 202, Eikenella shayeganii GCF 000226875.1 GTDB.r202, Neisseria zoodegmatis GCF 900187305.1 GTDB.r202, Unclassified Bacteroidales order, Unclassified Ralstonia genus, Ralstonia insidiosa GCF 001663855.1 GTDB.r202, Blautia A sp900541345, Neisseria animaloris GCF 002108605.1 GTDB.r202, Unclassified Peptacetobacter genus, Ralstonia pickettii B GCF 000020205.1 GTDB.r202, Collinsella intestinalis, Frigoribacterium spOO 1421165 GCF 001421165.1 GTDB.r202, Sphingomonas aurantiaca

GCF 001421685.1 GTDB.r202, Unclassified Enterobacteriaceae family, Neorhizobium soli GCF 001423215.1 GTDB.r 202, Gemella palaticanis GCF 015234765.1 GTDB.r 202, Unclassified Lachnospiraceae family, Unclassified Flavobacteriales order, Unclassified Clostridia class, Turicibacter sp002311155, Methylobacterium spOOl 422375 GCF 001422375.1 GTDB.r 202, Paracoccus marcusii, Arthrobacter D sp001422665 GCF 001422665.1 GTDB.r202, Nocardioides glacieisoli GCF 004137245.1 GTDB.r202, Unclassified Actinomycetales order, Unclassified Proteobacteria phylum, Porphyromonas gulae, Acinetobacter pittii GCF 004360215.1 GTDB.r 202, Cutibacterium acnes GCF 001281065.1 GTDB.r202, Bacteroides sp900766005, Unclassified Capnocytophaga genus, Unclassified Phocaeicola genus, Phascolarctobacterium A sp900552855, Unclassified Streptococcus genus, Unclassified Mycobacterium genus, Porphyromonas A canoris GCF 000769115.1 GTDB.r202, Massilia aureaB GCF 014200505.1 GTDB.r202, Methylobacterium spOOl 423085 GCF 001423085.1 GTDB.r 202, Fusobacterium B sp900554885, Fusobacterium A sp900555845, Unclassified Neisseria genus, Unclassified Prevotella genus, Unclassified Escherichia genus, Capnocytophaga cynodegmi, Buchananella hordeovulneris GCF 001907235.1 GTDB.r202, Pasteurella canis, Unclassified Pasteur ellaceae family, Deinococcus marmoris

[Strawberry _pl metabat2 low PE.004. contigs GTDB. r202 ], Unclassified

Erysipelotrichaceae family, Unclassified Fusobacterium B genus, Streptococcus minor GCF 000377005.1 GTDB.r 202, Pantoea agglomerans, Unclassified Alphaproteobacteria class, Pasteurella dagmatis GCF 900186835.1 GTDB.r 202, Capnocytophaga canis GCF 000827555.1 GTDB.r 202, Unclassified Prevotellamassilia genus, Paracoccus marcusii GCF 019141545.1 GTDB.r 202, Unclassified Corynebacterium genus, Unclassified Brevundimonas genus, Paenibacillus O herberti GCF 002233675.1 GTDB.r202, Blautia hansenii, Pasteurella multocida A GCF 000973525.1 GTDB.r 202, Microbacterium saperdae

GCF 014646775.1 GTDB.r202, Actinomyces bowdenii B GCF 015234535.1 GTDB.r202, Unclassified Psychrobacter genus, Holdemanella sp002299315, Porphyromonas A canoris GCF 000765975.1 GTDB.r202, Unclassified Moraxella A genus, Unclassified Neisseriaceae family, Methylobacterium sp000376345 GCF 001422345.1 GTDB.r 202, Peptacetobacter sp900550335, Unclassified Bifidobacterium genus, Paracoccus marcusii GCF 006151785.1 GTDB.r 202, Unclassified Moraxella genus, Capnocytophaga canimorsus GCF 000220625.1 GTDB.r202, Capnocytophaga canimorsus GCF 002302565.1 GTDB.r202, Porphyromonas A canoris, Curtobacterium flaccumfaciens A, Aliterella sp003003885 GCF 003003885.1 GTDB.r202, Erwinia B gerundensis GCA 001517405.1 GTDB.r 202, Capnocytophaga canimorsus

GCF 002302655.1 GTDB.r202, Microbacterium foliorum

GCF 000956415.1 GTDB.r 202, Capnocytophaga canimorsus

GCF 002302445.1 GTDB.r202, Porphyromonas crevioricanis

GCF 900167225.1 GTDB.r202, Paracoccus marcusii GCF 000967825.1 GTDB.r202,

GCF 001564455.1 GTDB.r202, Kocuria rhizophila GCF 002861865.1 GTDB.r202, Weissella confusa GCF 018390755.1 GTDB.r202, Clavibacter michiganensis K, Variovorax ginsengisoli GCF 006438845.1 GTDB.r202, Terribacillus saccharophilus GCF 002884435.1 GTDB.r202, Ruminococcus B gnavus, Frondihabitans sp001423105 GCF 001423105.1 GTDB.r202, Turicibacter sp001543345, Porphyromonas gulae GCF 000971515.1 GTDB.r202, Actinomyces GCF 016598775.1 GTDB.r202, Frigoribacterium endophyticum GCF 001423665.1 GTDB.r202, Plantibacter flavus GCF 900177615.1 GTDB. r202, Plantibacter spOOl 423185

GCF 001421315.1 GTDB.r202, Unclassified Frigoribacterium genus, Porphyromonas gulae GCF 000378065.1 GTDB. r 202, Unclassified Weeksellaceae family, Lactobacillus acidophilus, Dubosiella newyorkensis, Brevundimonas intermedia GCF 004614235.1 GTDB.r202, UBA7173 sp001701135, Anaerobiospirillum succiniciproducens, Clavibacter michiganensis K GCF 002931135.1 GTDB.r202, Bifidobacterium animalis GCF 000612705.1 GTDB.r 202, Fusobacterium A sp900015295,

Unclassified Porphyromonadaceae family, Phocaeicola vulgatus, Unclassified Hymenobacter genus, Sanguibacter inulinus GCF 015234745.1 GTDB. r 202, Romboutsia timonensis GCF 900106845.1 GTDB.r202, Methylobacterium sp000372825 GCF 000372825.1 GTDB.r202, Psychrobacter sp001652315

GCF 007280595.1 GTDB.r202, Peptacetobacter hiranonis, Unclassified

Mycobacteriaceae family, Frigoribacterium sp000878135 GCF 001421865.1 GTDB.r202,

Fusobacterium canifelinum, Mycobacterium sp001428895 GCF 001428895.1 GTDB.r202,

Acinetobacter guillouiae, Deinococcus aquatilis GCF 000378445.1 GTDB.r202, Aeromicrobium fastidiosum GCF 017876595.1 GTDB.r202, Atopobiaceae Jenna _p2 metabat2 low PE.018.contigs GTDB.r202, Eisenbergiella sp900539715,

Porphyromonas gulae GCF 000769475.1 GTDB.r202, Unclassified Acidovorax genus,

Muribaculum sp002492595, Curtobacterium flaccumfaciens A

GCF 001864905.1 GTDB. r 202, Unclassified Curtobacterium genus, Pantoea eucalypti

GCF 900167425.1 GTDB.r202, Curtobacterium flaccumfaciens A

GCF 000349565.1 GTDB. r202, Methylobacterium sp001422815

GCF 001422165.1 GTDB.r202, Microcoleus sp000317475

GCF 000317475.1 GTDB.r202, Porphyromonas macacae

GCF 000379945.1 GTDB. r202, Pararhizobium sp001426685

GCF 001426685.1 GTDB. r202, Pararhizobium sp001424045

GCF 001424045.1 GTDB.r202, Faecalimonas sp900550235, Allobaculum stercoricanis, Muribaculum sp002492595 GCF 004803695.1 GTDB.r202, Porphyromonas gingivalis,

Mycobacterium sp002043095 GCF 002043095.1 GTDB.r202, Nocardioides exalbidus GCF 900105585.1 GTDB.r202, Pseudomonas E sp002843585

GCF 002843585.1 GTDB.r 202, Peptostreptococcus canis

GCF 014229965.1 GTDB.r202 , and combinations thereof. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 75, about 100, about 125, about 150, about 175, about 200, about 225, or about 230 microbial taxa selected from the group consisting of microbial taxa shown in Figure 9.

Additionally or alternatively, in any of the disclosed methods, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 75 or more, about 100 or more, about 125 or more, about 150 or more, or about 170 or more microbial taxa selected from the group consisting of microbial taxa shown in Figure 17, for example, Acidovorax sp., Acinetobacter sp., Acinetobacter Johnsonii, Acinetobacter _pittii,

Acinetobacter guillouiae, Actinomyces bowdenii B, Actinomyces GCF 016598775.1, Aeromicrobium fcislidiosum, Agreia spOOl 421485, Aliterella sp003003885, Allobaculum sp., Allobaculum stercoricanis, Amulumruptor sp900539915,

Bifidobacterium globosum, Bifidobacterium animalis, Blautia sp900556555, Blautia hansenii, Blautia A sp., Blautia A sp900541345, Brevundimonas sp., Brevundimonas intermedia, Buchananella hordeovulneris, Capnocytophaga canimorsus, Capnocytophaga sp., Capnocytophaga canis, Capnocytophaga cynodegmi, Capnocytophaga canimorsus, Clavibacter californiensis, Clavibacter sp., Clavibacter michiganensis K, Clostridium sp900766315, Collinsella intestinalis, Conchiformibius steedae, Corynebacterium mustelae, Corynebacterium sp., Curtobacterium flaccumfaciens A, Curtobacterium sp., Curtobacterium spOOl 424385, Cutibacterium acnes, Cutibacterium sp., Deinococcus mar mor is, Deinococcus aquatilis, Dubosiella newyorkensis, Eikenella shay eganii, Eisenbergiella sp900539715, Erwinia B gerundensis, Escherichia sp., Faecalibacterium sp900540455, Faecalibaculum rodentium, Faecalimonas umbilicata, Faecalimonas sp900550235, Frederiksenia canicola, Frigoribacterium spOOl 421165, Frigoribacterium endophyticum, Frigobacterium sp., Frigoribacterium sp000878135, Frondihabitans sp001423105, Fusobacterium canifelinum, Fusobacterium A sp900555845, Fusobacterium A sp., Fusobacterium A sp900015295, Fusobacterium B sp., Fusobacterium B sp900554885, Gemella palalicanis, Histophilus haemoglobinophilus, Holdemanella sp002299315,

Hymenobacter sp., Hymenobacter norwichensis, Ileibacterium sp., Kocuria rhizophila, Lactobacillus acidophilus, Massilia aurea B, Methylobacterium sp., Methylobacterium spOO 1423085, Methylobacterium spOO 1422985 ,

Methylobacterium spOOO 376345, Methylobacterium spOO 1422375,

Methylobacterium spOOO 372825, Methylobacterium spOO 1422815,

Pantoea agglomerans, Pantoea eucalypti, Paracoccus mar cusii, Paraprevotella sp., Pararhizobium spOO 1424045, Pararhizobium spOO 1426685, Parasutterella sp000980495, Pasteurella dagmatis, Pasteurella canis, Pasteurella multocida A, Peptacetobacter sp., Peptacetobacter sp900550335, Peptacetobacter hiranonis, Peptostreptococcus canis, Phascolarctobacterium A sp900552855, Phocaeicola sp900546645, Phocaeicola sp., Phocaeicola vulgatus, Plantibacter flavus, Plantibacter sp001423185,

Porphyromonas gingivicanis, Porphyromonas gulae, Porphyromonas sp., Porphyromonas crevioricanis, Porphyromonas gingivalis, Porphyromonas macacae, Porphyromonas A cangingivalis, Porphyromonas A canoris, Prevotella copri, Prevotella sp., Prevotellamassilia sp000437675, Prevotellamassilia sp., Pseudomonas E sp., Pseudomonas E graminis, Pseudomonas E sp002843585, Psychrobacter spOO 1652315, Psychrobacater sp., Ralstonia insidiosa, Ralstonia sp., Ralstonia _pickettii B, Rathayibacter sp002930885, Rhodococcus B sp., Rhodococcus B f asci an s, Rhodococcus B sp002259335, Rhodococcus C spOO 1426185, Romboutsia timonensis, Ruminococcus B gnavus, Sanguibacter inulinus, Schaedlerella sp900765975, Sphingomonas spOO 1421415, Sphingomonas aurantiaca, Sphingomonas aerolata, Sphingomonas sp., Sphingomonas spOO 1421745, Sphingomonas spOO 1421805, Streptococcus sp., Streptococcus minor, Terribacillus saccharophilus, Turicibacter sp002311155, Turicibacter spOO 1543345, UBA 7173 spOOl 701135,

Variovorax ginsengisoli, Weissella confusa, and combinations thereof. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20 , about 25, about 30, about 35, about 40, about 45, about 50, about 75, about 100, about 125, about 150, or about 170 microbial taxa selected from the group consisting of microbial taxa shown in Figure 17,

Additionally or alternatively, in any of the disclosed methods, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, or about 74 microbial taxa shown in Figure 14, for example, Variovorax ginsengisoli, Sphingomonas sp001421805, Sphingomonas sp001421745, Sphingomonas sp001421415, Sphingomonas aurantiaca, Sphingomonas aerolata, Sphingomonas sp, Sanguibacter inulinus, Ruminococcus B gnavus, Rhodococcus C sp001426185, Rhodococcus B sp002259335, Rhodococcus B fascians, Rhodococcus B sp., Pseudomonas E graminis, Psudomonas E sp., Prevotella sp., Phocaeicola vulgatus, Phocaeicola sp900546645, Phocaeicola sp., Phascolarctobacterium A sp900552855, Peptostreptococcus canis, Pararhizobium sp001426685, Pararhizobium sp001424045, Pantoea agglomerans, Nocardioides glacieisoli, Nocardioides exalbidus, Mycobacterium sp002043095, Mycobacterium sp001428895, Mycobacterium sp001426545, Muribaculum sp002492595, Microcoleus sp000317475, Microbacterium saperdae, Methylobacterium sp001423085, Methylobacterium sp001422985, Methylobacterium spOOl 422375, Methylobacterium sp000376345, Methylobacterium sp000372825, Massilia aurea B, Hymenobacter norwichensis, Hymenobacter sp., Gamella palaticanis, Fusobacterium B sp900554885, Fusobacgerium B sp., Fusobacterium A sp900555845, Fusobacterium A sp., Frondihabitans sp001423105, Frigoribacterium sp001421165, Frigoribacterium sp000878135, Frigoribacterium endophyticum, Frigoribacterium sp., Faecalimonas sp900550235, Faecalibacterium sp900540455, Eisenbergiella sp900539715, Deinococcus marmoris, Deinococus aquatilis, Curobacterium sp001424385, Curtobacterium flaccumfaciens A, Clavibacter michganensis K, Clavibacter califoriensis, Clavibacter sp., Buchananella hordeovulneris, Brevundimonas intermedia, Brevundimonas sp., Bifidobacerium animalis, Bacteroides sp900766005, Bacteroides sp., Arthrobacter D sp001422665, Anaerobiospirillum succiniciproducens, Agreia sp001421485, Aeromicrobium fastidiosum, Actinomyces GCF 016598775.1, Acinetobacter johnsonii, Acinetobacter guillouiae, Acidovorax sp., and combinations thereof. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, or about 74 microbial taxa shown in Figure 14.

Additionally or alternatively, in any of the disclosed methods, the one or more microbial taxa comprise about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, or about 10 microbial taxa shown in Figure 24 which are enriched in the gut microbiome of healthy dogs, for example, Prevotella genus, Prevotella copri, Prevotellamassilia genus, Prevotellamassilia sp000437675, Catenibacterium genus, Catenibacterium sp000437715, Prevotella sp., Acidaminococcales order, Acidaminococcaceae family, Phascolarctobacgerium A genus, and combinations thereof. In certain embodiments, the one or more microbial taxa comprise about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, or about 10 microbial taxa shown in Figure 24 which are enriched in the gut microbiome of healthy dogs. In certain embodiments, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, or about 9 microbial taxa shown in Figure 24 which are enriched in dogs with clinical signs of CAD, for example, Firmicutes A sp., Clostridia class, Phocaeicola vulgatus, Ruminococcus B genus, Escherichia genus, Bacteroides ster coris, Escherichia sp., Bacteroides uniformis, Terrisporobacter genus, and combinations thereof. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, or about 9 microbial taxa shown in Figure 24 which are enriched in dogs with clinical signs of CAD,

Additionally or alternatively, in any of the disclosed methods, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, or about 30 or more microbial taxa shown in Figure 26 which are enriched in dogs with clinical signs of CAD, for example Bacteroidia class, Bacteroidota phylum, Staphylococcaceae family, Staphylococcus genus, Actinobacteria class, Actinobacteriota phylum, Staphylococcales order, Bacteroides pyrogenes, Bacillales order, Bergeyella zoohelcum, Kocuria rhizophila, Porphyromonas cangingivalis, Staphylococcus schleiferi, Rhizobiaceae family, Acinetobacter radioresistens, Actinomycetales order, Actinomycetaceae family, Allorhizobium-Neorhizobium- Pararhizobium-Rhizobium genus, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium sp., Capnocytophaga genus, Staphylococcus xylosus, Flavobacterium sp., Flavobacterium genus, Cutibacterium sp., Nocar dioidaceae family, Nocardioides genus, Aureimonas sp., Auerimonas genus, Porphyromonas gingivicanis sp., and combinations thereof. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, or about 30 microbial taxa shown in Figure 26 which are enriched in dogs with clinical signs of CAD

Additionally or alternatively, in any of the disclosed methods, the one or more microbial taxa comprise about 1 or more, about 2 or more, about 3 or more, or about 4 microbial taxa shown in Figure 26 which are enriched in healthy dogs, for example, Clostridia class, Enterobacteriaceae family, Streptococcus mitis, Acinetobacter johnsonii, and combinations thereof. In certain embodiments, the one or more microbial taxa comprise about 1, about 2, about 3, or about 4 microbial taxa shown in Figure 26 which are enriched in healthy dogs.

Additionally or alternatively, in any of the disclosed methods, the skin microbiome of the subject exhibits: a) an abundance or relative abundance of one or more microbial taxa that is less than the expected minimum relative abundance shown in Figure 9; b) an abundance or relative abundance of one or more microbial taxa that is greater than the expected maximum relative abundance shown in Figure 9; c) an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa selected from the group consisting of Bacteroidia class, Bacteroidota phylum, Staphylococcaceae family, Staphylococcus genus, Actinobacteria class, Actinobacteriota phylum, Staphylococcales order, Bacteroides pyrogenes, Bacillales order, Bergeyella zoohelcum, Kocuria rhizophila, Porphyromonas cangingivalis, Staphylococcus schleiferi, Rhizobiaceae family, Acinetobacter radiore sistens, Actinomycetales order, Actinomycetaceae family, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium genus, Allorhizobium- Neorhizobium-Pararhizobium-Rhizobium sp., Capnocytophaga genus, Staphylococcus xylosus, Flavobacterium sp., Flavobacterium genus, Cutibacterium sp., Nocardioidaceae family, Nocardioides genus, Aureimonas sp., Auerimonas genus, Porphyromonas gingivicanis sp., and combinations thereof; or d) a reduction, decreased abundance, or decreased relative abundance of one or more microbial taxa selected from the group consisting of Clostridia class, Enterobacteriaceae family, Streptococcus mitis, Acinetobacter johnsonii, and combinations thereof.

Additionally or alternatively, in any of the disclosed methods, the subject is a domestic animal. In certain embodiments, the domestic animal is a dog.

Additionally or alternatively, any of the disclosed methods can include performing an assay on a sample to measure an amount of a microbial nucleic acid. Bacterial community profiles within a skin microbiome of an animal can vary depending on the source of a sample taken from the animal. In certain embodiments, the sample is collected from a skin site, e.g., haired skin site, mucosal surface, mucocutaneous junction, ear canal, interdigital region of the paw, dorsal lumbar, right groin, abdomen, or a combination thereof. In certain embodiments, the sample is from the gastrointestinal tract, e.g., a faecal sample, an ileal sample, a jejunal sample, a duodenal sample or a colonic sample. In certain embodiments, the sample is collected from a haired skin site. In certain embodiments, the sample is collected from a mucosal surface or a mucocutaneous junction. In certain embodiments, the sample is collected from an ear canal, interdigital region of the paw, dorsal lumber, right groin, or a combination thereof. In certain embodiments, the sample is collected using a swab, e.g., a swab, flocked swab. In certain embodiments, the sample is collected using a swab soaked in sterile wetting solution. In certain embodiments, at least one sample, or at least two samples, or at least three samples, or at least four samples are collected from each animal. In certain embodiments, samples are collected from at least one skin site, or at least two skin sites, or at least three skin sites, or at least four skin sites from each animal. In some embodiments, the sample is obtained from a conscious animal or from an unconscious animal.

The methods and kits of the disclosed subject matter can be used to detect microbial taxa, e.g., bacteria, present on samples collected from the subject. For purposes of example, any of the disclosed methods can include performing an assay for testing for the presence and/or relative amounts of any bacteria disclosed herein. In certain embodiments, the one or more microbial taxa are one or more bacteria. In some embodiments, the one or more bacteria associated with a skin disease or disorder is selected from the group consisting of microbial taxa shown in Figure 9, Figure 14, Figure 17, Figure 24, and Figure 26.

Additionally or alternatively, any of the disclosed methods can include performing a universal polymerase chain reaction (PCR) assay which detects the presence of bacterial DNA in the dog skin or gut microbiome. Universal primers, and how to create them, are known to skilled people in the art. Examples of methods relating to universal primers include those described in Ott et al., J. Clin. Microbiol. 2004 Jun; 42(6): 2566 -2572. Doi: 10.1128/JCM.42.6.2566-2572.2004, the contents of which is incorporated by reference in its entirety.

As known as above, various assays for identifying the presence of bacteria or other markers associated with a skin disease or disorder or good skin health are known in the art. In certain embodiments, the assay is polymerase chain reaction (PCR). In certain embodiments, the assay is quantitative polymerase chain reaction (qPCR). In certain embodiments, the assay includes DNA sequencing. In certain embodiments, the assay includes shotgun metagenomics sequencing. In certain embodiments, the microbial nucleic acid can be a microbial DNA or RNA, e.g., a 16S ribosomal DNA (rDNA) or 16S ribosomal RNA (rRNA).

The methods can also include a step of extracting a nucleic acid, e.g., performing a DNA or RNA extraction, according to methods known in the art prior to performing the PCR assay. Generally, a DNA extraction can be performed by lysing the cells containing the DNA and precipitating and purifying the DNA.

Additionally or alternatively, any of the disclosed methods can include detecting bacteria by testing the sample for the presence of bacteria. In certain embodiments, testing the sample can include for presence and/or relative abundance of one or more of the bacteria disclosed herein, e.g., bacteria associated with a skin disease or disorder, bacteria associated with good skin health, or both. In certain embodiments, testing the sample can include detecting the abundance or increased relative abundance compared to a training data set (e.g., bacteria associated with good skin health, bacteria associated with a skin disease or disorder, bacteria not associated with good skin health or skin disease or disorder, and combinations thereof). Detecting a presence or relative increased abundance of one or more of the bacteria disclosed herein can, for instance, indicate that the animal has or is susceptible to developing a skin disease or disorder.

Additionally or alternatively, any of the disclosed methods can include detecting bacteria by testing the sample for an absence or relatively low abundance of bacteria. In certain embodiments, testing the sample can include for the absence or relatively low abundance of one or more of the bacteria disclosed herein, e.g., bacteria associated with a skin disease or disorder. In certain embodiments, testing the sample can include detecting the decreased abundance or decreased relative abundance compared to a training data set. Detecting the absence or relatively low abundance of the one or more of the bacteria associated with the skin disease can, for instance, indicate that the animal does not have an skin disease or disorder or is less likely to develop a skin disease or disorder.

In certain embodiments, the reference abundance or relative abundance of the one or more microorganism corresponds to the abundance or relative abundance of the one or more microorganisms in one or more healthy animal. In certain embodiments, the reference abundance or relative abundance of the one or more microorganism corresponds to the abundance or relative abundance of the one or more microorganisms in one or more animal with skin disease. Non-limiting examples of animals with skin disease include animals having clinical signs of CAD. In certain embodiments, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 75 or more, about 100 or more, about 125 or more, about 150 or more, about 175 or more, about 200 or more, about 225 or more, or about 230 selected from the group consisting of microbial taxa shown in Figure 9 In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 75, about 100, about 125, about 150, about 175, about 200, about 225, or about 230 selected from the group consisting of microbial taxa shown in Figure 9 In certain embodiments, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, about 75 or more, about 100 or more, about 125 or more, about 150 or more, or about 170 or more microbial taxa selected from the group consisting of microbial taxa shown in Figure 17. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, about 75, about 100, about 125, about 150, or about 170 microbial taxa selected from the group consisting of microbial taxa shown in Figure 17. In certain embodiments, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, about 30 or more, about 35 or more, about 40 or more, about 45 or more, about 50 or more, or about 75 or more microbial taxa shown in Figure 14. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, about 30, about 35, about 40, about 45, about 50, or about 75 microbial taxa shown in Figure 14. In certain embodiments, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, or about 10 microbial taxa shown in Figure 24 which are enriched in healthy dogs. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, or about 10 microbial taxa shown in Figure 24 which are enriched in healthy dogs. In certain embodiments, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, or about 9 microbial taxa shown in Figure 24 which are enriched in dogs with clinical signs of CAD. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, or about 9 microbial taxa shown in Figure 24 which are enriched in dogs with clinical signs of CAD. In certain embodiments, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, or about 4 microbial taxa shown in Figure 26 which are enriched in healthy dogs. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, or about 4 microbial taxa shown in Figure 26 which are enriched in healthy dogs. In certain embodiments, the one or more microbial taxa is about 1 or more, about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 15 or more, about 20 or more, about 25 or more, or about 30 or more microbial taxa shown in Figure 26 which are enriched in dogs with clinical signs of CAD. In certain embodiments, the one or more microbial taxa is about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 15, about 20, about 25, or about 30 microbial taxa shown in Figure 26 which are enriched in dogs with clinical signs of CAD.

Additionally or alternatively, the disclosed methods can include administering to the animal a therapeutically effective amount of a topical treatment, non-medicated shampoo, medicated shampoo, a therapeutic (e.g., an antibiotic), or a combination thereof. In certain embodiments, the methods comprise administering a topical treatment or shampoo which is formulated to improve the skin health status when the health status is “not health” or “skin disease”.

Additionally or alternatively, the disclosed methods of using the kits of the disclosed subject matter can include testing the sample for the presence and/or relative amounts of microbes associated with skin health. In certain embodiments, the testing includes for the presence and/or relative amounts of a bacterial nucleic acid (e.g., DNA or RNA).

Additionally or alternatively, in any of the methods disclosed herein, the detection of the presence of bacteria or other markers can include measuring the amounts of bacteria or other markers, and the amounts can be compared to a scale that correlates the amount of bacteria or other markers to the likelihood that the animal has skin disease or disorder or poor skin health. The likelihood can be indicated as a percentage. For purpose of example and not limitation, the Cq (cycle quantitation) score of a qPCR test that detects the nucleic acid (e.g. DNA or RNA) of bacteria associated with skin disease or disorder can be used to create the scale for calculating the likelihood that the animal has skin disease or disorder. A lower Cq score can indicate the presence of higher levels of the bacteria associated with skin disease or disorder and therefore the likelihood that the animal has skin disease or disorder can be higher than the animal with a higher Cq score. In certain embodiments, all qPCR data can be normalized to the level of a universal assay for each sample; this adjusts the data for differences in the overall amount of total bacterial DNA in each sample, i.e., yield the abundance relative to the total bacterial population. The data can be then linearised, such that the final qPCR data outputs are relative proportions (2'^{( Cq Test} - c_q.Totai) j_{n cer}^_ajn embodiments, Cq.Test refers to the Cq score associated with a microbial species. Samples with Cq.Test values outside of the reliable range of the assay (where Cq>21) can be assumed to have undetectable amounts of DNA and therefore those relative proportions can be imputed as the limit of quantification or 0. Cq and Ct (cycle threshold) can be used interchangeably.

For purpose of example and not limitation, a report can be generated summarizing the results of sample testing. In certain embodiments, electronic communications can be used to communicate the report. For example, a personalized report can be generated and sent to communicate the animal’s skin health status. In other embodiments, the report can be provided as a hard copy. The personalized report can, for example, include an indicator system such as a traffic light system, e.g., green, yellow, red, to communicate the skin health status of the animal. The personalized report can also include a representation of the scale as reference above and an indication of where the animal’s skin health falls on the scale, e.g., 0% is indicative of no disease and 100% is indicative of severe disease.

In certain embodiments, the skin health status of the animal is “health”, “skin health”, or “not skin disease”. In certain embodiments, the skin health status of the animal is predicted to be “not health” or “skin disease”. In certain embodiments, the animal has or is suspected to have a skin disease or disorder. In certain embodiments, the animal is suspected to have a skin disease or disorder due to excessive scratching or licking. In certain embodiments, the skin health status comprises a skin disease or disorder. In certain embodiments, the skin disease or disorder is dermatitis, psoriasis, atopic dermatitis, cutaneous form of food allergy, pruritic diseases, bacterial folliculitis, furunculosis, allergic dermatitis, pyoderma, mange, and immune or auto-immune dermatitis. Additionally or alternatively, any of the method disclosed herein can include quantifying the one or more microbial taxa in samples obtained from the subject on at least two time points. In certain embodiments, the two time points are at least 6 months or 1 year apart.

EXAMPLES

The present disclosure will be better understood by reference to the following Examples, which are provided as exemplary of the presently disclosed subject matter, and not by way of limitation.

EXAMPLE 1: Metagenomic characterisation of canine skin reveals a core healthy skin microbiome

Furthering knowledge of the skin microbiome is essential to understand health and disease in canines. To date, studies into the canine skin microbiome have focused on 16S rRNA high throughput sequencing however, these lack the granularity of species and strain level taxonomic characterisation and their associated functions. The aim of this study was to provide a comprehensive assessment of the skin microbiome by analysing the skin microbiome of 75 healthy adult dogs, across four distinct skin sites and four breeds, using metagenomic sequencing to a mean depth of 28 million reads per sample. Results show that breed and skin site are drivers of variation, and a core group of taxa and genes are present within the skin microbiome of healthy dogs, comprising 230 taxa and 1,219 gene families. 15 species are identified within the core microbiome that are represented by more than one strain. The biosynthesis of secondary metabolites pathway was enriched in the core microbiome showing that the skin microbiome can play a role in colonisation resistance and protection from invading pathogens.

Methods

Cohort Description. The cohort comprised 75 healthy adult dogs housed at the Waltham Petcare Science Institute (Leicestershire, UK). The dogs belonged to four breeds: Labrador Retriever (n=40), Beagle (n=13), Petit Basset Griffon Vendeen (n=7) and Norfolk Terrier (n=15). At the time of sampling, the cohort had a mean age of 3.8 years (range 0.9- 9.3 years) and a mean bodyweight of 19.93 kg (range 3.55-36.20 kg). The cohort contained 38 male and 37 female dogs of which 51 were neutered and 24 were entire. The metadata associated with the cohort is provided in Table 2. Dogs were housed on the four different kennel units across the site and remained cohabiting with the same dog(s) in the week preceding sample collection (moves were permitted for behaviour and welfare reasons). On the day of sample collection, dogs were either boarded into their pen area or given access to the outside paddock area if it was paved but were not exercised off-lead or socialised with any dog other than their cohabitating paddock group (maximum four dogs) until sampling had been completed.

Table 2. Cohort Metadata.

The dogs had no concurrent illnesses that impact the skin, had not been treated with any antimicrobials in the three months prior to sampling, and had not been washed with any detergent or shampoo, nor had any topical treatments in the previous 24 hours to sampling. All dogs were fed commercially available complete dry diets throughout the study, appropriate for their life stage (puppy, adult) and/or weight and diet remained consistent throughout the sampling phase.

Sample Collection. Skin microbiome samples were collected from four different sites from all dogs, totalling 300 samples. The skin sites selected for sampling were: right ear canal, interdigital region of the left fore paw, dorsal lumbar and right groin, named A, B, C and D respectively (Figure 1A). For each skin site, two sterile flocked swabs with 80 mm breakpoint (Norgen Biotek Corp.) were used. Swabs were soaked in sterile wetting solution (Tris buffer (pH 8.0), 2 mM EDTA and Triton X-100, Norgen Biotek Corp.) then applied, with rotation, to the desired skin area for a period of at least one minute. For the interdigital region, sample was collected from the skin region between each digit of the left fore paw. Duplicate swabs for each sample site were stored in stabilisation solution (Swab Collection and DNA Preservation System, Norgen Biotek Corp.) and stored at 4°C until DNA extraction. To minimise cross contamination between sample sites and individual animals, a new pair of nitrile gloves were worn by the sampling person for each sampling site.

DNA Extraction. DNA extraction was conducted at Alkek Center for Metagenomics and Microbiome Research (CMMR), Baylor College of Medicine. Genomic DNA from skin swabs were extracted using the Saliva DNA Isolation Kit (Norgen Biotech Corp.) according to the manufacturers Supplementary Protocol for the Isolation of DNA from Norgen’s Swab Collection and DNA Preservation System using Norgen’s Saliva DNA Isolation Kit.

Shotgun Sequencing. Library preparation and shotgun sequencing was conducted at Alkek Center for Metagenomics and Microbiome Research (CMMR), Baylor College of Medicine. Six samples were removed for failure to reach the desired thresholds for sequencing. These included four samples from the same dog (DogNo. 2). A total of 294 collected samples from 74 dogs were successfully sequenced to a depth of 5gb/sample using an Illumina NovaSeq S4 sequencer. A negative control and positive control (ATCC MS Al 003) were also included to check pipeline sensitivity and specificity.

Bioinformatics. Sequencing data was processed at Diversigen using the MetaGene™ Canine pipeline with taxonomic assignments and direct functional profiling via alignment to a canine specific curated reference database (Diversigen, USA). The mean read depth per sample was 28 million reads (min 9,710, max 122 million). Post host removal, the mean read depth per sample was 7.68 million reads. Of these reads, a large proportion could not be assigned to taxonomy; reads mapping ranged from 2,642 reads (0.76%) to 6.6 million reads (68.97%), mean 9.1 million reads (12.50%). Previously, RefSeq was used to assign taxonomy to this dataset and a range of 0.07-2.00% of reads mapping (mean 0.63%) was observed. For gene families, a mean of 405,311 reads mapping post host removal (5.55%) was observed, with a range of 1,104 reads (0.18%) to 3.2 million reads (33.08%). A conservative threshold of 500,000 reads was applied to be mapped to taxa for samples to be taken forward in the study. This was based on previous published observations of sequencing depth required for shallow shotgun sequencing. This reduced the number of samples by 55%, from 294 samples from 74 different dogs to 162 samples from 72 different dogs.

Statistical Analysis. Downstream analyses were performed using R version 4.1.3 and the raw count tables (taxonomic and gene families), using only the samples that have reached the 500,000 mapped reads threshold (n=162). Prior to analyses, a noise removal step was conducted. Any count that was lower than 0.01% of total counts was inputted with 0, any taxa that had 0 across all samples was subsequently removed. Additionally, taxa that were only present in one sample were removed. Prior to alpha diversity calculations, samples were rarefied to the read depth of the lowest sample (502,635 reads). Alpha diversity analyses to compare the diversity of communities within samples were assessed using two metrics; Shannon Diversity to assess evenness and Observed Species to assess richness. Shannon diversity and species richness of each sample were, separately, fit to a linear mixed effects model with the relevant grouping (skin site or breed) as the fixed effect and individual dog as the random effect. Contrasts were made between the groups, and the family-wise 95% confidence intervals and p values were obtained. Beta diversity analyses to compare the diversity of communities between samples was assessed using Bray-Curtis dissimilarity on relative abundances and visualised using non-metric multidimensional scaling (NMDS). PERMANOVA (permutational multivariant analysis of variance) was used to assess statistical differences between skin sites and confounding factors (breed and sex). Pairwise PERMANOVA was implemented with the pairwise Adonis package and p values adjusted using the Bonferroni method. Dispersion tests were conducted on the same pairwise comparisons using the vegdist function from the vegan package and p values adjusted using the Bonferroni method. Prevalence of a feature (taxon or gene) was estimated per skin site defined as the proportion of samples where the feature was present. The number of features that was observed above a certain prevalence threshold (cumulative number of features) was then plotted. Core was defined as the overlap of features across the four sites; all other features were categorised as accessory. For comparisons between core and accessory taxa, abundances at the phylum level were estimated relative to the counts in the core or accessory sets, respectively. KEGG pathway enrichment analysis was conducted using MicrobiomeProfiler. All detected genes were used as universe background genes. Adjustment of p values was performed using the Benjamini & Hochberg method.

Results

Overview of the Healthy Canine Skin Microbiome. The skin microbiome was analyzed from four distinct skin sites: right ear canal, interdigital region of the left fore paw, dorsal lumbar and right groin, named A, B, C and D, respectively (Figure 1A). A total of 2,687 strains could be identified as 2,137 species, 624 genera, 172 families, 35 classes and 26 phyla. The canine skin microbiome is dominated by three phyla: Proteobacteria, Bacteroidota and Actinobacteriota, with these phyla accounting for 85% of the total count. At lower abundances (1-7%), Firmicutes, Firmicutes A and Fusobacteriota were detected. Taxa that were mapped to the Kingdom Bacteria but unassigned at phylum level were termed “unresolved.” Approximately 2% of all reads across sites were unresolved and represent potential novel phyla. At the phylum level the microbial profile was consistent across skin sites with the three most abundant phyla in the same order across all sites; Proteobacteria accounting for 36-40% of the total microbial composition, Bacteroidota accounting for 27- 31% and Actinobacteriota for 16-23% (Figure IB). At the family level, Porphyromonadaceae, Moraellaceae and Neisseriaceae were identified as the most abundant across all samples (Figure 1C).

Breed and Skin Site are the Main Drivers for Variation in the Canine Skin Microbiome. To understand the main drivers of variation within the canine skin microbiome, variation in skin microbiome between breed, skin site and sex were investigated using Bray- Curtis dissimilarity visualised using nMDS. Breed (Figure 2A) and skin site (Figure 2B) showed separation on nMDS whereas sex (Figure 2C) had no effect. PERMONOVA showed statistically significance differences in the microbial profile between three skin site comparisons (interdigital/groin; interdigital/dorsal lumbar; groin/dorsal lumbar; adjP <0.01). All comparisons between ear canal and other skin site showed no statistical significance. The interdigital and groin sites were most different and were driving 4.7% of the variation (R2 = 0.047, adjP = 0.006). The comparisons between groin and dorsal lumbar sites (R2 = 0.037, adjP = 0.006) and between interdigital and dorsal lumbar (R2 = 0.030, adjP = 0.006) were the second and third biggest drivers for variation in the skin microbiome sites. Breed was the biggest driver for variation across the skin microbiome using Bray-Curtis dissimilarity and all comparisons showed statistical significance when tested using PERMONAVA (adjP <0.05, Table 3). The biggest variation was observed between Beagles and Norfolk Terriers, which were driving 6.5% of the variation in the skin microbiome (R2 = 0.065, adjP = 0.006). Additionally, over 5% of the variation of the skin microbiome can be explained by the differences between Beagles and Petit Basset Griffon Vendeen (R2 = 0.052, adjP = 0.042), and between Labrador Retrievers and Norfolk Terriers (R2 = 0.052, adjP = 0.006). Additionally, a dispersion test was conducted for each comparison within breed, skin site and sex (Table 4). Skin site and sex were not statistically significantly dispersed. For breed, Beagles were more dispersed than Norfolk Terriers (adjP = 0.017) and Petit Basset Griffon Vendeen (adjP = 0.008).

Table 3. Pairwise comparisons using PERMANOVA. Table 4. Pairwise comparisons using dispersion test.

Richness metrics. The Shannon diversity of the canine skin microbiome ranged between 4.56 and 5.64 across sites and breeds. The species richness of the canine skin microbiome ranged between 574 and 885 across sites and breeds. There was no observed statistical significance in Shannon diversity between skin sites (Figure 3 A) however, the ear canal and interdigital sites were statistically significantly different when measured using species richness (p <0.05, Figure 3B). No other skin sites showed statistical significance in species richness. There were no statistically significant differences in alpha diversity between breeds by either metric (Figures 3C and 3D).

Core Canine Skin Microbiome. Due to the interface between the skin and the environment, experiments were conducted to determine whether a core microbiome existed that was present across most dogs, and an accessory microbiome was present that only occurred across a small percentage of the population. The prevalence of taxa identified on each canine skin site was assessed. The prevalence of a taxa was determined by assessing the proportion of samples within which that taxon appeared (Figure 4A). Subsequently, taxa prevalent at defined thresholds between 10 and 100% across the four sites were overlaid to determine those taxa consistent across all sites (Figure 4B). Based on cumulative taxa curves, the majority of bacterial diversity occurred in less than 25% of dogs. Furthermore, when overlaying taxa shared across sites, there was a consistent proportion of taxa shared between 25 and 80% of dogs (59 - 61%), which decreased sharply when setting a higher threshold than 80% prevalence across dogs. Based on this, a prevalence threshold of 80% was used to define core. At this threshold a total of 375 taxa were detected across all dogs, with 230 of these identified across all sites and termed core (Figure 4B). All other taxa were termed accessory (a total number). The top three phyla represented in the core and accessory skin microbiome remained the same; Proteobacteria, Bacteroidota and Actinobacteriota however, of note the proportion of Bacteroidota appeared greater in the core microbiome than the accessory. Additionally, a higher proportion of “unresolved” taxa (taxa that were mapped to the kingdom bacteria but not assigned to taxonomy at phyla level) were identified in the core microbiome in comparison to the accessory (Figure 4C). To further investigate the differences within the core and accessory at phyla level, the fold change of the relative abundance of each phylum was investigated between the core and accessory microbiomes at each skin site (Figure 5). Bacteroidota was highly abundant within the core microbiome in comparison to the accessory (fold change >1) across all skin sites. Additionally, Fusobacteriota was observed to be more highly abundant in the core microbiome rather than the accessory at the dorsal lumbar site, whereas in the ear canal, groin, and interdigital region of the paw it was more highly abundant in the accessory microbiome. Within the accessory microbiome Proteobacteria, Firmicutes (including Firmicutes A and C), Deinococcota and Cyanobacteria were identified as more highly abundant (fold change <1) than in the core microbiome. Other phyla were noted as being completely absent from the core microbiome and only present in the accessory as shown by phyla with a fold change of zero in Figure 3. These included Spirochaetota, Myxococcota, Methanobacteriota, Firmicutes B, Desulfobacterota, Deferribacterota, Chlamydiota and Campylobacterota which were present in the accessory microbiome in all skin sites and absent from the core, showing that the core and accessory microbiomes are mutually exclusive at phylum level.

Within the core canine skin microbiome, a total of 113 strains was identified that could be classified as 136 species, 93 genera, 44 families, 28 orders, 11 classes and 10 phyla. A list of the 230 taxa in the core microbiome can be found in Figure 9. The most represented families within the core microbiome are represented by members of Microbacteriaceae, Porphyromonadaceae, Flavobacteriaceae, Bacteroidaceae, Moraxellaceae, Lachnospiraceae and Spingomonadaceae . The most represented genera within the core microbiome were Capnocytophaga, Porphyromonas, Sphingomonas, Methylobacterium and Porphyromonas A, where greater than five species belonging to each genus were identified. Porphyromonas A was the most abundant genera within the canine skin microbiome with an average abundance of 5.7-7.2% across all sites. Cutibacterium (1.2-5.0%), Spingomonas (1.3-3.4%) and Psychrobacter (1.0-2.1%) were also within the most abundant genera of the canine skin microbiome across all skin sites (Figure 6). A total of 15 species were detected in the core microbiome where more than one strain belonging to the species was present, a list of these species can be found in Table 5. Of particular interest, Capnocytophaga canimorsus and Porphyromonas gulae were represented by five strains each, and Capnocytophaga canis, Paracoccus marcusii and Sphingomonas aerolata were represented by four strains each.

Table 5. List of species of interest within the canine core microbiome that are represented by two or more strains

Core Function of the Canine Skin Microbiome. The gene family metagenomic data were analyzed to understand the core function of the canine skin microbiome. To establish the core function, a similar approach to that employed for the taxonomic core microbiome was used firstly by assessing the prevalence of gene families identified on each canine skin site (Figure 7A). The cumulative number of genes decreased rapidly after 90% prevalence in dogs and increased rapidly after 12.5% of dogs. Furthermore, between thresholds of 10 and 90%, the proportion of gene families shared across sites was consistent, varying between 80- 86%. After this point, the proportion of shared gene families decreased as the threshold was increased. Based on this a prevalence threshold of 90% was selected for further study where a total of 1,538 gene families were detected. From the 1,538, 1,219 gene families were identified across all sites and were deemed core (Figure 7B). All other gene families were classified as the accessory microbiome (total number).

The most abundant functions within the core microbiome were functions involved in DNA, energy and intermediary metabolism, such as basic replication machinery genes such as gyrA and gyrB,' and genes involved biosynthesis of nucleotides such as nrdE and nrdE (Figure 7C). Additionally, genes involved in lipid metabolism, such as fad IP and RNA metabolism, rpoB and rpoC, were highly abundant. More interestingly, genes associated with the active transport of large receptor molecules were discovered within the top 20 most abundant genes within the core; cirA, c rA, hmuR (Figure 7C). These genes encode outer membrane receptors associated with the transport of ferrienterobactin and colicins. Ferri enterochelin is also known as ferrienterochelin which is iron siderophore that contains an enterobactin, and colicin is a type of bacteriocin. Other genes encoding enterobactin exporter systems were also identified, such as entS. Upon delving deeper into the genes present in the core functional microbiome, the structural gene for lantibiotic Pep5 was detected; pepA. Other genes from the Pep5 gene cluster were also identified, pep' pepN, pepP,pepD, which include the genes encoding the transport protein for PepA (pepT) and the serine protease for proteolytic processing of PepA (pepP).

Other commonly occurring genes of interest within the core functional microbiome were genes involved in multidrug resistance, such as proteins belonging to the Resistance Nodulation Cell Division (RND) superfamily. Genes encoding multidrug and toxic compound extrusion (MATE) transporters and hydrophobic/amphiphilic exporters (HAE) were identified. Although RND systems are involved in maintaining cell homeostasis, they are also known to broad substrate spectrums and roles in drug resistance. Within the identified core functional microbiome genes involved in multidrug efflux systems are: mdtA, mdtB, mdtC, mdtE, mdlK. mdtN, emrA. emrB. acrA, mexA. mexB. tcaB and mepA. Genes encoding penicillicin binding proteins, mrcB. pbp2A. pbpB. pbp(\ were also detected in the core functional microbiome. Finally, genes that provide colonisation resistance to resident microbes were also identified within the core, such as genes associated with adhesins, biofilms and hemolysis; icaA. fim(ffiml). tlyC respectively.

To further investigate differences within the core and accessory function of the canine skin microbiome, an enrichment analysis was conducted at pathway level. The hypergeometric test was conducted to test over-representation of pathways in the core genes of the canine skin microbiome (Figure 8A). Most of these pathways are ones that are essential for bacterial survival, such as pathways for DNA replication, aminoacyl-rRNA biosynthesis, carbon metabolism and biosynthesis of cofactors. A pathway for pantothenate (vitamin B5) and CoA (coenzyme A) biosynthesis was identified as enriched within the core. CoA is essential in various metabolic reactions, including in the synthesis of phospholipids which are a major class of lipids represented in the skin. Additionally, the average relative abundance of the pathways enriched in the core was assessed (Figure 8B). This revealed the biosynthesis of secondary metabolites pathway as the most highly abundant. Secondary metabolites can include bioactive compounds and antimicrobials which can mediate environmental responses or interactions between members of the microbiome.

Discussion

This study utilized a shotgun metagenomic approach to characterise the healthy canine skin microbiome of 75 dogs and establish a core microbiome at the taxonomic and functional level. In addition, this study assessed four distinct skin sites and four breeds to determine factors influencing variation within the healthy canine skin microbiome.

Previous studies into the canine skin microbiome have generally utilised 16S sequencing approaches where the hypervariable region of the 16S gene studied can bias the reported microbial composition. There is only one study published to date utilising shotgun sequencing for a small number of samples (n=l 1) for canine skin samples. The specificity of databases, such as RefSeq, for canine skin samples has been documented as a pitfail to utilising shotgun sequencing approaches. In this study, a canine-specific database (MetaGeneCanine™, Diversigen, US) was employed to enhance taxonomic assignment and increase species and strain resolution. Overall, relatively low mapping rates (mean 12.5% mapped reads) was experienced however, this is still a substantial uplift on the mapping reads observed when using RefSeq to assign taxonomy to this dataset (mean 0.65% mapped reads). The low mapping rate shows there is novelty within the canine skin microbiome that is not yet captured by the databases and there is a risk that taxa of interest in other species (specifically human) are focused on due to their representation in the databases and canine specific taxa are under studied.

A set of 230 taxa were identified that are highly prevalent (>80%) across samples and skin sites. Unlike previous studies, a core taxa is defined at a species level. Many of the most commonly occurring species within the core skin microbiome (defined as species where more than one strain belonging to that species were detected within the core) are also commonly isolated from the canine oral cavity, including C. canimorsus, P, gulae, C. canis. B. zoohelcum. C. cynodegmi. P. cangingivalis. C. flaccumfaciens and P. canoris. The inclusion of a high number of species commonly occurring in the oral cavity of dogs on the skin microbiome was expected due to the cleaning and licking behaviours of dogs and were indicative of an oral-skin microbiome axis within dogs.

Within the canine core microbiome defined within this study, species belonging to genera previously reported to be present on the canine skin microbiome were identified, such as species belonging to Pyschrobacler. Sphingomonas and Cutibacterium. Additionally, species belonging to the genera Rathayibacter have been identified within the core canine microbiome in this study. Rathayibacter have previously been isolated from groin samples from humans. Finally, strains were detected belonging to the species P. marcusii within the canine core microbiome. This species has been shown to be associated with healthy skin in humans and enriched in the human skin microbiome after cleansing. Additionally, it is thought to have antimicrobial activity with some strains containing extrachromosomal elements and gene clusters potentially involved in the production of bacteriocins and bioactive polyketides. This supports a role of the skin microbiome in colonisation resistance against opportunistic pathogens.

Shotgun sequencing technology has allowed the core function of the canine skin microbiome to be elucidated. The core function was established by assessing overall prevalence of gene families across breeds and sites; although many of the core functions are involved in bacterial metabolism and survival, many functions alluding to the role of the skin microbiome in colonisation resistance and protection from invading pathogens have been identified. The gene cluster involved in the synthesis, transport and cleavage of a well-known bacteriocin, Pep5 was discovered. Bacteriocins are antimicrobial peptides produced by bacteria and their inhibitory activity has the potential to give the producing bacterial strain a competitive advantage over other colonisers or invading pathogens. Pep5 is a lantibiotic produced by Staphylococcus epidermidis. a common human skin microbiome commensal and has shown antimicrobial activity to methicillin resistant Staphylococcus aureus (MRSA) strains. An increased abundance of Staphylococcus species have been detected in the skin microbiome of dogs with atopic dermatitis, and Staphylococcus pseudintermedius is commonly associated. S. pseudintermedius isolated from the skin lesions of dogs have susceptibility to another known bacteriocin (Gallidermin) produced by Staphylococcus gallinarum. Therefore, the production of Pep5 by members of the canine skin microbiome provide resistance and control colonisation of S. pseudintermedius in healthy dogs. Genes were discovered encoding outer membrane receptors for ferrienterochelin and colicins. These receptors are involved in the active transport of iron which is essential for replication and growth. Ferrienterochelin contains an enterobactin which is known to be deployed by commensal and pathogenic species belonging to Enterob acteriaceae during colonisation. Although the genes involved in the production of enterobactin and colicins (another bacteriocin) were not detected within the core function of the microbiome, it is not unlikely that the presence of receptor proteins are indicative of its role in colonisation, protection from oxidative stress and production of biofilms, all of which would be useful traits of members of the skin microbiome. Additionally, the pathway for the biosynthesis of secondary metabolites was highly abundant and enriched in the core functional microbiome. This pathway is responsible for the production of secondary metabolites, such as antimicrobials, which can further indicate the role of the core functional microbiome in colonisation resistance.

In this study, genes associated with adhesion, biofilm production and virulence, were identified further suggesting the role of the canine skin microbiome in colonisation resistance and protection from invading pathogens. Genes belonging to the ica locus were identified which have been implicated in virulence and mediating intracellular adhesion in some S. epidermidis strains. Although these can be beneficial attributes, they can also be implicated in opportunistic invasion of skin commensals. Finally, genes involved in multidrug resistance were identified, such as mexB and acrB. These genes encoding efflux pumps are involved in the export of biological metabolites and antimicrobial compounds and are known to play roles in both intrinsic and elevated drug resistance in Gram negative bacteria. MexB has demonstrated specificity to beta-lactams whilst AcrB efflux systems are commonly associated with efflex of Penicillin, cl oxacillin and macrolides. Additionally, genes encoding specific penicillin binding proteins were found; mcrB. pbp2A. pbpB, pbpC. PBP2A is a peptidoglycan transpeptidase which can catalyse cell wall biosynthesis in the presence of beta-lactam antibiotics which enables bacterial growth and survival. The presence of genes involved in drug resistance can indicate another role of the core functional skin microbiome in protection against pathogens and in colonisation resistance. The presence of intrinsic resistance systems in members of the skin microbiome allow them a competitive advantage over other transient organisms and allow them to persist during antimicrobial treatment, meaning dysbiosis which can be associated with skin diseases such as atopic dermatitis, occur less frequently. Within this study, breed and skin site were shown to be involved in driving variation within the skin microbiome. Particularly, the microbial profile of all sites (except for the ear canal) were different when compared to each other when measured using Bray-Curtis dissimilarity. The differences in the microbial composition at different skin sites could be due to physiological characteristics at each site. The dorsal lumbar has higher sebum production and a greater number of sebaceous glands due to more dense hair at this site than other sites. The ear canal experiences slightly more anaerobic conditions and the presence of wax however, this was not observed when beta diversity was measured. A dog’s paws will be in more regular contact with the environment than other areas, specifically the dorsal lumbar, and can attribute to the changes in the microbial composition between the interdigital site and others. Additionally, a difference in the species richness between ear canal and interdigital sites was observed but no other differences in alpha diversity were observed between sites.

The microbial profile of Beagles and Norfolk Terriers appeared most pronounced using Bray-Curtis dissimilarity. The coat type and length of Norfolk Terriers is different to Beagles, with Norfolk Terriers having a longer coat. This can influence the differences in the skin microbiome. Additionally, small breed dogs (such as Norfolk Terriers) are known to have periodontitis more commonly, where increased levels of P. gingivitis can be detected in the oral cavity. This could influence the composition of transferred microbes to the skin through licking.

In this study, a core group of taxa and genes are present within the skin microbiome of healthy dogs. Shotgun sequencing coupled with a well annotated species-specific database are used to understand the function of the skin microbiome and to increase granularity at species and strain taxonomic levels. Establishing a clear picture of health is vital to further understanding of the canine skin microbiome in disease, such as atopic dermatitis, where species and strain specific bacteria are likely to be playing an important role. The skin microbiome plays a role in colonisation resistance and protection from invading pathogens.

EXAMPLE 2: Disruption, recovery, and stability of the canine skin microbiome after chlorhexidine and mild detergent intervention

Medicated shampoos are often recommended for the management of clinical signs of atopic dermatitis in dogs and for the maintenance of skin health, however there are very few studies investigating the long-term impacts and microbial shifts associated with washing interventions. The aim of this study was to provide a comprehensive assessment of the impact of washing, using both medicated and everyday-use shampoos, on the skin microbial composition in a large cohort of dogs. A dramatic but reproducible drop was observed in the microbial load of the skin following two wash interventions, with both shampoo types, to below the limits of detection of shotgun sequencing methods. Subsequently a recovery in microbial load was observed within a 7-to-28-day period following the washing interventions. Additionally, a recovery in microbial composition was observed within the natural variation of the control group by 7 days within the medicated group, whilst a slower recovery of 28 days was observed after washing using the everyday-use shampoo. Notable effects of either shampoo type on the diversity of skin microbial communities were not observed. Recovery of the skin microbiome primarily consisted of a regrowth of the extant core microbiome rather than the introduction of new taxa. These insights provide further evidence of the existence of a taxonomic core microbiome in healthy dogs and its role in microbiome resilience.

The skin is a physical interface between the body and the outside world and acts as the first line defence system for the host. The microbes residing on the skin, known as the skin microbiome, play essential roles in educating the immune system, colonisation resistance to invasive pathogens and lipid metabolism. The composition of the skin microbial community is affected by host specific factors, such as age and gender, and environmental factors, such as hygiene routines and antibiotic usage. In recent years the skin microbiome of humans has been extensively researched due, in part, to interest from the cosmetics and beauty industry.

This work has generated speculation regarding washing routines and whether they are detrimental to epidermal antibacterial defence systems, through disruption of the skin microbiome. Many cosmetic products contain chemicals that strip the skin of its natural oils, changing the micro-environment of the skin site, which in turn can influence the bacterial composition.

The skin microbiome of dogs has been described in several previous studies, with the majority focusing on a comparison between health and disease, or drivers of variation such as individual animal, skin site, season, and breed. Most commonly, Proteobacteria, Firmicutes, Actinobacteria and Bacteroidota reported as the dominant phyla across canine skin sites. In dogs with atopic dermatitis, reduction in Shannon diversity is observed alongside increased proportions of Staphylococcus pseudintermedius compared to healthy dogs and a correlation between S. pseudintermedius relative abundance and clinical sign severity is noted. A core skin microbiome associated with healthy dogs was recently reported. The core represents 230 taxa and >1,200 gene families that are enriched within the core microbiome in comparison to the accessory microbiome in healthy adult dogs and indicate a role for the core microbiome in colonisation resistance. Skin site and breed were the main drivers of variation within the canine skin microbiome.

Medicated shampoos are often recommended for the long-term maintenance of skin health and are frequently prescribed to treat skin irritation in dogs with atopic dermatitis. To date, one study has explored the effect of topical antimicrobial therapy, using medicated shampoo containing the antimicrobial ingredients chlorhexidine and miconazole, on the canine skin microbiota. The study showed a clear separation for samples from four different skin sites when samples from healthy and atopic dermatitis dogs were combined, and the authors attributed this separation a result of treatment. More recently a second study has reported the skin microbiota composition in a washing paradigm designed to replicate similar regimes to those undertaken in the decontamination of working dogs, where a rigorous everyday wash regime is deployed. Daily bathing with a 1.6% detergent for 14 days significantly increased within sample diversity and decreased relative abundance of key phyla, Actinobacteria, Firmicutes and Proteobacteria.

It is essential to establish the duration required for skin microbiome re-establishment after washing. In the current study, the impact on the microbiome of medicated versus nonmedicated shampoo was evaluated. A shotgun sequencing approach was used to characterise the taxonomic profiles of the skin microbiome with increased resolution at genus and species level before and after washing interventions. Unique insights are provided into how the microbiome is removed and recovers following different washing regimes.

Methods

Cohort Description. The study cohort comprised 90 healthy adult dogs housed at the Waltham Petcare Science Institute (Leicestershire, UK) and sampling was conducted between January and March 2022. Upon enrolment to the study, dogs were allocated into one of three groups (control, medicated shampoo, or non-medicated shampoo) balanced for breed, age, neuter status, and sex (Figure 16). In the control group, four breeds were represented: Labrador Retriever (n=10), Beagle (n=6), Petit Basset Griffon Vendeen (n=4) and Norfolk Terrier (n=l l). The control group contained 15 male and 16 female dogs of which 25 were neutered and 6 entire. The control group had a mean age of 4.4 years (range 1.4-8.4 years) and mean bodyweight of 15.3 kg (range 3.6-33.5 kg). The medicated shampoo group contained the same four breeds (Labrador Retriever n=12, Beagle n=5, Petit Basset Griffon Vendeen n=3, Norfolk Terrier n=8), and contained 15 males and 13 females of which 18 were neutered and 10 entire. The medicated shampoo group had a mean age of 4.2 years (range 1.1-10.7 years) and a mean bodyweight of 16.9 kg (range 3.3-30.4 kg). The nonmedicated shampoo group was represented by Labrador Retriever (n=12), Beagle (n=5), Petit Basset Griffon Vendeen (n=3) and Norfolk Terrier (n=9). The non-medicated shampoo group comprised 15 males and 14 females, of which 5 were entire and the remainder neutered. The mean age of the non-medicated shampoo group was 4.4 years (range 1.1-10.6), and the mean bodyweight was 17.0 kg (range 4.8-35.3 kg). Dogs were housed in pairs in a pen that provided continuous inside and outside access; in addition, dogs from adjacent pens were socialised in group paddocks during the day. Dogs also have off-lead exercise and socialisation with other dogs, except on sampling days.

The dogs had no history of skin issues or seasonal skin issues and had not been treated with any antibiotics, immunosuppressants or anti-inflammatories in the six weeks prior to the start of the study. The use of topical treatments was excluded in the two weeks prior to the start of the study. Washing with shampoo in the 72 hours before baseline sample was excluded. Where required, dogs were bathed in water only for the duration of the study for the purposes of hygiene. Dogs taking supplements for skin or joint health were excluded from the study. Medication and washing exclusions remained in place for the duration of the study and resulted in the removal of two dogs for the study cohort in the first week of the study. All dogs were fed commercially available complete dry diets throughout the study, appropriate for their life stage (puppy, adult) and/or weight, and diet remained consistent throughout the sampling phase.

Study Design. Dogs enrolled onto the study were stratified into three groups according to breed, age, and sex, and sampled according to the cadence outlined in Figure 10. Dogs in the control group (n=31) were sampled throughout the duration of the study but underwent no wash interventions. Dogs in the medicated shampoo group (n=28) were washed on two separate occasions using medicated shampoo containing chlorhexidine and miconazole (Dechra Ltd., Yorkshire, UK), at day 0 and day 29. Dogs in the non-medicated shampoo group (n=29) were washed on two separate occasions using an “everyday use” nonmedicated shampoo (Groomers Banana and Mango shampoo, Groomers Ltd., Berkshire, UK), at day 0 and day 29. An exact quantity (15 ml for large dogs, and 7.5 ml for medium and small dogs) of shampoo was applied to the abdomen of dogs in the medicated and nonmedicated shampoo groups. In accordance with the recommended use of medicated shampoo, the medicated shampoo was retained on the skin for a period of 10 minutes prior to being washed off. The everyday use non-medicated shampoo was immediately washed off after application. Once the shampoo was thoroughly removed, the area of the skin was towel dried and air-dried for up to 10 minutes prior to sampling.

Skin Swab Sample Collection. Skin microbiome samples were collected from the abdomen region of all dogs at a total of 7 timepoints throughout the study (Figure 10). Samples were collected on the day prior (Day -1) to the first wash intervention, immediately after both wash interventions (Day 0 and Day 29), and at 7 days and 28 days post the wash interventions (Day 7, Day 28, Day 36, and Day 57). In total, 616 samples were collected. The hair at each site was parted by the sample collector to ensure access to the skin surface, and areas were not shaved prior to sample collection. Two sterile flocked swabs with 80 mm breakpoint (Norgen Biotek Corp.) were used to collect skin microbiome samples. Swabs were soaked in sterile wetting solution (Tris buffer (pH 8.0), 2 mM EDTA and Triton X-100, Norgen Biotek Corp.) then applied, with rotation, to the desired skin area for a period of at least one minute. Duplicate swabs for each sample site were stored in stabilisation solution (Swab Collection and DNA Preservation System, Norgen Biotek Corp.).

DNA Extraction. Skin swab samples were transferred to Diversigen (New Brighton, MN, USA) where DNA extraction, qPCR and sequencing was conducted, four samples were removed from the analysis at this point due to sample integrity being compromised during shipping. Genomic DNA from skin swabs were extracted using the Saliva DNA Isolation Kit (Norgen Biotech Corp.) according to the manufacturers Supplementary Protocol for the Isolation of DNA from Norgen’s Swab Collection and DNA Preservation System using Norgen’s Saliva DNA Isolation Kit. Genomic DNA was quantified using the Quant-IT™ PicoGreen™ dsDNA Assay kit and reagents (Invitrogen).

Quantitative PCR. 16S rRNA gene copy number in the extracted DNA was quantified via qPCR targeting variable region 4 of the 16S rRNA gene with the following primer sequences: 515F: 5’-GTGCCAGCMGCCGCGGTAA-3’ (SEQ ID NO.: 1) and 806R: 5’- GTGCCAGCMGCCGCGGTAA-3’ (SEQ ID NO.: 2). Samples were input neat and at 10- fold serial dilutions up to 1 : 1000.

Library Preparation, Shotgun Sequencing and Bioinformatics. Libraries were prepared with a procedure adapted from the DNA Prep kit (Illumina) and sequenced at Diversigen (New Brighton, MN, USA) on an Illumina NovaSeq 6000 using paired-end 2x150 bp reads (Illumina) with a target mean read depth of 5 million reads per sample. DNA sequences were filtered for low quality (Q-Score < 30) and length (< 50 bp), and adapter sequences were trimmed using Cutadapt. Host (canine) sequences were removed using Bowtie before DNA sequences were aligned to Diversigen’ s curated database containing all representative genomes in RefSeq for bacteria with additional manually curated strains (DivDB-Canine). Every input sequence (read) was compared to every reference sequence in DivDB-Canine using fully-gapped alignment with BURST and an identity threshold of 97%. Ties were broken by minimizing the overall number of unique Operational Taxonomic Units (OTUs). For taxonomy assignment, each sequencing read was assigned to the lowest common ancestor that was consistent across at least 80% of all reference sequences tied for best hit. Kyoto Encyclopedia of Genes and Genomes Orthology groups (KEGG KOs) were observed directly via alignment to a gene database derived from Div-DB-Canine. All sequencing reads were aligned to all reference gene sequences at an identity threshold of 97% using fully-gapped alignment with BURST. Ambiguously mapped reads were excluded from the resulting functional feature table. A mean read depth of 6.8 million reads per sample (range 78-18.7 million reads) was obtained, of which a mean of 7.6% reads mapped to taxonomy (range 0.6-40.5%); 3.0% mapped to species level (range 0.1-34.6%) and 3.3% mapped to KEGG Orthologs (range 0.2-21.6%).

Statistical Analysis. Downstream analyses were performed using R version 4.2.242 and the raw count tables (taxonomic and gene families). Prior to taxonomic analyses, data quality filtering was conducted. Firstly, Methylobacterium fujisawaense were identified within the negative control sample (Norgen preservation tube only), accounting for 40,000 reads. This bacterium is a known water bacterium and “kitome” contaminant and was therefore removed from all samples prior to conducting any further analysis. Additionally, negative control and mock community (MSA-2002) samples were used to assess an appropriate relative abundance threshold for taxonomic noise removal. Any count that was lower than 0.03% of total counts was inputted with 0 and any taxa that had 0 across all samples was subsequently removed. Additionally, taxa that were present, at any abundance, in only one sample were removed. Finally, a rarefaction curve was created to determine an appropriate threshold for normalisation of read depth; for this analysis samples were rarefied to a read depth of 100,000 reads and any sample with a read depth not reaching this threshold were removed. Data quality filtering discarded a total of 10,321 low abundance taxa and 201 samples, across groups and time points. This included over half of the samples from the control group at time point Day 57 (64.5%) and therefore all samples from this data point were removed due to potential data quality issues most likely caused by a collection error or sample processing error. Additionally, low read depth was observed in samples collected immediately following washing interventions in the medicated and non-medicated shampoo groups, rendering them below the limit of detection for robust sequencing data to be generated. Considering this, these time points were removed from further analyses. The data filtering process was repeated after samples relating to time points Day 0, Day 29 and Day 57 had been removed, using the same parameters, and showed removal of 81 samples. A total of 10,649 low abundance taxa were removed, creating a final catalogue of 1,588 taxa across all samples.

Next, beta diversity analyses were assessed to compare the diversity of communities between time points within group using Bray-Curtis dissimilarity on relative abundance and visualised using non-metric multidimensional scaling (NMDS) to determine the time for recovery of the microbiome post washing interventions. A non-inferiority test was conducted on the dissimilarity scores to assess equivalence of the microbial communities after washing (Day 7, 28, 36) compared with baseline (Day -1 or Day 28). Beta diversity significance of clustering was identified by permutational analysis of variance (PERMANOVA) and a dispersion test was conducted on the pairwise comparison (time point within group) using the vegdist and betadisper functions from the vegan package. Subsequently, an analysis of variance (ANOVA) of the distances to group centroids was performed to assess for significance. All outputs were visualised using the ggplot2 package. Bray-Curtis dissimilarity of microbial communities between groups (all time points) and by breed, sex, and neuter status were also visualised, and PERMANOVA and dispersion were tested as previously described.

Alpha diversity analyses to compare the diversity of taxonomic communities within samples over time were assessed using two metrics; Shannon diversity and species richness (observed species). Shannon diversity and species richness of each sample within group were, separately, fit to a linear mixed effects model with time point as the fixed effect and individual dog as the random effect. Contrasts were made between post wash time points (Day 7, 28 and 36) compared to baseline (Day -1 or Day 28), and the family-wise 95% confidence intervals and single-step adjusted p-values obtained (adjusted within study group). No adjustment was made for multiple comparisons across measures and across study groups. A subset of taxa (genera and species, Figure 17) identified within the healthy core canine skin microbiome were selected for univariant analysis between time points. Each taxon was modelled individually using a logistic regression, with count +2 and total +4 as the response variable, study group, time point and their interaction as the fixed effects, individual animal and observation level random effect (OLRE) as the random effects. The estimated means and 95% confidence intervals were extracted from the model. Comparisons were made between day 7 and day -1, day 28 and day -1, and day 36 and day 28 within each group. The estimated odds ratio, 95% confidence interval and single-step adjusted p-values were obtained. Any taxa that had a significant change over time in any group were visualised using a heat map.

Results

Reduction of bacterial load following wash interventions. At two distinct time points (Day 0 and Day 29), wash interventions were conducted for dogs in the medicated and shampoo groups. Dogs in the medicated group were washed with a medicated shampoo containing chi orhexi dine, whilst dogs in the non-medicated shampoo group were washed with a generic everyday use non-medicated shampoo (Groomers Mango and Banana). Skin swab samples (one per dog) were immediately collected after medicated or non-medicated shampoo had been removed and the area of the skin thoroughly dried. qPCR was conducted of the V4 region of the 16S gene to establish bacterial load before and after the wash interventions. This showed a statistically significant reduction in the number of copies of the 16S gene in the samples post wash (Day 0 and Day 29) in both the medicated and nonmedicated shampoo groups when compared to the baseline (Day -1 and Day 28 respectively, p<0.001, Figure 11). There was no significant difference in the 16S gene copy number observed in the control group for the same time points (p>0.05). Due to this, the quality of the sequencing data was not sufficient to obtain quality analysis from these samples and therefore were removed from further microbiome analyses.

Short-term stability and recovery of the canine skin microbiome following washing interventions. To determine whether the skin microbiome recovered to a similar microbial profile as baseline following disruption from the washing intervention, Bray-Curtis dissimilarity was calculated for all samples and tested for equivalence in the microbial profiles using a one-sided test. To establish a suitable threshold for equivalence testing, the variation in the microbial profile over time was determined in the control group to understand the stability of the canine skin microbiome over 36 days. There is considerable variation in the canine skin microbiome overtime (Figure 12A), mean Bray-Curtis dissimilarity 0.33 and 0.38 over 7 days and 28 days respectively, and 0.40 over 7 days following the second wash (95% CI: (0.29, 0.37) for Day -l:Day 7; (0.34, 0.42) for Day -l:Day 28; (0.36, 0.45) for Day 28:Day 36).

Based on the high normal variation observed in Bray-Curtis dissimilarity within the control group, a threshold for dissimilarity was set using the upper confidence interval for each time comparison to assess the recovery of the microbiome following the washing interventions (Figure 12A, Table 6). The skin microbiome showed a statistically equivalent microbiome 7 days following the first wash intervention when dogs were washed with medicated shampoo (p=0.05). However, the microbiome was slower to recover following the everyday use non-medicated shampoo wash and the microbial profile was not deemed equivalent at 7 days compared to baseline (p=0.096). In both groups, the microbial profile remained within the dissimilarity threshold 28 days after washing (medicated p=0.042, shampoo p=0.017). Following the second wash intervention on Day 29, recovery of the microbiome was observed within 7 days for both groups (medicated shampoo p=0.006; nonmedicated shampoo p=0.007). Additionally, the Bray-Curtis values were visualized using NMDS to observe microbial profile differences between groups and timepoints. Different microbial abundance profiles were not observed between timepoints for the control and nonmedicated shampoo group (PERMANOVA p>0.05; beta dispersion p>0.05 for both groups, Figure 12B) however, significantly different microbial profiles were observed between timepoints within the medicated shampoo group (PERMANOVA p=0.013, R2=0.065; beta dispersion p>0.05). The significance of PERMANOVA and non-significance in dispersion shows that the centroids are different between the timepoints, this is likely driven by Day 7 which shows some small separation (Figure 12B). Furthermore, potential confounding factors which could be contributing to variation within the canine skin microbiome were assessed. Significantly different microbial profiles were observed between groups, sex, neuter status, and breed (Figures 15A-15D). This was anticipated, and groups were balanced prior to the study to account for these confounding variables.

Table 6. Bray Curtis Dissimilarity and Equivalence Test

Microbial diversity within the samples were assessed using total species richness and Shannon diversity. The species richness of the skin microbiome was significantly different between Day -1 and Day 7 in the control group (p=0.017, Figure 13A), however no other significant comparisons were observed across time within group for any other group (p>0.05 for all comparisons). For Shannon diversity, a significant difference was observed in the nonmedicated shampoo group between Day 28 and Day 36 (p=0.035, Figure 13B), but there were no significant differences across time within group for any other group. Overall, the diversity of the microbiome did not appear to be impacted by washing, with the most notable difference being after the second wash intervention using the everyday use non-medicated shampoo.

Core Microbiome Disruption, Recovery and Stability Over Time. In previous work, a core microbiome was identified containing 230 taxa that are fundamental to the healthy canine skin microbiome. In this study, the core microbiome is subset to taxa above strain level resolution, resulting in a core list of 197 taxa at all taxonomic levels. 189 (96%) of the core taxa were identified across all samples, with only eight taxa not observed. Subsequently, 172 taxa were selected representing the genus and species of the core microbiome to study further (Figure 17). To establish whether the core microbiome remains stable over time and post-washing interventions, univariant analysis was conducted of taxa from the core microbiome (Figure 17) within the control group over time. The taxa with the highest fold change are shown in Figure 14. Taxa not represented (n=98, 57%) showed no significant fold change between any comparison. To establish the effect of conducting washing interventions on the canine core microbiome, the same genera and species were selected (Figure 17) for univariant analysis to identify significant changes in relative abundance at Day 7, 28 and 36 compared to pre-washing (Day 0 and Day 28; Figure 14). The taxa with the highest fold change were plotted. Overall, the core microbiome remained stable for 36 days, whilst changes in relative abundance of the core taxa were observed, absence of taxa over time were not observed. At Day -1, the relative abundance of many taxa was similar across the three groups supporting the notion that the core microbiome is present in healthy dogs, such as species of Sphingomonas, Pseudomonas and Rhodococcus. Interestingly, Phocaeicola sp900546645. Muribaculum sp002492595. Nocardoides glacieisoli. Deinococcus marmoris and Arthrobacter D sp001422665 were observed at high abundance across all time points and groups, showing very little effect of the washing interventions on these organisms except D. marmoris showing some reduction in relative abundance 7 days after the everyday use non-medicated shampoo wash intervention. Genera and species of the core microbiome showed slight reduction in relative abundance at day 7 across all groups suggesting natural variation in the abundance of core taxa rather than specific effects of the wash interventions. At Day 36, A. spOO 1422665 and D. marmoris showed increased abundance at Day 36 compared to the other time points across all groups whilst Phocaeicola sp900552855 and A7. sp002492595 appeared to have reduced relative abundance at Day 36 compared to the other time points across all groups.

Discussion

The effect of washing interventions on the canine skin microbiome and its’ subsequent recovery over time was investigated in comparison with the natural variation and stability of the skin microbiome. Across all washing interventions, a reproducible significant drop in bacterial load was demonstrated immediately after washing followed by a recovery of the microbiome to its original microbial load after seven days and to its original microbial composition within a 7 - 28 day window depending on the wash intervention. Remarkably, this was consistent across multiple wash interventions.

A significant reduction in bacterial load of nearly two orders of magnitude was observed following both washing interventions. This drop-off almost entirely recovered by day 7 and was completely back to baseline by day 28. This was consistent across two different types of wash interventions and suggests a high level of resilience within the skin microbiome to repopulate following depletion at least in healthy dogs. The microbiome composition at days 7 - 28 were consistent with the pre-washing timepoints, though changes in the rate of the microbiome recovery differed depending on treatment. Thus, repopulation of the skin microbiome occurred from the growth of the microbiota remaining on the skin as opposed to collection of additional microbes from an external environment. Indeed, analysis of the core microbiome supported this notion, with minimal changes in the composition of the core microbiome across 36 days. This is highly indicative that microbiome recovery following washing interventions was fuelled by a regrowth of the extant core microbiome.

After washing with a medicated shampoo, the skin microbiome recovered to the diversity of baseline after 7 days. Interestingly, recovery of the skin microbiome was slower (greater than 7 days) when an everyday use non-medicated shampoo (Groomers Mango and Banana) had been used. This was not anticipated due to the antimicrobial compounds present in the medicated shampoo which contains the active ingredients Chlorhexidine digluconate and Miconazole nitrate with specificity towards Malassezia pachydermatis and Staphylococcus intermedins. As the study was conducted in a healthy cohort of dogs, these organisms were not detected. Chlorhexidine digluconate is an antimicrobial agent known to inhibit the growth of S. intermedins through interference with energy transport mechanisms by directly affecting ATP-ase. It can act as a bactericidal and bacteriostatic, to both Grampositive and Gram-negative bacteria, depending on the concentration used. The Groomers Banana and Mango shampoo is a generic non-medicated shampoo containing some ingredients, such as linalool, that are known to have antimicrobial properties. The results show the everyday use non-medicated shampoo is potentially harsher and less specific than the medicated shampoo, effecting the growth of species which require longer to re-establish within the community.

This study supports previous work showing a core microbiome in heathy dogs detecting 96% of the core taxa within this dataset. Natural variation was observed in the skin microbial profile (Bray-Curtis) over 36 days however, species and genera of the core remained stable, with 57% of core species and genera showing no changes in relative abundance over time in any group. Although changes were observed in relative abundances of taxa specifically at Day 7 across all groups, absence of taxa over time was not observed. This supports the notion that the same skin microbial community is re-established after washing interventions, rather than the emergence of a new population, suggesting resilience of the core microbiome and giving further evidence of the role of the core microbiome in colonisation resistance.

In conclusion, washing with two different types of shampoo leads to a marked and reproducible, drop in microbial load, following by recovery within seven days in healthy dogs. Reestablishment of skin microbiome composition varied depending on shampoo type, with recovery in the microbiome primarily consisting of a regrowth of the extant core microbiome rather the introduction of new taxa.

EXAMPLE 3: Characterisation of the gut and skin microbiome in dogs with atopic dermatitis in comparison to healthy controls

The role of the canine skin microbiome in the aetiology of canine atopic dermatitis (CAD) has yet to be fully elucidated. This study characterises the gut and skin microbiome of dogs with clinical signs of CAD in comparison to a healthy control group across a breed specific cohort. CAD was associated with significant, concurrent changes to the microbial profile of both the skin and gut microbiome compared to healthy control dogs. Changes to the gut microbiome in dogs with clinical signs of CAD were driven by enrichment of potentially pathogenic taxa such as Escherichia and closely resemble that of other inflammatory conditions (such as chronic enteropathy) both taxonomically and functionally.

The skin of animals and humans provides the most immediate interface between the exogenous environment and an individual. It acts as a physical, immunological and microbial barrier and protects the body from dehydration. Skin related conditions occur when aspects of this complex and dynamic system become perturbed and are one of the most common diagnoses in dogs. An estimated prevalence of 12.58% diagnosed skin disorder cases were reported in the UK in 2016 making it the second most prevalent disorder reported that year. Precise diagnosis of atopic dermatitis accounted for an estimated 1.15% of cases.

Canine atopic dermatitis (CAD) is defined as a “genetically predisposed inflammatory and pruritic allergic skin disease often associated with a production of immunoglobulin E (IgE) against environmental allergens.” (Halliwell, 2006). Clinical signs usually develop when dogs are aged between 6 months and 3 years and present with primary skin lesions and pruritus (an unpleasant sensation of the skin that provokes the urge to scratch). Scratching can lead to self-induced alopecia and secondary infections with crusts, papules and pustules. Secondary infections are often associated with increased relative abundance of Staphylococcus pseudintermedius (Santoro & Rodrigues Hoffmann, 2016). Whereas in humans, these secondary infections are characterised by an increased relative abundance of Staphylococcus aureus (Geoghegan et al., 2018a). Clinical signs commonly present on certain body sites; the abdomen, axilla, inner pinna and paws and it is estimated around half of dogs with CAD also present with otitis externa (Favrot et al., 2010). CAD is a multifactorial disease with both genetic and environmental factors contributing to its pathogenesis and is associated with production of immunoglobulin E (IgE) in response to environmental allergens (Hensel et al., 2024). Some breeds of dog are deemed pre-disposed to CAD, including West Highland White Terriers, Labrador Retrievers, Golden Retrievers, German Shepherds and French Bulldogs, suggesting the involvement of genetics.

Many factors influence the microbial composition of the skin, such as host genetic variation, lifestyle, hygiene, and the environment (Grice & Segre, 2011; Santoro et al., 2024). To date, studies of the canine skin microbiota have focused on 16S rRNA high-throughput sequencing to describe the taxonomic profile of healthy dogs and those with skin conditions, such as atopic dermatitis (Apostolopoulos et al., 2021; Bradley et al., 2016a; Chermprapai et al., 2019; Cusco et al., 2017; Leverett et al., 2022; Meason-Smith et al., 2015; Ngo et al., 2018; Tang et al., 2020; Torres et al., 2017). Across these studies, the most dominant phyla observed across different skin sites are the Proteobacteria, with Firmicutes, Actinobacteria and Bacteroidota observed to a lesser extent. Studies have described the skin microbiota of multiple different skin sites including interdigital region of the paw, axilla, concave pinna, ear canal, dorsal lumber, conjunctiva, groin, perianal skin, chin, nasal and abdomen. A core microbiome associated with healthy canine skin was previously determined (Whittle et al., 2024) . The skin microbiome of 72 healthy adult dogs across four distinct skin sites were examined using shotgun metagenomics and identified a catalogue of 230 taxa and >1,200 genes that were deemed core. The core microbiome plays a role in colonisation resistance from invading pathogens, with genes associated with bacteriocin production detected. Further microbiome studies specifically comparing the skin microbial communities of healthy dogs with those diagnosed with atopic dermatitis report a reduction in Shannon diversity and increased proportions of Staphylococcus pseudintermedius and Corynebacterium in dogs with atopic dermatitis in comparison to healthy dogs (Bradley et al., 2016a). In dogs with atopic dermatitis a reduction in the relative abundance of S. pseudintermedius has been observed in correlation with decreased severity of atopic dermatitis clinical signs, and a restoration in bacterial diversity. More recently, research efforts have focused on breeds known to have pre-disposition to CAD, such as West Highland White Terriers, German Shepherds and Shiba Inu (Rodriguez-Campos et al., 2020; Thomsen et al., 2023).

More recently, there is growing evidence to suggest that there are links between the skin, gut and oral microbial communities and cross-communication can influence immune and endocrine systems (De Pessemier et al., 2021). The mechanisms of these links are currently not understood. There are several skin conditions that have been linked to gastrointestinal (GI) inflammation, such as acne, psoriasis and rosacea in humans (Mahmud et al., 2022; Sanchez-Pellicer et al., 2024). In addition, skin lesions have been observed in association with GI conditions such as celiac disease and inflammatory bowel disease (IBD) (Abenavoli et al., 2019). Communication between the gut and skin is exemplified in the adsorption of nutrients which have a direct effect on the skin. For example, yellowing of the skin has been correlated with the intake of carotenoids (Tuong et al., 2015). Evidence also exists to suggest that modulation of the gut microbiome can release secondary metabolites that have a direct or distant effect on the skin. Gut derived lipopolysaccharide has been shown to play a role in acne inflammation (Bowe & Logan, 2011). The role of the gut microbiome in skin diseases has been investigated, with patients suffering psoriasis (a skin disease) showing an increased prevalence of Cutibacterium spp. and a decreased prevalence of Bacteroides spp were observed in the gut compared to healthy controls (Olejniczak-Staruch et al., 2021). The link between the gut microbiome and canine atopic dermatitis has been investigated within Shiba Inu dogs, with concomitant dysbiosis of the gut and skin microbiome observed, associated with increased abundance of Escherichia/ Shigella spp and Staphylococcus species in these environments respectively (Thomsen et al., 2023). As such, it is not only important to understand how the skin microbiome can contribute to, or potentially play a causative role in CAD progression but also how the distant gut microbiome might exacerbate this in a multifaceted manner. This knowledge platform will aid in the development of effective treatments or interventions for CAD, alongside multiple similar skin conditions in both humans and animals.

In this study, the gut and skin microbiome of dogs with atopic dermatitis were characterised and compared to a healthy control group across a breed specific cohort. A detailed evaluation of microbial taxa acting as biomarkers of canine atopic dermatitis within both the gut and skin microbiota is provided, with correlation in the abundance of skin microbiome biomarkers across multiple distinct body sites.

Methods

Cohort Description. A total of 86 client owned dogs were enrolled onto a single timepoint study into either the healthy (n=42) or well characterised canine atopic dermatitis (CAD) group (n=44) (Table 7). All dogs were prospectively recruited to the study upon presentation at hospitals across the USA; and must have been over two years of age at the time of enrolment. Specifically, purebred dogs belonging to the following three breeds were enrolled; Labrador Retriever (LR) (CAD, n=16; health, n=16), Golden Retriever (GR) (CAD, n=16; health, n=18), and West Highland White Terrier (WHWT) (CAD, n=12; health, n=8). There were no restrictions on bodyweight, sex or neuter status, and all dogs had a body condition score (BCS) of between 3 and 7 out of 9 with bodyweight ranging from 6.3kg- 66.5kg (GR, 18.8kg-66.5kg; LR, 22.8kg-49.0kg, WHWT, 6.3kg-12.4kg). Golden Retrievers consisted of 16 males (4, entire; 12, neutered) and 18 females (2, entire; 16, neutered), Labrador Retrievers consisted of 15 males (1, entire; 14, neutered) and 17 females (2, entire; 15, neutered) and West Highland White Terriers consisted of 10 males (1, entire; 9, neutered) and 10 females (all neutered). All study participants provided informed consent prior to the start of the study and were advised of their right to withdraw at any time without explanation, penalty, or influence on their pet’ s veterinary care. All information on dog signalment can be found in Figure 22.

The dogs in the CAD group were considered if they had at least three of the major criteria or, at least two of the major criteria and one of the minor criteria described in Table 7 in the past 12 months and were recruited by board-certified dermatologists. Additionally, dogs must have undergone consistent routine ectoparasite control for at least 3 months prior to the start of the study to rule out flea allergy dermatitis, or clear evidence that dermatitis clinical signs were not associated with flea allergy provided by the investigating veterinarian and a single dose of oral Bravecto provided. Furthermore, dogs in the CAD group must not have been diagnosed with or have a suspected food allergy dermatitis. This was defined as when food allergy dermatitis has been ruled out historically through a diet elimination trial, or when flare up had not been associated with diet challenge or change in diet, or in circumstances where a history of clinical signs were observed during certain seasons whilst the same diet was maintained. Dogs were not required to have an active flare in clinical signs upon enrolment to the study. Dogs receiving Apoquel or topical steroid treatments in the 72 hours prior to enrolment were excluded.

Table 7. Well characterised atopic dermatitis (CAD) group inclusion criteria.

Dogs recruited into the healthy group were deemed clinically healthy at the time of enrolment examination by a veterinarian, without any uncontrolled medical conditions and without any history of atopic dermatitis or chronic skin irritation. Additionally, dogs in the healthy group must not have experienced chronic or recurring otitis or conjunctivitis that did not resolve with treatment in the past 6 months. Dogs receiving antibiotics, probiotics, immunosuppressant or anti-inflammatory drugs, or dietary supplements intended for skin or joint health in the 6 weeks prior to enrolment were excluded from both groups. Additionally, dogs washed with any shampoo (including medicated and generic), or presenting with poor faeces quality in the preceding 24 hours to enrolment were excluded. All dogs received a nutritionally complete diet as their main meal. The protocol was approved by the Waltham Animal Welfare Ethical Review Board (AWERB). At enrolment, all dogs within the CAD group were assessed for clinical sign severity using the Canine Atopic Dermatitis Evaluation and Severity Index Version 4 (CADESI-04) (Olivry et al., 2014) by a board-certified dermatologist. Faeces quality was reported by pet owners for each participant according to the Faeces Scoring System. All collected faeces scores were determined to be within the acceptable scoring range (<1.5 or >3.75) at the time of faeces sample collection. Clinical CADESI scores and faeces scores are summarised in Figure 22.

Skin swab collection. A total of six skin swab samples were collected from each participant at the time of enrolment (n=516) for microbiome analysis according to the method outlined previously (Whittle et al., 2024). Triplicate samples were collected from the abdomen and dorsal lumbar regions of each participant and duplicate swabs for each sample site were stored in stabilisation solution (Swab Collection and DNA Preservation System, Norgen Biotek Corp.). Stabilised samples were transported to a processing laboratory (Antech Diagnostics, USA) and stored at room temperature until processing. Subsequently, skin swab samples were transferred to Diversigen (New Brighton, MN, USA) where DNA was isolated from samples using the Saliva DNA isolation kit (Norgen Biotek Corp.) following the manufacturer’s guidelines. Samples were processed in two batches.

Faeces sample collection. At the time of enrolment, a single, naturally voided, faeces sample was collected from each dog (n=86). A sample of the faeces was collected into a Stool Nucleic Acid Collection and Preservation Tube (Norgen Biotek Corp.) according to the manufacturer’s instructions. Stabilised samples were transported to a processing laboratory (Antech Diagnostics, USA), where they were stored at room temperature prior to processing. Subsequently, faeces samples were transferred to Diversigen (New Brighton, MN, USA) where DNA was extracted using the PowerSoil Pro DNA Isolation kit (Qiagen, US) automated for high throughput on the QiaCube HT (Qiagen, US), using mechanical lysis via bead beating (Powerbead Pro plates, Qiagen). Samples were processed in two batches.

Library preparation, 16S amplicon sequencing and amplicon sequence variant (ASV) picking of skin swabs. Samples were prepared with a protocol previously described (Gohl et al., 2016), using KAPA HiFi Polymerase (KAPA Biosystems) to amplify variable regions 1- 3 (27F [AGAGTTTGATCMTGGCTCAG] (SEQ ID NO.: 3) and 534R [ATTACCGCGGCTGCTGG] (SEQ ID NO.: 4)) of the 16S rRNA gene. Libraries were sequenced at Diversigen (New Brighton, MN, USA) on an Illumina MiSeq using paired-end 2 x 300 bp reads with the MiSeq Reagent Kit V3 (Illumina, 600 cycle kit) to a target mean depth of 50,000 reads. Cutadapt v2.10 (Martin, 2011) was used to remove adapters and primers from sequencing reads. Paired reads were used to generate amplicon sequence variants (ASVs) via dada2 vl .16.0. FASTQ reads were trimmed to 280 bp or truncated at the first occurrence of a quality score of 2 or less. Reads that were less than 280 bp were discarded. Samples were filtered to remove reads containing Ns or >2 sequencing errors per read. The dada2 learn error rate model was used to estimate the error profile before using the core dada2 algorithm to infer the sample composition. Sequence chimeras were removed, and ASVs less than 50 bp in length were discarded. ASV taxonomy was assigned up to the genus level using the SILVA v.138 database (Quast et al., 2012) with the protocol derived from Wang et al. 2007 and a minimum bootstrapping support of 50%. Via dada2, specieslevel taxonomy was assigned to ASVs only with 100% Identity and unambiguous matching of the reference.

Due to reduced sample integrity following storage, 298 skin swabs could not be processed for sequencing and are not described in further detail, with a total of 218 skin swabs utilised for analysis. Twenty-four dogs provided skin swabs for both dorsal lumbar (DL) and abdomen sites (n=9, CAD; n=15, health) with a further 11 dogs providing only a DL swab (n=7, CAD; n=4, health) and 2 dogs providing only an abdominal swab (n=2 health). An additional 82 swabs from a further 19 (n=7, CAD; n=12 health) dogs were included in only end stage univariate analysis due to swab sample location being undefined (i.e. either abdomen or DL). A full summary of dog signalment, CADESI score and collected sample repertoire for the study cohort is provided in Figure 22 and Figure 23.

Sequencing of the 16S rRNA VI -V3 region of the collected 218 skin swab samples yielded an average of 114,073 merged reads per sample after initial quality filtering, with the removal of a further 3 swab samples following rarefaction (n=215). To account for potential contamination of samples during processing, in silico decontamination was performed using SCRUB removing contaminating reads based on sequencing plate negative controls (Austin et al., 2023). A total of 1,381 unique ASVs were detected within the final swab sample set, with 87% of ASVs assigned to at least genus level. With unequal sample replicate number per dog, replicate samples from the same location of each individual dog were assessed for microbiome compositional similarity according to Bray-Curtis dissimilarity indices before being plotted using nMDS. Samples were determined to cluster sufficiently following visual inspection, with one representative selected for initial analysis per site per individual (representatives were selected based on replicate number, with the lowest replicate selected where possible i.e. preference replicate l>replicate 2> replicate3), with all replicates utilised in end stage univariate analysis only. Library Preparation, shotgun metagenomic sequencing and bioinformatic processing of faeces samples. Libraries were prepared with a proprietary procedure adapted from the Nextera XT kit (Illumina) and sequenced at Diversigen (New Brighton, MN, USA) on an Illumina NovaSeq 6000 using paired-end 2x150 bp reads (Illumina). DNA sequences were filtered for low quality (Q-Score < 30) and length (< 50 bp), and adapter sequences were trimmed using Cutadapt (Martin, 2011). Host (canine) sequences were removed using Bowtie2 (Langmead & Salzberg, 2012). DNA sequences were aligned to Diversigen’ s curated database (MetaGeneCanine™, Diversigen, US) containing all representative genomes in RefSeq (Langmead & Salzberg, 2012) for bacteria with additional manually curated strains. Every input sequence (read) was compared to every reference sequence using fully-gapped alignment with BURST and an identity threshold of 97%. Ties were broken by minimizing the overall number of unique Operational Taxonomic Units (OTUs). For taxonomy assignment, each sequencing read was assigned to the lowest common ancestor that was consistent across at least 80% of all reference sequences tied for best hit. Kyoto Encyclopedia of Genes and Genomes Orthology (Langmead & Salzberg, 2012) groups (KEGG KOs) were observed directly via alignment to a gene database. All sequencing reads were aligned to all reference gene sequences at an identity threshold of 97% using fully- gapped alignment with BURST. Ambiguously mapped reads were excluded from the resulting functional feature table.

Due to reduced sample integrity following storage, four faeces samples could not be processed for sequencing and a further three samples were removed due to low sequencing depth, with the remaining 79 faeces samples utilised for downstream analysis. All samples yielded a minimum of 500,000 paired end metagenomic sequencing reads, with these subsequently annotated against a curated canine database. Identified taxa were denoised at a minimum relative abundance of 0.01%, describing a final catalogue comprising 441 bacterial species from 143 genera and 12 phyla. Average mapping rate across samples was 79%. To assess functional capacity of the gut microbiome, the metagenomic sequencing reads were further annotated against the KEGG database, to identify 4,519 distinct KO’s, ranging from 1,254 to 3,885 KO’s per sample. These KO’s were mapped to 191 different pathways across 6 pathway groups.

Statistical analysis associated with the gut microbiome. Statistical analysis was performed using R Studio version 4.3, processing raw taxonomic and KEGG module count tables. Taxonomic data was first denoised whereby any count lower than 0.01% of total counts was input as 0, before any taxa that had a 0 count across all samples were removed. All taxa present in only one sample were removed. Samples were rarefied at a sequencing depth of 500,000 reads, as determined through creation of a rarefaction curve. Taxonomic alpha diversity was measured according to Shannon diversity and species richness (observed species). Beta diversity analyses were assessed for taxonomic and functional data using a Bray-Curtis dissimilarity matric on relative abundance and visualised using non-metric multidimensional scaling. Significance of NMDS clustering was calculated by PERMANOVA ‘adonis2’ function from the ‘vegan’ R package (Dixon, 2003). Differential abundance analysis was performed to identify taxa and functional pathways with significant changes in abundance according to treatment, with this was conducted using LefSE analysis within the ‘microbiomeMarker’ R package (Cao et al., 2022). LefSE was performed with a LDA threshold of 3.5 and Wilcoxon p-value <0.05. This method does not take repeated measures into account. Alpha diversity was separately fitted to a linear mixed effects model using the Tme4’ R package (Bates et al., 2014) with ‘Experimental group’ as the fixed effect and ‘Animal’ and ‘Breed’ as the random effects to account for repeated measures and breed influence. Estimated means with 95% confidence intervals (Cis) and contrast p-values are reported as calculated through the ‘multcomp R package (Hothorn et al., 2008). All outputs were visualised using the ‘ggplot2’ R package (Wilkinson, 2011).

Statistical analysis associated with the skin microbiome. Taxonomic alpha diversity was measured according to Shannon diversity and species richness (observed species). Beta diversity analyses were assessed for taxonomic and functional data using a Bray-Curtis dissimilarity matric on relative abundance and visualised using non-metric multidimensional scaling. Significance of NMDS clustering was calculated by PERMANOVA ‘adonis2’ function from the ‘vegan’ R package. Differential abundance analysis was performed to identify taxa and functional pathways with significant changes in abundance according to treatment, with this was conducted using LefSE analysis within the ‘microbiomeMarker’ R package. LefSE was performed with a LDA threshold of 3.5 and Wilcoxon p-value <0.05. This method does not take repeated measures into account. Spearman correlations were performed for genera identified as biomarkers of CAD between dorsal lumbar swab samples and abdominal swab samples using the ‘stats’ R package. Statistical significance was determined using ‘cor.mtest’ from the ‘corrplot’ package. Pairwise with Benjamini- Hochberg multiple hypothesis correlation applied using p. adjust. Statistical significance and correlation thresholds were determined r >0.6 respectively. Visualisation of correlations was performed using ggcorrplot. Alpha diversity was separately fitted to a linear mixed effects model using the Tme4’ R package (Bates et al., 2014) with ‘Experimental group’ as the fixed effect and ‘Animal’ and ‘Body site’ as the random effects to account for repeated measures and breed influence. Univariate analysis was performed for four biomarker genera of interest using a further linear mixed effects model with ‘Experimental group’ as the fixed effect and ‘Animal’, ‘Replicate’, ‘Location’ and ’Breed’ as the random effects. Estimated means with 95% confidence intervals (Cis) and contrast p-values are reported as calculated through the ‘multcomp R package (Hothorn et al., 2008). All outputs were visualised using the ‘ggplot2’ R package (Wilkinson, 2011).

Results

This study explores the relationship between the canine gut microbiome structure and canine atopic dermatitis (CAD) through shotgun metagenomic sequencing of 81 gut samples collected from the study cohort (40 healthy dogs and 41 CAD dogs). Looking first at the gut microbiome from the cohort of 38 healthy dogs, Bacteroidota predominated describing 62% (±3%) of assigned reads, with Firmicutes describing a further 26% (comprising Firmicutes [5% ± 1%], Firmicutes_A [15% ± 2%] and Firmicutes_C [6% ± 1%]) and Fusobacteriota 3% (±1%). While high abundance of well described species such as Prevotella copri (29% ± 3%), Megamonas funiformis (5% ± 1%), Blautia hansenii (2% ± 0.4%) and Ruminococcus B gnavus (2% ± 0.5%) aligns with previous description of the canine gut microbiota, high abundance of newly described taxa were identified including Phocaeicola sp900546645 (9% ± 2%) and Prevotellamassilia sp000437675 (4% ± 1%) derived from MAGs of the canine gut (Cusco et al., 2022) alongside Bacteroides sp900766005 (2% ± 0.4%) only previously described in the human gut microbiome. As described previously, the majority of functional pathways are associated with processes associated with metabolic function (n=122, 64%).

Breed has shown previous association with gut microbiome composition (You & Kim, 2021). Accounting for experimental group, the influence of breed on alpha diversity metrics was assessed for the cohort (Figure 18 A). Here, breed had a significant impact on species richness between LR’s and WHWT’s with a difference in means of 17.9 (p=0.04; 95% CI[0.5, 35.4]) and GR’s and WHWT’s with a difference in means of 19.5 (p=0.02; 95% CI[2.3, 36.7]). Species richness was not significantly different between large breed dogs with a difference in means of 1.6 (p=0.97; 95% CI[- 13.7, 16.8]). No significant influence of breed on Shannon diversity (p>0.05) was observed. Similarly, breed resulted in both significant separation using beta diversity metrics of taxonomic (PERMANOVA [p=0.001, R2= 0.1]; Figure 18B) and functional (PERMANOVA [p=0.008, R2= 0.06]; Figure 18B) data. Visualised using nMDS, community composition of the gut microbiome of the two large breed dogs were strongly overlapped with WHWT’s showing significantly distinct clustering. No significant difference in sample dispersion was observed for either taxonomic (p=0.1; Figure 18C) or functional (p=0.5; Figure 18C) composition across the three breeds indicating comparable within breed similarity of the samples. Downstream analysis would take breed effects into account unless stated otherwise. Neither dog age nor sample storage time was associated with significant changes to the gut microbiome.

CAD is associated with key changes in relative abundance of multiple taxa and functional pathways within the gut microbiome. Using models accounting for the influence of breed, CAD had no significant impact on gut microbiome species richness, with a difference in mean compared to healthy dogs of -8.0 (p=0.16, 95% CI[-19.2, 3.2]) or Shannon diversity, with a difference in means of -0.03 (p=0.81, 95% CI[-0.3, 0.2]) compared to healthy dogs. Assessing similarity of whole community composition using beta diversity visualisation, the microbiome composition of the CAD group was significantly different to that of healthy dogs for both taxonomy (PERMANOVA [p=0.001, R2= 0.06]; Figure 19A) and function (PERMANOVA [p= 0.001, R2= 0.08]; Figure 19B), with significantly higher within group similarity of the healthy dog microbiome compared to that of CAD dogs (dispersion p=0.001 & p=0.003 respectively). Similar data trends were observed when considering large breeds and WHWT separately. Beta diversity showed no apparent association to CADESI score when colour coded and visualised using NMDS.

LEfSe differential abundance analysis was conducted on gut microbiome samples from large breed dogs (LR and GR) to determine the bacterial taxa that best characterise health and CAD groups and potential shifts in microbiome function, with these breeds showing strong similarity in microbiome composition and a high cohort sample size. A total of 19 taxa (classified at phylum level and beyond) across all phylogenetic levels were differentially abundant between healthy large breed dogs and those suffering CAD (LoglO LDA>3.5, p<0.05; Figure 19C; Figure 24), with 9 taxa enriched in the gut microbiome of CAD dogs and 10 enriched in healthy dogs. Taxa enriched in the healthy group were primarily associated with Bacteroidota (n=5), being abundant and highly prevalent species including Prevotella copri (32.0% ± 3.4% Healthy; 18.4% ± 3.6% CAD; p=0.005) and Prevotellamassilia sp000437675 (4.4% ± 0.7% Healthy; 3.0% ± 0.8% CAD; p=0.04). Firmicutes and Firmicutes C were also enriched within the healthy gut microbiome, with their abundance largely attributed to Phascolarctobacterium A (1.0% ± 0.2% Healthy; 0.7% ± 0.2% CAD; p=0.03) and Catenibacterium species (0.5% ± 0.2% Healthy; 1.5% ± 0.4% CAD; p=0.008). While some taxa assigned to Bacteroidota were similarly enriched within dogs of the CAD group (Bacteroides stercoris [0.3% ± 0.1% Healthy; 3.1% ± 1.3% CAD; p=0.007], Bacteroides uniformis [0.08% ± 0.04% Healthy; 1.4% ± 1.0% CAD; p=0.004], Phocaeicola vulgatus [1.0% ± 0.5% Healthy; 5.4% ± 1.8% CAD; p=0.04]), perhaps the most distinctive taxonomic enrichment within CAD was associated with Lachnospirales genus Ruminococus B (1.8% ± 0.5% Healthy; 5.2% ± 1.9% CAD; p=0.04) alongside the Enterobacterales genus Escherichia (0.3% ± 0.1% Healthy; 3.6% ± 2.0% CAD; p=0.04).

20 KEGG pathways with differential abundance were identified between the two states (LoglO LDA>3.0, p<0.05; Figure 19D; Figure 25); with 8 pathways enriched in dogs with CAD and 12 enriched in healthy dogs. The differentially abundant pathways belonged to 3 KEGG pathway families, with CAD states showing enrichment of pathways relating to environmental information processing; being two component systems (4.0% ± 0.05% Healthy; 4.5% ± 0.1% CAD; p<0.001) and ABC transporters (membrane transport) (3.4% ± 0.1% Healthy; 2.9% ± 0.1% CAD; p=0.001). Genetic information processing pathways were enriched in abundance within the gut microbiome of healthy dogs; being replication and repair (7.3% ± 0.08% Healthy; 7.6% ± 0.05% CAD; p=0.02), translation (5.6% ± 0.04% Healthy; 5.3% ± 0.1% CAD; p=0.03) and folding, sorting and degradation (3.0% ± 0.03% Healthy; 2.8% ± 0.05% CAD; p<0.001). Interestingly, the enriched metabolic pathway showing with the highest LDA effect size within CAD dogs was associated with xenobiotic biodegradation and metabolism (1.6% ± 0.06% Healthy; 1.9% ± 0.1% CAD; p=0.01) while in healthy dogs, three amino acid metabolism pathways were enriched (principally Lysine biosynthesis [1.6% ± 0.01% Healthy; 1.4% ± 0.2% CAD; p=0.01]).

Body site is a key driver in canine skin microbiome structure and diversity. To understand whether body site is a significant driver for skin microbiome composition and structure within the cohort, alpha and beta diversity metrics were compared across site for the 24 dogs providing both a dorsal lumbar (DL) and abdominal swab sample. When accounting for breed, individual and experimental group, taxonomic richness and Shannon diversity metrics were both significantly lower at the DL site compared to the abdomen with a difference in means from the DL to abdomen of 100.6 (p<0.001, 95% CI[66.0, 135.1]; Figure 20A) and 100.6 (p=0.04, 95% CI[0.009, 0.6]) respectively. This variation, driven by body site, was further supported by a significant difference in microbial profile according to bray-curtis dissimilarity and visualised using nMDS (PERMANOVA [p=0.001, R2= 0.04]; dispersion=0.003; Figure 20B). These results indicate that while the abdominal microbiome encompasses significantly more bacterial taxa and increased taxonomic diversity than the DL, the abdominal microbiome is significantly more conserved between different individuals of the cohort than that of the DL. As such, it is likely that the abdomen is home to a high number of low abundance, potentially transient, taxa not present within the DL; with these taxa having generally limited impact on cumulative microbiome composition. Breed showed no significant influence on alpha or beta diversity (p>0.05) of the skin microbiome at either site; therefore, skin site but not breed was accounted for within the further analyses.

Looking solely at the healthy dogs with both an abdominal and DL swab (n=15), the DL and abdomen were both dominated by Proteobacteria (DL= 52% ± 4%, abdomen= 40% ± 5%; Figure 20C), Firmicutes (DL= 22% ± 4%, abdomen= 26% ± 4%), and Bacteroidota (DL= 9% ± 2%, abdomen= 17% ± 3%) in line with previous reports (Whittle et al., 2024).

Identification of marker taxa associated with the skin microbiota of dogs with well characterised atopic dermatitis. To establish differences in the skin microbiota of healthy dogs and those with CAD, the cohort was subset to only samples from the abdominal site, encompassing samples from 17 healthy dogs and 9 CAD dogs. No significant difference in the abdominal skin microbiota, measured using alpha and beta diversity metrics (p>0.05) were observed between healthy dogs and dogs with well characterised CAD.

Sub setting instead by DL (CAD n=16, healthy n= 19), no significant difference in taxonomic richness was observed in the skin microbiota of the DL in dogs with CAD compared to healthy dogs (average of 143.4 and 90.9 respectively, p=0.06), however no difference was observed in the Shannon diversity (p=0.50; average of 3.9 and 3.8 respectively) between the groups. The DL microbiota of dogs with CAD was significantly different to that of healthy dogs using Bray-Curtis dissimilarity and visualised using nMDS (PERMANOVA [p=0.039, R2= 0.04]; dispersion=0.05), with this a result of decreased similarity of the DL microbiota between healthy individuals of the cohort compared to similarity between CAD dogs. 33 taxonomic features were identified across all phylogenetic levels (LDA >3.5, p<0.05; Figure 20A; Figure 26) that were associated with this observed difference in skin microbiota, with the majority of these taxa (88%) being enriched in dogs of the CAD group. For the CAD group, differentially abundant taxa were identified within 4 bacterial phyla (Actinobacteriota [n=7], Bacteroidota [n=l l], Firmicutes [n=6] and Proteobacteria [n=6]), with the relative abundance of both Actinobacteriota (10.5% ± 2.2% Healthy; 14.7% ± 1.6% CAD; p=0.01) and Bacteroidota (10.4% ± 1.9% Healthy; 15.4% ± 2.6%; p=0.05) significantly enriched within the DL microbiota of dogs with CAD. At higher resolution, enrichment of Actinobacteriota was associated with enrichment of the phylogenetic class Actinobacteria (10.1% ± 2.2% Healthy; 14.6% ± 1.5% CAD; p=0.01), with the genera Kocuria (Kocuria rhizophila [0.1% ± 0.06% Healthy; 1.2% ± 0.8% CAD; p=0.01]) and Nocardioides (0.09% ± 0.06% Healthy; 0.4% ± 0.1% CAD; p=0.01) also identified. Enrichment of Bacteroidota was primarily associated with multiple oral taxa, Bacteroides pyogenes (0.0% ± 0.0% Healthy; 3.1% ± 2.3% CAD; p=0.02), Porphyromonas cangingivalis (1.1% ± 0.4% Healthy; 2.2% ± 0.5% CAD; p=0.04) and Bergeyella (0.7% ± 0.3% Healthy; 2.0% ± 0.6% CAD; p=0.01). Of particular interest, Staphylococcus abundance was characteristic of the DL microbiota in CAD dogs (2.1% ± 0.6% Healthy; 6.9% ± 1.7% CAD; p=0.01), with abundance of this taxa in atopic dogs well described in published research. Of only four taxa showing significantly higher abundance within the DL of healthy dogs compared to that of CAD dogs, Clostridia (4.8% ± 1.0% Healthy; 2.2% ± 1.2% CAD; p=0.01) and Enterob acteriaceae (2.1 % ± 0.5% Healthy; 0.8 % ± 0.3% CAD; p=0.04) showed highest LDA and average relative abundance, with no further resolution beyond these levels.

Further confirming the differential abundance of the identified taxonomic biomarkers within the skin microbiota of dogs with CAD compared to healthy controls, univariate analysis was performed on the relative abundance of all biomarker taxa classified sufficiently to genus level only (n=6). Utilising this method to account for the effects of body site and individual animal (i.e. replicate samples), the sample set was expanded to incorporate all replicate samples and samples from unknown location alongside samples utilised within previous analyses, creating a final sample set of 215 swab samples from 56 dogs (n=23 CAD; n=33 Healthy). While Nocardioides and Flavobacterium showed no significant difference in abundance between the skin microbiota of atopic and healthy dogs within the expanded dataset, Capnocytophaga, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Aureimonas. and Staphylococcus showed significantly different relative abundance between the two groups in support of previous analysis.

Abundance of marker taxa for CAD is unlikely to be localised to specific body sites. Although the abdominal microbiota of the cohort was, as described, significantly different in composition to that of the DL microbiota within individuals, there was a strong correlation in the abundance of the 29 identified marker taxa described above for atopic dermatitis within the DL microbiota and the abdominal microbiota in 78% (7/9) of atopic dogs where samples were collected from both body sites. These results indicate that the increased abundance of these marker taxa is not localised to the DL and suggests overall skin microbiota dysbiosis in CAD dogs.

Discussion

Atopic dermatitis is a chronic, inflammatory skin condition that affects both humans and dogs. Canine atopic dermatitis (CAD) is highly prevalent, affecting around 10-15% of dogs globally (Hillier & Griffin, 2001). Here, the taxonomic and functional differences in both the skin and gut microbiome of healthy dogs was explored compared to dogs with clinical signs of CAD across three breeds deemed genetically predisposed. Evidence of clear microbial dysbiosis is demonstrated in both the gut and skin microbiome of atopic dogs, with associated changes to the relative abundance of multiple marker taxa and their respective functional pathways.

The skin microbiome acts as the first interface between the exogenous environment and an individual; having more recently been alluded to play a crucial role in the maintenance of skin homeostasis (Swaney & Kalan, 2021). Consistent with other reports of the healthy canine skin microbiome, both the DL and abdomen of healthy dogs were dominated by Proteobacteria, Bacteroidota and Firmicutes, and skin site was observed as a driver of variation in skin microbiome composition (Whittle et al., 2024). In line with previous findings (Rodrigues Hoffmann et al., 2014), the DL microbiome of healthy dogs was significantly different to that of the abdomen, with reduced taxonomic richness and diversity alongside increased between sample variability. With a higher density of hair follicles and sebaceous glands on the DL compared to the abdomen, the significantly increased variability within samples of DL microbiome could be the result of well described variation in sebum production and composition between individuals, with both factors previously shown to significantly influence microbial community structure and function (Rodrigues Hoffmann et al., 2014).

While no significant difference was observed between alpha diversity metrics of CAD compared to healthy dogs within either the DL or the abdomen microbiome, assessment of whole community composition using beta diversity metrics showed clear evidence of dysbiosis of the DL skin microbiome in dogs suffering CAD. With the DL microbiome of CAD showing significantly decreased inter-individual variability compared to that of healthy dogs, it is likely that this increased microbiome similarity is driven by conserved changes to relative abundance of prevalent taxa within the skin of dogs suffering CAD. Identifying taxa that might be driving this change using differential abundance analysis, significant increases were described in abundance of multiple prevalent taxa across four different phyla, with the majority of these being concomitantly described within the canine oral cavity. As defined for humans, predominant members of the oral microbiome are often found at different abundance across multiple body sites, including the skin (Rodrigues Hoffmann et al., 2014). It is likely that the increased occurrence of these oral associated taxa within the skin microbiome of atopic dogs are the result of increased licking activity associated with CAD clinical signs (Rodrigues Hoffmann et al., 2014). At higher resolution, differential abundance of four of the six key genera identified as biomarkers of CAD within the DL was conserved when describing the larger cohort of 56 dogs across three breeds and two sampling locations. For the first time, clear evidence is shown of a link between the abundance of three of these poorly described genera (Capnocytophaga, Allorhizobium-Neorhizobium-Pararhizobium- Rhizobium. Aureimonas) and canine atopic dermatitis, providing an essential baseline for future work examining the function of these taxa and how this relates to atopic dermatitis phenotype. A well described characteristic of canine atopic dermatitis (Bradley et al., 2016b; Geoghegan et al., 2018b; Marsella et al., 2012; Santoro & Rodrigues Hoffmann, 2016), the data supported significant enrichment of Staphylococcus within the skin microbiome of dogs with diagnosed atopy. Consistently observed increases in abundance of staphylococcal species in atopic dogs compared to healthy dogs strongly suggests changes to colonisation plays an important role in CAD and subsequent pyoderma. Within the cohort, S. xylosus and S. schleiferi were identified as enriched within the skin microbiome of CAD dogs, implying other species of Staphylococcus are implicated in CAD, rather than only S. pseudintermedius as extensively reported in the literature. These Staphylocci species also exist ubiquitously as commensals on the canine skin microbiome in healthy dogs, with colonisation or infection of atopic skin not associated with any particular opportunistic strain or cluster of strains from these taxa (Bannoehr & Guardabassi, 2012). It has been previously suggested that alterations in the skin barrier function, particularly breed-associated reduction in filaggrin expression, alongside reductions in skin microbial diversity act to promote staphylococcal colonisation of the skin microbiome (Outerbridge & Jordan, 2021; Pucheu-Haston, 2016). Furthermore, skin lesions associated with CAD show significantly higher expression of cutaneous receptors for staphylococci species (Outerbridge & Jordan, 2021), resulting in greater adhesion of these species onto comeocytes of atopic dogs compared to healthy counterparts. Once adhered, a higher prevalence of staphylococcal colonisation in atopic dogs is implicated for its role in the high frequency of recurrent skin infections within these dogs (Fazakerley et al., 2009). In this study, lesioned skin were not specifically sampled, yet changes in the relative abundance of Staphylococcus were still observed. Additionally, dogs experiencing a flare in clinical signs at the time of appointment were not specifically recruited, including in the cohort dogs with well managed clinical signs, with the majority of the CAD cohort experiencing mild or moderate CAD (CADESI score <=35). This further suggests that Staphylococcus relative abundance is not only associated with flares but rather overall skin microbiome dysbiosis associated with CAD. Despite observing significant dissimilarity between the skin microbial communities inhabiting different body sites, strong correlation were described in the abundance of CAD biomarker taxa between the DL and the abdomen in the majority of the CAD cohort. With atopic skin lesions being reported for the CAD group in multiple body sites, as reported through CADESIv4 assessment, distinct from those sampled as part of this study, strong correlation for these marker taxa indicates their differential abundance is unlikely localised to swabbed sites and instead indicative of systemic skin microbiome dysbiosis.

In parallel with findings from the skin microbiome, the gut microbiome of dogs with well characterised CAD had a significantly different taxonomic and functional profile compared to that of the healthy cohort. While healthy dogs showed strong interindividual similarity in microbiome structure, dogs suffering CAD showed significantly higher within group variability. This could be reflective of the fact that dogs with characterized CAD were concurrently experiencing some degree of gut microbial dysbiosis (Thomsen et al., 2023). In a similar trend to that seen with canine chronic enteropathy, the increased variability identified within the gut microbiome of dogs from the study CAD group could reflect the inability of these dogs to establish a meaningful gut microbial ecosystem leading to community dysregulation. Such dysregulation could be the result of observed depletion of cornerstone microbial taxa such as Prevotella, Prevotellamassilia, and Catenibacterium often described as abundant and highly prevalent members of the core healthy canine gut microbiome (Pilla & Suchodolski, 2020). In alignment with depletion of these taxa in CAD dogs, significant depletion was identified of known aspects of their function associated with health, including amino acid metabolism pathways known for their role in gut barrier integrity (e.g. glutamate metabolism) (Krishna Rao, 2012) and provision of key host absorbed nutrients (e.g. lysine biosynthesis) (Yin et al., 2017). A commonly described marker of gut microbiome dysbiosis was observed in both humans and animals, the Gammaproteobacteria genus Escherichia, to be enriched within the gut microbiome of dogs with CAD, with this potentially the result of increased gut permeability from inflammation allowing the increase in abundance of this opportunistic facultative anaerobic species (Yin et al., 2017). Higher abundances of E. coli in faeces have been previously reported to be associated with atopic eczema in humans (Yin et al., 2017). Increases in the abundance of potentially pathogenic species, including Escherichia, underlie the enrichment of both membrane transport (ABC transporter) and signal transduction pathways (Two-component system) within atopic dogs, with these acting as key strategies for pathogens to adapt to their living environments and nutrient uptake, contributing to their survival (Ahmad et al., 2020). In conclusion, the skin and gut microbiome of dogs with well characterised atopic dermatitis were described in comparison to healthy dogs in a breed controlled prospective clinical study. Broad scale changes were observed in the skin microbiota and key marker taxa, including Staphylococcus spp, were described that are associated with CAD consistent across skin sample site and breed. Additionally, concurrent dysbiosis of the gut microbiome was observed in dogs with CAD likely consistent with that observed in other inflammatory conditions (such as CE) and primarily driven by enrichment of Escherichia spp.

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* * * Although the presently disclosed subject matter and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the present disclosure. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, and compositions of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the present disclosure of the presently disclosed subject matter, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the presently disclosed subject matter. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps. Various patents, patent applications, publications, product descriptions, protocols, and sequence accession numbers are cited throughout this application, this present disclosures of which are incorporated herein by reference in their entireties for all purposes.

Claims

WHAT IS CLAIMED IS:

1. A method for determining skin health status of an animal comprising: a. quantifying one or more microbial taxa from a sample to determine abundance or relative abundance of the one or more microbial taxa, and b. determining the skin health status of the animal.

2. The method of claim 1 , further comprising providing to the animal a topical treatment or shampoo which is formulated to improve the skin health status when the health status is “not health” or “skin disease”.

3. The method of claim 1 or 2, wherein determining the skin health status of the animal comprises comparing the abundance or relative abundance of the one or more microbial taxa with a reference abundance or relative abundance of the one or more microorganism.

4. The method of claim 3, wherein the reference abundance or relative abundance of the one or more microorganism corresponds to the abundance or relative abundance of the one or more microorganisms in one or more healthy animal.

5. The method of claim 3, wherein the reference abundance or relative abundance of the one or more microorganism corresponds to the abundance or relative abundance of the one or more microorganisms in one or more animal with skin disease.

6. The method of any one of claims 1-5, wherein the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 9.

7. The method of claim 6, wherein the skin health status is “not health” or “skin disease” when the relative abundance of the one or more microbial taxa in the sample is: a. less than the expected minimum relative abundance shown in Figure 9; or b. greater than the expected maximum relative abundance shown in Figure 9.

8. The method of any one of claims 1-5, wherein the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 14.

9. The method of any one of claims 1-5, wherein the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 17.

10. The method of any one of claims 1-5, wherein the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 24.

11. The method of claim 10, wherein the sample exhibits an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa selected from the group consisting oiFirmicutes A sp., Clostridia class, Phocaeicola vulgatus, Ruminococcus B genus, Escherichia genus, Bacteroides stercoris, Escherichia sp., Bacteroides uniformis, Terrisporobacter genus, and combinations thereof.

12. The method of claim 10, wherein the sample exhibits a reduction, decreased abundance, or decreased relative abundance of one or more microbial taxa selected from the group consisting of Prevotella genus, Prevotella copri, Prevotellamassilia genus, Prevotellamassilia sp000437675, Catenibacterium genus, Catenibacterium sp000437715, Prevotella sp., Acidaminococcales order, Acidaminococcaceae family, Phascolarctobacgerium A genus, and combinations thereof.

13. The method of any one of claims 1-5, wherein the one or more microbial taxa is selected from the group consisting of microbial taxa shown in Figure 26.

14. The method of claim 13, wherein the sample exhibits an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa selected from the group consisting of Bacteroidia class, Bacteroidota phylum, Staphylococcaceae family, Staphylococcus genus, Actinobacteria class,

Actinobacteriota phylum, Staphylococcales order, Bacteroides pyrogenes, Bacillales order, Bergeyella zoohelcum, Kocuria rhizophila, Porphyromonas cangingivalis, Staphylococcus schleiferi, Rhizobiaceae family, Acinetobacter radioresistens, Actinomycetales order, Actinomycetaceae family, Allorhizobium-Neorhizobium- Pararhizobium-Rhizobium genus, Allorhizobium-Neorhizobium-Pararhizobium- Rhizobium sp., Capnocytophaga genus, Staphylococcus xylosus, Flavobacterium sp., Flavobacterium genus, Cutibacterium sp., Nocar dioidaceae family, Nocardioides genus, Aureimonas sp., Auerimonas genus, Porphyromonas gingivicanis sp., and combinations thereof.

15. The method of claim 13, wherein the sample exhibits a reduction, decreased abundance, or decreased relative abundance of one or more microbial taxa selected from the group consisting of Clostridia class, Enterobacteriaceae family, Streptococcus mitis, Acinetobacter johnsonii, and combinations thereof.

16. The method of any one of claims 1-15, wherein the one or more microbial taxa is measured using PCR, qPCR, DNA sequencing, or shotgun metagenomics sequencing.

17. The method of any one of claims 1-16, wherein the abundance, presence, or relative abundance of the one or more microbial taxa is determined by amplifying or sequencing 16S rRNA, 16S rDNA.

18. The method of any one of claims 1-17, wherein the animal is a domestic animal.

19. The method of claim 18, wherein the domestic animal is a dog.

20. The method of any one of claims 1-19, wherein the one or more microbial taxa is associated with dermatitis, psoriasis, atopic dermatitis, cutaneous form of food allergy, pruritic diseases, bacterial folliculitis, furunculosis, allergic dermatitis, atopic dermatitis, pyoderma, mange, and immune or auto-immune dermatitis.

21. The method of any one of claims 1-20, further comprising extracting nucleic acid from the sample.

22. The method of claim 21, wherein the nucleic acid is DNA.

23. The method of claim 21, wherein the nucleic acid is RNA.

24. The method of any one of claims 1-23, wherein the sample is obtained from a conscious animal or from an unconscious animal.

25. The method of any one of claims 1-24, wherein the animal has or is suspected to have dermatitis, psoriasis, atopic dermatitis, cutaneous form of food allergy, pruritic diseases, bacterial folliculitis, furunculosis, allergic dermatitis, pyoderma, mange, and immune or auto-immune dermatitis.

26. The method of any one of claims 1-25, wherein the animal is suspected to have a skin disease or disorder due to excessive scratching or licking.

27. The method of any one of claims 1-26, wherein the skin health status comprises skin health or skin disease.

28. The method of any one of claims 1-27, wherein the one or more microbial taxa is present in a sample.

29. A method of improving the skin microbiome of a subject in need thereof, comprising administering a topical treatment or shampoo which is formulated to improve the skin microbiome, wherein the skin microbiome of the subject exhibits: a) an abundance or relative abundance of one or more microbial taxa that is less than the expected minimum relative abundance shown in Figure 9; b) an abundance or relative abundance of one or more microbial taxa that is greater than the expected maximum relative abundance shown in Figure 9; c) an enrichment, increased abundance, or increased relative abundance in one or more microbial taxa selected from the group consisting of Bacteroidia class, Bacteroidota phylum, Staphylococcaceae family, Staphylococcus genus, Actinobacteria class, Actinobacteriota phylum, Staphylococcales order, Bacteroides pyrogenes, Bacillales order, Bergeyella zoohelcum, Kocuria rhizophila, Porphyromonas cangingivalis, Staphylococcus schleiferi, Rhizobiaceae family, Acinetobacter radioresistens, Actinomycetales order, Actinomycetaceae family, Allorhizobium-Neorhizobium-Pararhizobium- Rhizobium genus, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium sp., Capnocytophaga genus, Staphylococcus xylosus, Flavobacterium sp., Flavobacterium genus, Cutibacterium sp., Nocar dioidaceae family,

Nocardioides genus, Aureimonas sp., Auerimonas genus, Porphyromonas gingivicanis sp., and combinations thereof; or d) a reduction, decreased abundance, or decreased relative abundance of one or more microbial taxa selected from the group consisting of Clostridia class, Enterobacteriaceae family, Streptococcus mitis, Acinetobacter johnsonii, and combinations thereof.

30. The method of claim 29, wherein the animal is a domestic animal.

31. The method of any one of claims 29-30, wherein the domestic animal is a dog.

32. The method of any one of claims 29-31, wherein the one or more microbial taxa is associated with dermatitis, psoriasis, atopic dermatitis, cutaneous form of food allergy, pruritic diseases, bacterial folliculitis, furunculosis, allergic dermatitis, pyoderma, mange, and immune or auto-immune dermatitis.