Diagnosis of bacterial species of canine periodontitis by quantitative polymerase chain reaction

Abstract

Disclosed herein are methods for determining oral health of an animal by compositional analysis of oral microbiome. The abundance or relative abundance of one or more classified population of microorganisms is determined using qPCR and compared to modeling data produced by a canine having a healthy or periodontal disease state.

Description

[0001] Cross - reference to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 403,542, filed on September 2, 2022, the content of which is hereby incorporated by reference in its entirety.

[0003] Sequence Listing

[0004] A sequence listing conforming to the rules of WIPO Standard ST.26 is hereby incorporated by reference. The sequence listing has been submitted as an electronic document in XML-encoded ASCII format via PatentCenter. The electronic document was created on August 29, 2023, titled "069269_0645_SL.xml", and is 97,652 bytes in size. Technical Field

[0005] The subject matter of the present disclosure relates to the compositional analysis of the canine oral microbiome. The subject matter of the present disclosure also relates to using the compositional analysis of the canine oral microbiome for the diagnosis of canine periodontal disease. Background Art

[0006] Gingival diseases, such as periodontal diseases, are common in veterinary practice, and the prevalence of such diseases is estimated to be 9% to 20% [1, 2]. Higher prevalences of 44% to 100% have been reported in studies of anesthetized dogs and examinations of autopsy samples [3 - 6]. These data suggest that there is underdiagnosis of the disease in veterinary practice because most examinations are performed on awake dogs.

[0007] Bacterial species associated with periodontal health and disease in dental plaque are known in the art [7-12]. For example, International Publication No. WO 2008 / 137506 (which is incorporated herein by reference in its entirety) describes the presence or absence of at least one microorganism in a sample from a dog's oral cavity, wherein the microorganisms associated with periodontal disease in dogs are one or more selected from the following: species of Peptostreptococcus sp., species of Synergistes sp., species of Clostridiales sp., Eubacterium nodatum, species of Selenomonas sp., species of Bacteroidetes sp., Odoribacter denticanis, Desulfomicrobium ovale, species of Moraxella sp., Bacteroides denticanoris, Fillifactor villosus, Porphyromonas canoris, Porphyromonas gulae, Treponema denticola, and Porphyromonas salivosa. International Publication No. WO 2014 / 199115 (which is incorporated herein by reference in its entirety) further describes bacteria associated with disease, particularly periodontal disease and good oral health.

[0008] A variety of techniques have been developed to assess oral diseases and conditions, such as periodontal disease. These techniques include, but are not limited to, probing the gums and obtaining dental radiographs within the oral cavity. However, these methods and techniques require general anesthesia of the animal, highly trained individuals, and are expensive. In addition, these methods may cause stress to the test animal and the owner of the test animal. Therefore, developing a method for diagnosing oral diseases or conditions, such as periodontal disease, in awake animals is attractive to veterinarians and animal owners and can improve animal welfare. Summary of the Invention

[0009] The objects and advantages of the disclosed subject matter will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosed subject matter. The additional advantages of the disclosed subject matter will be realized and attained by means of the methods and systems particularly pointed out in the written description and the claims hereof as well as the appended drawings.

[0010] In one aspect, the present disclosure provides a method for determining the oral health status of an animal. In a specific embodiment, the method for determining the oral health status of an animal includes quantifying one or more microbial taxa (e.g., microbial species) in a sample to determine the abundance or relative abundance of the one or more microbial taxa, comparing the abundance or relative abundance of the one or more microbial taxa in the sample with the abundance or relative abundance of microbial taxa in a control dataset, and determining the oral health status of the animal. In certain embodiments, the one or more microbial taxa are quantified using quantitative polymerase chain reaction (qPCR) or DNA sequencing methods. In certain embodiments, the one or more microbial taxa are associated with periodontal health or periodontal disease. In certain embodiments, the one or more microbial taxa in the control dataset are associated with periodontal health or periodontal disease.

[0011] In certain embodiments, one or more microbial taxa are bacterial species. In certain embodiments, the one or more microbial taxa are selected from the species of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp., Fusobacterium sp., Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp., Porphyromonas sp., Synergistales sp., Tannerella sp., and combinations thereof. In one embodiment, the one or more microbial taxa are selected from Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp.)COT-028, Desulfomicrobium orale COT-008, Filifactor sp. COT-064, Frigovirgula sp. COT-007, Fusobacterium sp. COT-169, Fusobacterium sp. COT-189, Gemella palaticanis COT-089, Granulicatella sp. COT-095, Helcococcus sp. COT-069, Lachnospiraceae XIVa [G-4] sp. COT-099, Lachnospiraceae XIVa [G-5] sp. COT-024, Leptotrichia sp. COT-345, Moraxella sp. COT-017, Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp. COT-044, Peptostreptococcaceae XI [G-1] sp. COT-004, Peptostreptococcaceae XI [G-1] sp. COT-006, Peptostreptococcaceae XI [G-3] sp. COT-104, Peptostreptococcaceae XI [G-4] sp. COT-019, Peptostreptococcaceae XI [G-6] sp. COT-068, Peptostreptococcaceae XIII [G-1] sp. COT-030, Peptostreptococcaceae XIII [G-2] sp. COT-077, Peptostreptococcus sp. COT-033, Peptostreptococcus sp.)COT-227, Porphyromonas cangingivalis COT-109, Porphyromonas cangingivalis COT-022, Porphyromonas gulae ICOT-052, Porphyromonas macacae COT-192, Porphyromonas sp. COT-108, a species of Syntrophobacterales [F-2,G-1] COT-138, Tannerella forsythus COT-023, and combinations thereof. In embodiments, the one or more microbial taxa are selected from Clostridiales sp. COT-028, a species of Peptostreptococcaceae XI [G-4] COT-019, Capnocytophaga sp. COT-339, and combinations thereof. In embodiments, the abundance or relative abundance of the one or more microbial taxa is determined by amplifying and / or sequencing 16S rRNA, 16S rDNA, ITS, 18S rRNA, or 18S rDNA. In certain embodiments, bacterial taxa are determined by amplifying and / or sequencing one or more of the V1-V3, V3-V4, or V4 regions of 16S rDNA, e.g., by using 454-pyrosequencing or Illumina sequencing.

[0012] In certain embodiments, the one or more microbial taxa are fungal species, archaeal species, or protozoan species.

[0013] In certain embodiments, the animal is a domestic animal. In certain embodiments, the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat. In certain embodiments, the animal is a wild animal. In certain embodiments, the wild animal is a wolf, bison, elk, deer, lion, or tiger.

[0014] In certain embodiments, the methods of the present disclosure further comprise extracting DNA from a sample. In certain embodiments, the sample is a gingival region sample, such as a gingival margin sample, a subgingival region sample, a supragingival region sample, a saliva sample, a tongue sample, or a buccal sample. In certain embodiments, the sample is obtained from a conscious animal or an unconscious animal. In certain embodiments, the animal has or is suspected of having gingivitis and / or periodontitis. In certain embodiments, the oral health condition comprises a periodontal disease.

[0015] In another aspect, the present disclosure provides a method for determining the oral health status of an animal implemented by a computer system. In certain embodiments, the method includes receiving input data of an animal, where the input data includes at least a first array, the first array includes a first plurality of entries, and each entry contains a numerical value representing the amount of one or more microbial taxon types; determining the oral health status of the animal based on the input data of the animal by a machine learning model, where the oral health status of the animal identifies the predicted oral health status classification of the animal; obtaining the oral health status of the animal by the machine learning model; and outputting the oral health status of the animal.

[0016] In certain embodiments, the input data includes one or more microbial taxa quantified in relation to the periodontal health or periodontal disease of a sample. In certain embodiments, the one or more microbial taxa are quantified using qPCR or DNA sequencing methods. In certain embodiments, the bacterial taxa are determined by amplifying and / or sequencing one or more of the V1-V3, V3-V4, or V4 regions of 16S rDNA, for example, by using 454-pyrosequencing or Illumina sequencing. In certain embodiments, one or more microbial taxa in the control dataset are related to periodontal health or periodontal disease.

[0017] In certain embodiments, the method further includes: obtaining training data of a plurality of animals, where the training data represents the amount of the relative abundance of the microbial types present in the samples of each animal among the plurality of animals; associating the training data with the oral health status classification of the animal, where associating the training data with the oral health status classification of the animal includes associating each animal among a second plurality of animals with the oral health status classification; and training a machine learning model using the training data associated with the oral health status classification of the animal.

[0018] In certain embodiments, the method further includes: identifying specific microbial taxa significantly associated with health, gingivitis, and / or periodontitis in canine dental plaque by a machine learning model to determine the oral health status of the animal.

[0019] In certain embodiments, the input data further includes one or more of an animal breed identifier, animal size, animal weight, animal age, animal health information, animal diet, geographical location information, sample location, or a combination thereof. In certain embodiments, the input data includes the abundance or relative abundance of one or more microbial taxa. In certain embodiments, one or more microbial taxa are bacterial species.

[0020] In certain embodiments, the one or more microbial taxa are selected from species of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp., Fusobacterium sp., Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp., Porphyromonas sp., Synergistales sp., Tannerella sp., and combinations thereof. In certain embodiments, the one or more microbial taxa are selected from Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp.)COT-028, Desulfomicrobium orale COT-008, Filifactor sp. COT-064, Frigovirgula sp. COT-007, Fusobacterium sp. COT-169, Fusobacterium sp. COT-189, Gemella palaticanis COT-089, Granulicatella sp. COT-095, Helcococcus sp. COT-069, Lachnospiraceae XIVa[G-4] sp. COT-099, Lachnospiraceae XIVa[G-5] sp. COT-024, Leptotrichia sp. COT-345, Moraxella sp. COT-017, Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp. COT-044, Peptostreptococcaceae XI[G-1] sp. COT-004, Peptostreptococcaceae XI[G-1] sp. COT-006, Peptostreptococcaceae XI[G-3] sp. COT-104, Peptostreptococcaceae XI[G-4] sp. COT-019, Peptostreptococcaceae XI[G-6] sp. COT-068, Peptostreptococcaceae XIII[G-1] sp. COT-030, Peptostreptococcaceae XIII[G-2] sp. COT-077, Peptostreptococcus sp. COT-033, Peptostreptococcus sp.) COT-227, Porphyromonas cangingivalis COT-109, Porphyromonas cangingivalis COT-022, Porphyromonas gulae ICOT-052, Porphyromonas macacae COT-192, Porphyromonas sp. COT-108, a species of Syntrophobacterales [F-2, G-1] COT-138, Tannerella forsythus COT-023, and combinations thereof. In certain embodiments, the one or more microbial taxa are selected from Clostridiales sp. COT-028, a species of Peptostreptococcaceae XI [G-4] COT-019, Capnocytophaga sp. COT-339, and combinations thereof.

[0021] In certain embodiments, the one or more microbial taxa are fungal species, archaeal species, or protozoan species.

[0022] In certain embodiments, the abundance or relative abundance of the one or more microbial taxa is determined by amplifying 16S rRNA, 16S rDNA, ITS (Internal Transcribed Spacer), 18S rRNA, or 18S rDNA.

[0023] In certain embodiments, the animal is a domestic animal. In certain embodiments, the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat. In certain embodiments, the animal is a wild animal. In certain embodiments, the wild animal is a wolf, bison, elk, deer, lion, or tiger.

[0024] In certain embodiments, the one or more microbial taxa are associated with periodontal health or periodontal disease. In certain embodiments, one or more microbial taxa in the control dataset are associated with periodontal health or periodontal disease. In certain embodiments, the one or more microbial taxa are present in a sample. In certain embodiments, the sample is a gingival area sample, gingival margin sample, subgingival area sample, supragingival area sample, saliva sample, tongue sample, or buccal sample. In certain embodiments, the sample is obtained from a conscious animal or an unconscious animal. In certain embodiments, the animal has or is suspected of having gingivitis and / or periodontitis.

[0025] In another aspect, the present disclosure provides a system comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors being configured to execute the instructions to: receive input data of an animal, wherein the input data at least includes a first array, the first array includes a first plurality of entries, and each entry includes a numerical value representing an amount of one or more types of microbial taxa; determine an oral health state of the animal based on the input data of the animal by a machine learning model, wherein the oral health state of the animal identifies a predicted oral health state classification of the animal; obtain the oral health state of the animal by the machine learning model; and output the oral health state of the animal.

[0026] In another aspect, the present disclosure provides a system comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors being configured to execute the instructions to implement any method disclosed herein.

[0027] In yet another aspect, the present disclosure provides a non-transitory computer-readable medium comprising: instructions that, when executed by one or more processors of a computing system, cause the one or more processors to receive input data of an animal, wherein the input data at least includes a first array, the first array includes a first plurality of entries, and each entry includes a numerical value representing an amount of one or more types of microbial taxa; determine an oral health state of the animal based on the input data of the animal by a machine learning model, wherein the oral health state of the animal identifies a predicted oral health state classification of the animal; obtain the oral health state of the animal by the machine learning model; and output the oral health state of the animal.

[0028] In yet another aspect, the present disclosure provides a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the one or more processors to implement the methods disclosed herein.

[0029] Certain embodiments of the present disclosure may include some, all, or none of these advantages. These advantages and other features will be more clearly understood from the following detailed description in conjunction with the accompanying drawings and the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The following drawings are used to illustrate certain aspects of the present disclosure, but should not be regarded as exclusive embodiments. Without departing from the scope of the present disclosure, the disclosed subject matter is capable of numerous modifications, changes, combinations, and equivalents in form and function.

[0031] Figure 1Principal component scores are provided, where the ellipses represent 95% confidence regions after analysis of the counts and proportions identified by high-throughput sequencing (HTS) and quantitative polymerase chain reaction (qPCR), respectively. Differentiated by health status: healthy (green), gingivitis (orange), and periodontitis (red); differentiated by analysis method: qPCR (open data points; dashed ellipses) and HTS (solid data points; solid ellipses).

[0032] Figures 2A through Figure 2C Correlations between high-throughput sequencing (HTS) and quantitative polymerase chain reaction (qPCR) datasets of the following bacteria obtained by analyzing the same sample cohort are provided: 2A) Capnocytophaga sp. COT-339, 2B) Peptostreptococcaceae XI [G-4] sp. COT-019, and 2C) Clostridiales sp. COT-028. Samples are differentiated by periodontal health status: healthy (green), gingivitis (orange), and periodontitis (red).

[0033] Figure 3 Sensitivity assessments of the relative 1-specificity of Peptostreptococcaceae XI [G-4] sp. COT-019, Clostridiales COT-028, and Capnocytophaga sp. COT-339 using five machine learning classification models are provided. The methods employed are logistic regression (LR, red), weighted k-nearest neighbor (KKNN, dark blue), kernel support vector machine (KSVM, yellow), linear discriminant analysis (LDA, purple), and random forest (RF, light green). Mean estimates ± standard deviations are obtained through bootstrapping tests of the optimized training models.

[0034] Figure 4 is a schematic diagram of an embodiment of a diagnostic system 100 configured to perform animal diagnosis using machine learning.

[0035] Figure 5 is a flowchart of an embodiment of an oral health status determination process 200 of the diagnostic system 100.

[0036] Figure 6 is an embodiment of a network device 102 of the diagnostic system 100. DETAILED DESCRIPTION

[0037] The subject matter of the present disclosure relates to methods for sampling the oral microbiome of animals and monitoring the oral health of animals. The subject matter of the present disclosure is particularly suitable for sampling the oral microbiome of companion animals, such as domestic dogs.

[0038] 1. Definitions

[0039] The terms used in this specification generally have their ordinary meanings in the art, in the context of the present invention, and in the particular context in which each term is used. Certain terms are discussed below or elsewhere in the specification to provide additional guidance to the practitioner in describing the methods and compositions of the present invention and how to make and use them.

[0040] Reference to the percent sequence identity between two nucleotide sequences refers to the percentage of nucleotides that are the same in the two sequences being compared when aligned. Any suitable software program can be used to determine such alignment and the percent homology or sequence identity. For example, as described in section 7.7.18 of reference

[13] . In one embodiment, the BLAST algorithm or the Smith-Waterman homology search algorithm is used to determine the alignment, where the gap opening penalty for affine gap retrieval is 12, the gap extension penalty is 2, and the BLOSUM matrix is 62. The Smith-Waterman homology search algorithm is disclosed in reference

[14] . The alignment can cover the entire reference sequence, i.e., it can cover 100% of the sequence length disclosed herein.

[0041] As used herein, when used in the claims and / or the specification in conjunction with the term "comprising", the phrase "a" or "an" can mean "one", but it is also consistent with the meanings of "one or more", "at least one", and "one or more than one". Further, the terms "having", "including", "containing", and "comprising" are interchangeable, and those skilled in the art know that these terms are open-ended terms. In addition, the term "comprising" encompasses "including" as well as "consisting of", e.g., a composition "comprising" X can consist only of X or can include some additional components, e.g., X + Y.

[0042] As used herein, the term "about" or "approximately" means within an acceptable error range of a 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, in accordance with the practice in the art, "about" can mean within 3 standard deviations or greater than 3 standard deviations. Alternatively, "about" can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, still more preferably up to 1% of a given value. Alternatively, particularly for biological systems or processes, the term can mean within an order of magnitude of the value, preferably within 5-fold, more preferably within 2-fold.

[0043] The term "taxon" refers to a taxonomic group, such as a kingdom, phylum, class, order, family, genus, and species. The term "abundance" can refer to the absolute amount (including presence or absence) of a given bacterial taxon present in a sample. For example, abundance can refer to the bacterial sequence count of a bacterial taxon after appropriate amplification of 16S rDNA. The term "relative abundance" can refer to the compositional percentage of a particular bacterial taxon (e.g., species) relative to the total number of bacteria in a sample. It can be calculated by determining the number of sequences of a given bacterial taxon divided by the total number of all bacterial sequences, and then multiplying by 100. For example, relative abundance can refer to the relative amount of nucleic acid present in a sample after appropriate amplification or sequencing of 16S rDNA. In certain embodiments, relative abundance can refer to the binary classification of a bacterial taxon. For example, but without any limitation, the binary classification can include the detected taxon and the undetected taxon, or the present taxon and the absent taxon. In certain embodiments, relative abundance is calculated as an odds ratio. As used herein, the 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 one group is compared to another group. In certain embodiments, 16S rRNA comprises or consists of one of the sequences provided in Table 1.

[0044] Table 1: 16S rRNA Sequences

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] As used herein, the term "animal" refers to a 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, etc. The term "animal" can also refer to wild animals, including but not limited to wolves, bison, elk, deer, lions, tigers, etc. In some embodiments, the animal is a companion animal. In certain cases, the animal is a dog or a cat.

[0082] As used herein, the terms "nucleic acid molecule" and "nucleotide sequence" refer to a single-stranded or double-stranded covalently linked nucleotide sequence, wherein the 3' and 5' ends of each nucleotide are linked by a phosphodiester bond. Nucleic acid molecules can contain deoxyribonucleoside bases or ribonucleoside bases, and can be synthesized in vitro or isolated from natural sources.

[0083] As used herein, the term "oral disease or disorder" refers to a disease or disorder that occurs in the oral cavity of a subject (such as an animal) and is caused by or associated with one or more bacteria. For example, the disease or disorder can affect the teeth of the subject, the structures supporting the teeth, such as the periodontal ligament, alveolar bone, or the gums. Exemplary oral diseases or disorders of the present disclosure include but are not limited to periodontal diseases, gingivostomatitis, odontoclastic resorptive lesions, and halitosis.

[0084] As used herein, the term "periodontal disease", also known as gum disease, refers to an inflammation or infection that affects the tissues surrounding the teeth. The severity of periodontal disease varies, for example, from gingivitis (such as gingivitis caused by dental plaque) to periodontitis. Examples of the spectrum can be found at https: / / avdc.org / avdc-nomenclature / .

[0085] The terms "isolated" or "purified" are used interchangeably herein and refer to a nucleic acid, polypeptide, or other biological moiety that has been separated from its naturally associated components. The term "isolated" can refer to a polypeptide that is separated and discrete from the whole organism in which the molecule is found in nature, or the polypeptide is present in the substantially absence of other biological macromolecules of the same type. For polynucleotides, the term "isolated" can refer to a nucleic acid molecule that is wholly or partially lacking the sequences that are normally associated with it in nature; or a sequence that exists in nature but has heterologous sequences associated with it; or a molecule that is separated from a chromosome.

[0086] As used herein, the term "biomarker" can refer to a characteristic that is objectively measured and evaluated as an indicator of a physiological biological process, a pathogenic process, or a pharmacological response 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 vivo or in its products and that affects or predicts an outcome or the incidence of a disease.

[0087] As used herein, and as is well known in the art, "treatment" is a method of obtaining a beneficial or desired result, including a clinical result. For the subject matter herein, beneficial or desired clinical results include, but are not limited to, alleviating or improving one or more symptoms, reducing the severity of a disorder, stabilizing the disorder state (i.e., not worsening), preventing a disorder, delaying or slowing the progression of a disorder, and / or improving or alleviating the disorder state. In certain embodiments, a reduction can be a reduction in the severity of a complication or symptom by about 0.01%, about 0.1%, about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, or about 99%. "Treatment" can also mean an extended survival period compared to the expected survival period of an untreated subject.

[0088] The "effective treatment" or "effective amount" of a substance refers to the treatment or amount of a substance sufficient to produce a beneficial or desired result, including a clinical result. Thus, the "effective treatment" or "effective amount" depends on the context in which it is applied. In the case of administering a composition (e.g., a dietary modification, a functional food, a supplement, a nutritional composition, or a pharmaceutical composition) to alter the microbiome composition of an unhealthy microbiome, an effective amount is an amount sufficient to restore the health state of the microbiome to a healthy state, which is determined according to a method disclosed herein. In certain embodiments, an effective treatment as described herein can also include administering an amount of treatment sufficient to reduce any symptoms associated with an unhealthy microbiome. The reduction can be a reduction in the severity of the symptoms of an unhealthy microbiome by about 0.01%, about 0.1%, about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, or about 99%. The effective amount can be administered one or more times. The likelihood of an effective treatment as described herein is the probability that the treatment is effective, i.e., sufficient to alter the microbiome, or to treat or improve a disorder and / or inflammation, and to alleviate symptoms.

[0089] The term "substantially" does not exclude "completely"; for example, a composition "substantially free" of Y can be completely free of Y. When necessary, the term "substantially" can be omitted from the definitions of the present disclosure.

[0090] 2. Microorganisms in the Oral Microbiome

[0091] Among other things, the present disclosure relates to kits and related methods for detecting one or more microbial taxa (e.g., bacteria) in the oral microbiome of an animal. One or more microbial taxa (e.g., bacteria) can be associated with oral diseases or disorders, such as periodontal disease, or with good oral health. The animal can be a companion animal, such as a dog or a cat.

[0092] In certain embodiments, one or more microbial taxa are one or more bacteria. In some embodiments, one or more bacteria associated with periodontal disease are selected from the species Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp. COT-028, Desulfomicrobium orale COT-008, Filifactor sp. COT-064, Frigovirgula sp. COT-007, Fusobacterium sp. COT-169, Fusobacterium sp. COT-189, Gemella palaticanis COT-089, Granulicatella sp. COT-095, Helcococcus sp. COT-069, Lachnospiraceae XIVa [G-4] species COT-099, Lachnospiraceae XIVa [G-5] species COT-024, Leptotrichia sp. COT-345, Moraxella sp. COT-017, Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp.) COT-044, a species of Peptostreptococcaceae XI [G-1]; COT-004, a species of Peptostreptococcaceae XI [G-1]; COT-006, a species of Peptostreptococcaceae XI [G-1]; COT-104, a species of Peptostreptococcaceae XI [G-3]; COT-019, a species of Peptostreptococcaceae XI [G-4]; COT-068, a species of Peptostreptococcaceae XI [G-6]; COT-030, a species of Peptostreptococcaceae XIII [G-1]; COT-077, a species of Peptostreptococcaceae XIII [G-2]; Peptostreptococcus sp. COT-033; Peptostreptococcus sp. COT-227; Porphyromonas cangingivalis COT-109; Porphyromonas cangingivalis COT-022; Porphyromonas gulae ICOT-052; Porphyromonas macacae COT-192; Porphyromonas sp. COT-108; a species of Syntrophobacterales [F-2, G-1] COT-138 or Tannerella forsythus COT-023...

[0093] The bacterial community profiles of the oral microbiome of animals may vary depending on the source of the samples collected from the animals. For example, three discrete oral ecologies can include soft tissue surfaces such as the lips, cheeks, and tongue; hard tissue surfaces such as the teeth; and saliva. In some embodiments, the oral ecology is from a hard tissue surface such as one or more teeth. In some embodiments, the oral ecology includes the gingival margin, the subgingival margin, or the supragingival margin. The methods and kits of the disclosed subject matter can be used to detect bacteria in the oral microbiome of various animals such as quadrupeds, primates, and other mammals. The methods and kits of the disclosed subject matter are particularly suitable for companion animals such as dogs, cats, and other domestic animals.

[0094] 3. Companion Animals

[0095] The subject matter of the present disclosure focuses on the health assessment of companion animals. In certain embodiments, the companion animal is a domestic dog.

[0096] Dog breed

[0097] Among other things, the present disclosure relates to methods for assessing animal health and well-being. Companion animals can vary in characteristics, including size, sex, breed, and species. However, for dogs, which are the most common members of this category, the methods can generally provide an indication of method effectiveness when applied to other animals.

[0098] As used herein, the term "size category" refers to the definition of an animal (e.g., dog, cat, etc.) based on the average weight of a particular animal breed. Animals of the same breed (e.g., dog, cat, etc.) can have relatively uniform physical characteristics, such as size, coat color, physiology, and behavior, compared to animals of different breeds. It is noted that the following discussion focuses on dogs; however, other companion animals and wild animals are also covered within the scope of the present disclosure, and the present disclosure is not intended to be limited to dogs.

[0099] The dog can be any breed of dog, including toy / teacup breeds, small breeds, small to medium breeds, medium breeds, medium to large breeds, large breeds, or extra large / giant breeds. Non-limiting examples of toy / teacup breeds include Affenpinscher, Australian Silky Terrier, Bichon Frise, Bolognese, Cavalier King Charles Spaniel, Chihuahua, Chinese Crested, Coton De Tulear, English Toy Terrier, Brussels Griffon, Havana, 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 dog breeds include, but are not limited to, Great Dane, Neapolitan Mastiff, Scottish Deerhound, Bordeaux, Newfoundland, English Mastiff, Saint Bernard, Leonberger, and Irish Wolfhound. Other non-limiting examples of breeds are listed in Wallis et al. (2021). Hybrid breeds are generally classified as toy / teacup dogs, small dogs, small to medium dogs, medium dogs, medium to large dogs, large dogs, and extra large / giant dogs based on their weight. In certain embodiments, the dog is a toy / teacup breed. In certain embodiments, the dog is a small breed, small to medium breed, medium breed, medium to large breed, large breed, or extra large / giant breed. In some embodiments, the dog is a mix of two or more breeds. In such cases, the mixed breed dog can still be classified by size according to its weight and can exhibit characteristics associated with two or more breeds of dog (e.g., behavioral characteristics, genetic characteristics, etc.).

[0100] The Fédération Cynologique Internationale currently recognizes 346 purebred dog breeds. For example, a dog's breed can be identified by observing its physical characteristics or through genetic analysis. A purebred dog is the offspring of two dogs of the same breed and is eligible for registration with an approved club or association that maintains a registry of dogs of that breed. There are many purebred dog registration programs, the most well-known of which is the Kennel Club.

[0101] Table 2: List of dog size categories.

[0102]

[0103] In certain embodiments, the dog size category is selected according to Salt et al. 2017 (Table 2). In other embodiments, the dog size category is selected according to alternative designations. Small breeds can correspond to animals with an average weight of about 6.5 kg to about 9 kg. Medium breeds can correspond to animals with an average weight of about 9 kg to about 30 kg. Large breeds can correspond to animals with an average weight of about 30 kg to about 40 kg. Giant breeds can correspond to animals with an average weight of more than about 40 kg.

[0104] 4. Methods

[0105] The disclosed methods of using the disclosed subject matter's methods can include assaying a sample to measure the amount of microbial nucleic acid. In certain embodiments, the microbial nucleic acid can be microbial DNA or RNA, such as 16S ribosomal DNA (rDNA) or 16S ribosomal RNA (rRNA). Various assays are known in the art for identifying the presence of bacteria or other markers associated with oral diseases or conditions (such as periodontal disease) or good oral health. In one embodiment, the assay is quantitative polymerase chain reaction (qPCR).

[0106] For illustrative purposes, any disclosed method can include performing an assay for testing the presence and / or relative amount of any bacteria disclosed herein. In certain embodiments, the bacteria are Peptostreptococcaceae species and / or bacteria that produce volatile organic compounds, or any other periodontal bacteria, such as but not limited to species of the genus Peptostreptococcus, species of the order Syntrophobacterales, species of the order Clostridiales, Eubacterium nodatum, species of the genus Selenomonas, species of the genus Bacteroides, Odoribacter denticanis, Desulfomicrobium ovalum, species of the genus Moraxella, Bacteroides denticola, Filifactor alocis, Porphyromonas canoris, Porphyromonas gingivalis, Treponema denticola, or Porphyromonas salivosa.

[0107] Additionally or alternatively, any disclosed method can include performing a universal qPCR assay to detect the presence of bacterial DNA in a dog's oral microbiome. Universal primers are known to those of skill in the art, examples of which 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 are incorporated by reference in their entirety. Methods for performing qPCR assays are well known in the art.

[0108] The method can also include the step of extracting nucleic acids according to methods known in the art prior to performing the qPCR assay, such as performing DNA extraction. Generally, DNA extraction can be carried out by lysing cells containing DNA and precipitating and purifying the DNA.

[0109] Additionally or alternatively, any disclosed method can include detecting bacteria by testing for the presence of bacteria in a sample. In certain embodiments, the test sample can include detecting the presence and / or relative abundance of one or more of the bacteria disclosed herein, such as bacteria associated with an oral disease or disorder (e.g., periodontal disease), bacteria associated with good oral health, or both. In certain embodiments, the test sample can include detecting the abundance or increased relative abundance compared to a training dataset (e.g., bacteria associated with good oral health, bacteria associated with an oral disease or disorder, bacteria not associated with good oral health or an oral disease or disorder, and combinations thereof). For example, detecting the presence or increased relative abundance of one or more of the bacteria disclosed herein can indicate that the animal has or is suspected of developing an oral disease or disorder.

[0110] Additionally or alternatively, any disclosed method can include detecting bacteria by testing for the absence or relatively low abundance of bacteria in a sample. In certain embodiments, the test sample can include detecting the absence or relatively low abundance of one or more of the bacteria disclosed herein, such as bacteria associated with an oral disease or disorder (e.g., periodontal disease). In certain embodiments, the test sample can include detecting a decreased abundance or decreased relative abundance compared to a training dataset. For example, detecting the absence or relatively low abundance of one or more bacteria associated with an oral disease can indicate that the animal does not have or is less likely to develop an oral disease or disorder.

[0111] Additionally or alternatively, the disclosed method of using the disclosed kit can include testing for the presence and / or relative amount of oral health-related microorganisms in a sample. In one embodiment, the test includes detecting the presence and / or relative amount of bacterial nucleic acids (e.g., DNA or RNA).

[0112] Additionally or alternatively, the disclosed method may include determining the oral health status of an animal. In a specific embodiment, the method for determining the oral health status of an animal includes quantifying one or more microbial taxa in a sample to determine the abundance or relative abundance of the one or more microbial taxa, comparing the abundance or relative abundance of the one or more microbial taxa in the sample with the abundance or relative abundance of microbial taxa in a control dataset, and determining the oral health status of the animal. In certain embodiments, the one or more microbial taxa are bacterial species. In certain embodiments, the one or more microbial taxa are selected from the species of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp., Fusobacterium sp., Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp., Porphyromonas sp., Synergistales sp., Tannerella sp., and combinations thereof. In one embodiment, the one or more microbial taxa are selected from Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp.)COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp. COT-028, Desulfomicrobium orale COT-008, Filifactor sp. COT-064, Frigovirgula sp. COT-007, Fusobacterium sp. COT-169, Fusobacterium sp. COT-189, Gemella palaticanis COT-089, Granulicatella sp. COT-095, Helcococcus sp. COT-069, Lachnospiraceae XIVa[G-4] sp. COT-099, Lachnospiraceae XIVa[G-5] sp. COT-024, Leptotrichia sp. COT-345, Moraxella sp. COT-017, Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp.)COT-044, species COT-004 of Peptostreptococcaceae XI [G-1], species COT-006 of Peptostreptococcaceae XI [G-1], species COT-104 of Peptostreptococcaceae XI [G-3], species COT-019 of Peptostreptococcaceae XI [G-4], species COT-068 of Peptostreptococcaceae XI [G-6], species COT-030 of Peptostreptococcaceae XIII [G-1], species COT-077 of Peptostreptococcaceae XIII [G-2], species COT-033 of Peptostreptococcus sp., species COT-227 of Peptostreptococcus sp., Porphyromonas cangingivalis COT-109, Porphyromonas cangingivalis COT-022, Porphyromonas gulae ICOT-052, Porphyromonas macacae COT-192, Porphyromonas sp. COT-108, species COT-138 of Syntrophobacterales [F-2,G-1], Tannerella forsythus COT-023, and combinations thereof. In an embodiment, the one or more microbial taxa are selected from species COT-028 of Clostridiales sp., species COT-019 of Peptostreptococcaceae XI [G-4], species COT-339 of Capnocytophaga sp., and combinations thereof. In certain embodiments, the one or more microbial taxa are associated with periodontal health or periodontal disease. In certain embodiments, one or more microbial taxa in the control data set are associated with periodontal health or periodontal disease. In certain embodiments, the one or more microbial taxa are fungal species, archaeal species, or protozoal species. In certain embodiments, the one or more microbial taxa are present in the sample.

[0113] In any method disclosed herein, the animal is a domestic animal. In certain embodiments, the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat. In certain embodiments, the animal is a wild animal. In certain embodiments, the wild animal is a wolf, bison, elk, deer, lion, or tiger. In certain embodiments, the methods of the present disclosure further comprise extracting DNA from a sample. In certain embodiments, the sample is a gingival area sample, such as a gingival margin sample, a subgingival area sample, a supragingival area sample, a saliva sample, a tongue sample, or a buccal sample. In certain embodiments, the sample is obtained from a conscious animal or an unconscious animal. In certain embodiments, the animal has or is suspected of having gingivitis and / or periodontitis. In certain embodiments, the oral health condition comprises a periodontal disease.

[0114] In any method disclosed herein, the one or more microbial taxa are quantified using quantitative polymerase chain reaction (qPCR) or DNA sequencing methods. In embodiments, the abundance or relative abundance of the one or more microbial taxa is determined by amplifying and / or sequencing 16S rRNA, 16S rDNA, ITS, 18S rRNA, or 18S rDNA. In certain embodiments, the bacterial taxa are determined by amplifying and / or sequencing one or more of the V1-V3, V3-V4, or V4 regions of 16S rDNA, such as by using 454-pyrosequencing or Illumina sequencing.

[0115] In any method disclosed herein, detecting the presence of a bacterium or other marker can include measuring the amount of the bacterium or other marker, and the amount can be compared to a scale that correlates the amount of the bacterium or other marker with the likelihood that the animal has an oral disease or disorder (e.g., a periodontal disease) or poor oral health. The likelihood can be expressed as a percentage. For purposes of illustration and not limitation, the Cq (cycle quantification) score of a qPCR test that detects the nucleic acid (e.g., DNA or RNA) of a bacterium associated with an oral disease or disorder (e.g., a periodontal disease) can be used to create a scale for calculating the likelihood that an animal has an oral disease or disorder (e.g., a periodontal disease). A lower Cq score can indicate the presence of a higher level of the bacterium associated with an oral disease or disorder (e.g., a periodontal disease), and thus the likelihood that the animal has an oral disease or disorder (e.g., a periodontal disease) can be higher than that of an animal with a higher Cq score.

[0116] In certain exemplary embodiments, all qPCR data can be normalized to a universal detection level for each sample; this adjusts the data for differences in the total amount of bacterial DNA in each sample, i.e., resulting in abundances relative to the total bacterial population. The data can then be linearized such that the final qPCR data output is a relative proportion (2-(Cq.测试-Cq.总) )。In some embodiments, the Cq test refers to the Cq score associated with a microbial species. In some embodiments, the Cq test refers to the Cq score associated with a canine oral taxon (COT). It can be assumed that samples with Cq test values outside the reliable range of detection (where Cq > 21) have undetectable amounts of DNA, and thus these relative proportions can be estimated as 0. Cq and Ct (cycle threshold) can be used interchangeably.

[0117] For purposes of illustration and not limitation, a report can be generated to summarize the results of sample testing. In some embodiments, electronic communication can be used to convey the report. For example, a personalized report can be generated and sent to convey the oral health status of an animal. In other embodiments, the report can be provided as a hard copy. The personalized report can include an indication system, such as a traffic light system, e.g., green, yellow, red, to convey the oral health status of the animal. The personalized report can also include a representation of the above scale as a reference and an indication of where the animal's oral health falls within the scale, e.g., 0% indicating no disease and 100% indicating severe disease.

[0118] 5. Machine learning model

[0119] In some embodiments, the present disclosure also provides a machine learning model for detecting one or more microbial taxa (e.g., bacteria) in the oral microbiome of an animal. One or more microbial taxa (e.g., bacteria) may be associated with oral diseases or conditions, such as periodontal disease, or with good oral health. The animal can be a companion animal, such as a dog or a cat. In some embodiments, the one or more microbial taxa are one or more bacteria.

[0120] The disclosed method may include a method for determining the oral health status of an animal implemented by a computer system. In certain embodiments, the method includes receiving input data of an animal, wherein the input data includes at least a first array, the first array includes a first plurality of entries, and each entry includes a numerical value representing the amount of one or more microbial taxon types; determining the oral health status of the animal based on the input data of the animal by a machine learning model, wherein the oral health status of the animal identifies the predicted oral health status classification of the animal; obtaining the oral health status of the animal by the machine learning model; and outputting the oral health status of the animal. In certain embodiments, the input data includes one or more microbial taxa quantified and related to the periodontal health or periodontal disease of the sample. In certain embodiments, the one or more microbial taxa are quantified using qPCR or DNA sequencing methods. In certain embodiments, the bacterial taxa are determined by amplifying and / or sequencing one or more of the V1-V3, V3-V4, or V4 regions of 16S rDNA, for example, by using 454-pyrosequencing or Illumina sequencing. In certain embodiments, one or more microbial taxa in the control dataset are related to periodontal health or periodontal disease. In certain embodiments, the method further includes: obtaining training data of a plurality of animals, wherein the training data represents the amount of the relative abundance of the microbial types present in the samples of each animal among the plurality of animals; associating the training data with the oral health status classification of the animal, wherein associating the training data with the oral health status classification of the animal includes associating each animal among a second plurality of animals with the oral health status classification; and training the machine learning model using the training data associated with the oral health status classification of the animal. In certain embodiments, the method further includes: identifying specific microbial taxa significantly associated with health, gingivitis, and / or periodontitis in canine dental plaque by a machine learning model to determine the oral health status of the animal. In certain embodiments, the input data further includes one or more of an animal breed identifier, animal size, animal weight, animal age, animal health information, animal diet, geographical location information, sample location, or a combination thereof. In certain embodiments, the input data includes the abundance or relative abundance of one or more microbial taxa. In certain embodiments, the one or more microbial taxa are bacterial species.

[0121] In certain embodiments, the one or more microbial taxa are selected from species of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp., Fusobacterium sp., Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp., Porphyromonas sp., Synergistales sp., Tannerella sp., and combinations thereof. In certain embodiments, the one or more microbial taxa are selected from Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp.)COT-028, Desulfomicrobium orale COT-008, Filifactor sp. COT-064, Frigovirgula sp. COT-007, Fusobacterium sp. COT-169, Fusobacterium sp. COT-189, Gemella palaticanis COT-089, Granulicatella sp. COT-095, Helcococcus sp. COT-069, Lachnospiraceae XIVa[G-4] sp. COT-099, Lachnospiraceae XIVa[G-5] sp. COT-024, Leptotrichia sp. COT-345, Moraxella sp. COT-017, Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp. COT-044, Peptostreptococcaceae XI[G-1] sp. COT-004, Peptostreptococcaceae XI[G-1] sp. COT-006, Peptostreptococcaceae XI[G-3] sp. COT-104, Peptostreptococcaceae XI[G-4] sp. COT-019, Peptostreptococcaceae XI[G-6] sp. COT-068, Peptostreptococcaceae XIII[G-1] sp. COT-030, Peptostreptococcaceae XIII[G-2] sp. COT-077, Peptostreptococcus sp. COT-033, Peptostreptococcus sp.) COT-227, Porphyromonas cangingivalis COT-109, Porphyromonas cangingivalis COT-022, Porphyromonas gulae ICOT-052, Porphyromonas macacae COT-192, Porphyromonas sp. COT-108, a species of Syntrophobacterales [F-2, G-1] COT-138, Tannerella forsythus COT-023, and combinations thereof. In certain embodiments, the one or more microbial taxa are selected from Clostridiales sp. COT-028, a species of Peptostreptococcaceae XI [G-4] COT-019, Capnocytophaga sp. COT-339, and combinations thereof. In certain embodiments, the one or more microbial taxa are fungal species, archaeal species, or protozoal species. In certain embodiments, the abundance or relative abundance of the one or more microbial taxa is determined by amplifying 16S rRNA, 16S rDNA, ITS, 18S rRNA, or 18S rDNA. In certain embodiments, the one or more microbial taxa are associated with periodontal health or periodontal disease. In certain embodiments, the one or more microbial taxa in the control dataset are associated with periodontal health or periodontal disease. In certain embodiments, the one or more microbial taxa are present in the sample.

[0122] In certain embodiments, the animal is a domestic animal. In certain embodiments, the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat. In certain embodiments, the animal is a wild animal. In certain embodiments, the wild animal is a wolf, bison, elk, deer, lion, or tiger. In certain embodiments, the sample is a gingival area sample, gingival margin sample, subgingival area sample, supragingival area sample, saliva sample, tongue sample, or buccal sample. In certain embodiments, the sample is obtained from a conscious animal or an unconscious animal. In certain embodiments, the animal has or is suspected of having gingivitis and / or periodontitis.

[0123] In another aspect, the present disclosure provides a system comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors being configured to execute the instructions to: receive input data of an animal, wherein the input data includes at least a first array, the first array includes a first plurality of entries, and each entry includes a numerical value representing an amount of one or more types of microbial taxa; determine an oral health state of the animal based on the input data of the animal by a machine learning model, wherein the oral health state of the animal identifies a predicted oral health state classification of the animal; obtain the oral health state of the animal by the machine learning model; and output the oral health state of the animal.

[0124] In another aspect, the present disclosure provides a system comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors being configured to execute the instructions to implement any method disclosed herein.

[0125] In yet another aspect, the present disclosure provides a non-transitory computer-readable medium comprising: instructions that, when executed by one or more processors of a computing system, cause the one or more processors to receive input data of an animal, wherein the input data includes at least a first array, the first array includes a first plurality of entries, and each entry includes a numerical value representing an amount of one or more types of microbial taxa; determine an oral health state of the animal based on the input data of the animal by a machine learning model, wherein the oral health state of the animal identifies a predicted oral health state classification of the animal; obtain the oral health state of the animal by the machine learning model; and output the oral health state of the animal.

[0126] In yet another aspect, the present disclosure provides a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the one or more processors to implement a method disclosed herein.

[0127] System Overview

[0128] Figure 4 FIG. 16 is a schematic diagram of an embodiment of a diagnostic system 100 configured to use machine learning for animal diagnosis. The diagnostic system 100 is generally configured to input various types of information related to the health and attributes of an animal into a machine learning model 112. The machine learning model 112 is configured to predict the oral health condition of the animal based on the provided input. This process allows the diagnostic system 100 to determine the oral health condition of the animal based on the physical attributes of the animal.

[0129] In one embodiment, the diagnostic system 100 includes one or more user devices 104 and a network device 102 that communicate with each other via a network 106. The network 106 can be any suitable type of wireless and / or wired network, including but not limited to all or part of the Internet, intranet, private network, public network, peer-to-peer network, public switched telephone network, cellular network, local area network (LAN), metropolitan area network (MAN), personal area network (PAN), wide area network (WAN), and satellite network. The network 106 can be configured to support any suitable type of communication protocol, as understood by those skilled in the art.

[0130] User device

[0131] Examples of the user device 104 include but are not limited to computers, laptops, tablets, smartphones, smart devices, Internet of Things (IoT) devices, data storage devices (e.g., universal serial bus (USB) drives or flash drives), or any other suitable type of device. The user device 104 is configured to provide input data 118 of an animal to the network device 102. The input data 118 can include information related to bacteria, fungi, or archaea information (e.g., taxon), or genetic information, animal breed identifier, animal size, animal weight, animal age, animal health information (e.g., veterinary records), animal diet, geographical location information, or any other suitable type of information related to the animal. In response to providing the input data 118 of the animal to the network device 102, the user device 104 is configured to receive the oral health status classification of the animal from the network device 102 and display the oral health status classification of the animal to the user. For example, the user device 104 can include a graphical user interface (e.g., a display or a touch screen) that allows the user to view the oral health status classification of the animal. The user device 104 can also include a touch screen, a touchpad, keys, buttons, a mouse, or any other suitable type of hardware that allows the user to provide input to the user device 104. In some embodiments, the oral health status classification can include healthy (H or PD0), gingivitis (G or PD1), and periodontitis (periodontal disease stages 2, 3, or 4; PD2, PD3, or PD4), for example, as described by the AVDC.

[0132] Network device

[0133] Examples of the network device 102 include but are not limited to servers (e.g., cloud servers), computers, laptops, or any other suitable type of network device. In one embodiment, the network device 102 includes a diagnostic engine 108 and a memory 110. Figure 6Additional details regarding the hardware configuration of network device 102 are described. Memory 110 is configured to store machine learning model 112, training data 114, test data 116, health information 122, control data set 124, and / or any other suitable type of data.

[0134] In one embodiment, diagnostic engine 108 is generally configured to employ machine learning model 112 to determine the oral health status of an animal based on information related to the animal. Instances of the operation of diagnostic engine 108 are described in more detail below. Figure 5 Instances of the operation of diagnostic engine 108 are described in more detail below.

[0135] Examples of machine learning model types include, but are not limited to, multi-layer perceptrons, recurrent neural networks (RNNs), RNN long short-term memory (LSTM), convolutional neural networks (CNNs), deep learning algorithms, probability models, linear regression, non-linear regression, or any other suitable type of algorithm or model, such as logistic regression (LR), weighted k-nearest neighbor (KKNN), kernel support vector machine (KSVM), linear discriminant analysis (LDA), and random forest (RF). Machine learning model 112 can be configured with any suitable type of hyperparameters or settings. Examples of hyperparameters and settings include, but are not limited to, sensitivity levels, tolerance levels, epoch values, number of layers (e.g., hidden layers), number of inputs, number of outputs, output type, output format, or any other suitable type of setting or combination. For example, machine learning model 112 can be configured with hyperparameters such as a learning rate of 0.15, a maximum depth of 2, and a maximum number of epochs set to 22. As another example, machine learning model 112 can be configured with hyperparameters such as a learning rate of 0.15, a maximum depth of 5, and a maximum number of epochs set to 32. In other instances, machine learning model 112 can be configured with any other suitable hyperparameters.

[0136] The machine learning model 112 is generally configured to receive input data 118 of an animal as input and output an animal oral health status classification 120 based on the provided input data 118. The animal oral health status classification 120 is a classification corresponding to the animal oral health status predicted based on the provided input data 118 (e.g., healthy (H or PD0), gingivitis (G or PD1), and periodontitis (stage 2; PD2, stage 3; PD3, or stage 4; PD4)). Additional information regarding the health status classification can be found on the American Veterinary Dental College (AVDC) website (www.avdc.org). In one embodiment, supervised learning is used to train the machine learning model 112, where the training data 114 includes information related to different animals and their corresponding labels (e.g., the animal oral health status classification 120). During training, the machine learning model 112 determines weight and bias values that allow the machine learning model 112 to map information related to different animals to the animal oral health status classification 120. Through this process, the machine learning model 112 is able to identify the animal oral health status classification 120 based on the provided input data 118. The diagnostic engine 108 can be configured to train the machine learning model 112 using any suitable technique that would be understood by a person skilled in the art. For example, the machine learning model 112 can be trained using the XGBoost algorithm. In some embodiments, the machine learning model 112 can be stored and / or trained by a device external to the network device 102.

[0137] In some embodiments, the network device 102 can be configured to use a statistical model, a regression model (e.g., a non-linear regression model), a parametric model, or any other suitable type of model in conjunction with, or instead of, the machine learning model 112.

[0138] The control data set 124 can include information related to microbial taxa, animal breed identifiers, animal size, animal weight, animal age, animal health information, animal diet, geographical location information, or any other suitable type of information related to multiple animals. For example, the control data set 124 can include information regarding the oral microbiome of canines in different oral health states.

[0139] Training data 114 may include information related to microbial taxa, animal breed identifiers, animal size, animal weight, animal age, animal health information, animal diet, geographical location information, sample location (e.g., gingival margin, supra-gingival or sub-gingival), or any other suitable type of information related to animals that can be input into the machine learning model 112. For example, the training data 114 may include at least a portion of the control data set 124 collected for multiple animals. The test data 116 has the same data type as the training data 114. In some embodiments, the test data 116 is a subset or a portion of the training data 114. For example, twenty percent of the training data 114 may be used as the test data 116. In other instances, any other suitable percentage of the training data 114 may be used as the test data 116.

[0140] Health information 122 includes information related to one or more animals. Examples of health information include, but are not limited to, contact information of the animal owner, animal name or identifier, information related to microbial taxa, animal breed identifier, DNA information, animal size, animal weight, animal age, animal health information, animal diet, geographical location information, gingivitis information, periodontitis information, or any other suitable type of information related to animals.

[0141] Oral health status determination process using machine learning

[0142] Figure 5 is a flowchart of an embodiment of the oral health status determination process 200 of the diagnostic system 100. The diagnostic system 100 may employ the process 200 to predict the oral health status of an animal using machine learning. The process 200 allows the diagnostic system 100 to input various types of information related to the health and physical attributes of the animal into the machine learning model 112 configured to predict the oral health status of the animal. This process allows the diagnostic system 100 to determine the oral health condition of the animal based on the physical attributes of the animal.

[0143] At step 202, before using machine learning model 112 to determine the animal oral health status classification 120 of an animal, network device 102 first trains the machine learning model 112 for determining the animal oral health status classification 120. During the training process, the machine learning model 112 determines the weight and bias values that allow the machine learning model 112 to map certain types of training data 114 to different types of animal oral health status classifications 120. In one embodiment, using the labeled training data 114, a supervised learning training process is used to train the machine learning model 112. The supervised learning training process can include obtaining the training data 114 of multiple animals, associating the training data 114 of each animal with the animal oral health status classification 120, and then using the training data 114 associated with the animal oral health status classification 120 to train the machine learning model 112. The association of the training data 114 with the animal oral health status classification 120 connects the metadata of each animal with its corresponding animal oral health status classification 120. After training, each machine learning model 112 is configured to receive the microbial taxon, animal breed identifier, animal size, animal weight, animal age, animal health information, animal diet, geographical location information, sample location (e.g., gingival margin, supra-gingival or sub-gingival), or any other suitable type of information related to the animal as input, and output the animal oral health status classification 120 based on the input data 118. Through this process, each machine learning model 112 is trained to predict the oral health status of the animal (i.e., the animal oral health status classification 120) based on the input data 118. The network device 102 can be configured to use any suitable technique to train the machine learning model 112. In some embodiments, the machine learning model 112 can be trained by a third-party device (e.g., a cloud server) external to the network device 102. After training the machine learning model 112, the machine learning model 112 is stored in a memory (e.g., memory 110). This ends the training process of the machine learning model 112.

[0144] At step 204, network device 102 obtains input data 118 of an animal. In one embodiment, network device 102 may obtain input data 118 from user device 104. For example, user device 104 may send or transmit input data 118 to network device 102 as a message or data file. In this instance, user device 104 may use any suitable message transmission or data transmission technology to send or transmit input data 118 to network device 102. In some embodiments, a user may directly provide input data 118 to network device 102. For example, a user may input (e.g., type) input data 118 into network device 102 using a user interface on network device 102 (e.g., a keyboard, a mouse, and / or a touch screen). Input data 118 may include any suitable combination of microbial taxa, animal breed identifiers, animal size, animal weight, animal age, animal health information, animal diet, geographical location information, sample location (e.g., gingival margin, supra-gingival or sub-gingival), or any other information related to the animal.

[0145] In one embodiment, input data 118 includes an array of microbial taxon values. For example, qPCR amplification or sequencing of 16S rDNA, bacterial genomes, or archaeal genomes may be used to determine bacterial taxa. These methods identify the types and / or amounts of bacteria present in a sample collected from an animal's oral cavity. The sample may contain bacteria from the gingival area (e.g., near the gums), sub-gingival area (e.g., below the gum line), supra-gingival (e.g., above the gums), saliva, tongue, and buccal samples in the animal's oral cavity. Additional details of the process of collecting samples and identifying bacterial taxa from the samples are also provided below. For example, input data 118 may include an array associated with different types of bacteria present in an animal's oral cavity. The array includes a plurality of entries, each entry associated with a specific type of bacteria. In this instance, a value of zero for an entry indicates that the type of bacteria associated with that entry is not present or undetectable in the animal's oral cavity. A value of 1 for an entry indicates that the type of bacteria associated with that entry is present or detectable in the animal's oral cavity. In some embodiments, an entry may include a numerical value representing the amount of bacteria of each type of bacteria present or detected in the animal's oral cavity. In other embodiments, the amount of a bacterial taxon, such as a Cq value, may be a numerical value or code that uniquely identifies the type of bacteria present in the animal's oral cavity. For example, each type of bacteria may be associated with a unique numerical value or code. In other embodiments, bacterial taxa may use any other suitable type of format or data structure to identify the types of bacteria present in the animal's oral cavity. In one embodiment, bacterial taxa may include one or more, preferably two bacterial taxa. In some embodiments, bacterial taxa are preferably collected from the sub-gingival portion of the oral cavity.

[0146] In one embodiment, the input data 118 includes an array of eukaryotic taxon values. For example, eukaryotic taxa can be determined using qPCR amplification or sequencing of eukaryotic genomes, such as fungal genomes or protozoan genomes.

[0147] In some embodiments, microbial taxa can be collected from an animal while it is awake. In such cases, microbial taxa can be collected from the gingival margin, subgingival area, supragingival area, saliva, tongue, or buccal samples of the animal's oral cavity. In some embodiments, microbial taxa can be collected from an unconscious animal. In such cases, microbial taxa can be collected from the gingival margin, subgingival area, supragingival area, saliva, tongue, or buccal samples of the animal's oral cavity.

[0148] In some embodiments, the input data 118 can further include an animal breed identifier that identifies the breed of the animal. Examples of animal breeds include, but are not limited to, Affenpinscher, Australian Silky Terrier, Bichon Frise, Bolognese, Cavalier King Charles Spaniel, Chihuahua, Chinese Crested, Cotton Puff, English Toy Terrier, Brussels Griffon, Havana, Italian Greyhound, Japanese Chin, King Charles Spaniel (Lutino), Maltese, Miniature Pinscher, Papillon, Pekingese, Pomeranian, Pug, Russian Toy, Yorkshire Terrier, French Bulldog, Beagle, Dachshund, Pembroke Welsh Corgi, Miniature Schnauzer, Cavalier King Charles Spaniel, Shih Tzu, Boston Terrier, Bulldog, Cocker Spaniel, Shetland Sheepdog, Border Collie, Basset Hound, Siberian Husky, Dalmatian, Great Dane, Neapolitan Mastiff, Scottish Deerhound, Dogue de Bordeaux, Newfoundland, English Mastiff, Saint Bernard, Leonberger, and Irish Wolfhound, and hybrid breeds. In one embodiment, one-hot encoding can be used to identify the animal breed type. For example, the input data 118 can include an array associated with different animal breeds. The array includes a plurality of entries, each entry associated with a specific breed type. In this example, a value of zero for an entry indicates that the animal is not a member of the breed type associated with that entry. A value of 1 for an entry indicates that the animal is a member of the breed type associated with that entry. In other embodiments, the animal breed identifier can be a numerical value or code that uniquely identifies the breed type. For example, each breed type can be associated with a unique numerical value. In other embodiments, the animal breed identifier can use any other suitable type of format or data structure to identify the breed type of the animal.

[0149] In some embodiments, the input data 118 may further include an animal size classification value. The animal size classification value identifies the size of the animal based on the physical dimensions and / or weight of the animal. Examples of animal sizes include, but are not limited to, toy / teacup breeds, teacup breeds, small breeds, small to medium breeds, medium breeds, medium to large breeds, large breeds, and giant / extra large breeds. For example, a toy / teacup breed may correspond to an animal that is smaller in size than a small breed. A small breed may correspond to an animal with an average weight of from about 6.5 kilograms to about 9 kilograms. A medium breed may correspond to an animal with an average weight of from about 9 kilograms to about 30 kilograms. A large breed may correspond to an animal with an average weight of from about 30 kilograms to about 40 kilograms. A giant breed may correspond to an animal with an average weight exceeding about 40 kilograms. Additional details regarding animal size and breed classification can be found in Part 3 of the present disclosure. In one embodiment, one-hot encoding may be used to identify the animal size classification. For example, the input data 118 may include an array associated with different animal size classifications. The array includes a plurality of entries, each entry associated with a particular animal size. In this example, a value of zero for an entry indicates that the animal is not a member of the animal size classification (e.g., toy / teacup breed, small breed, medium breed, or large breed) associated with that entry. A value of 1 for an entry indicates that the animal is a member of the animal size classification associated with that entry. In other embodiments, the animal size classification value may be a numerical value or code that uniquely identifies the animal size type. For example, each animal size classification may be associated with a unique numerical value. In other embodiments, the animal size classification value may use any other suitable type of format or data structure to identify the animal's animal size classification. In one embodiment, the input data 118 may include an animal size classification value and one or more, preferably two, microbial taxa. In some embodiments, the microbial taxa are preferably collected from the gingival margin, subgingival, or supragingival portions of the oral cavity.

[0150] In some embodiments, the input data 118 may further include a sample location value that identifies the location where a sample was collected from the animal's oral cavity. For example, the sample location value may include a numerical value corresponding to a gingival margin location, subgingival location, supragingival location, saliva, tongue, buccal sample, or a combination thereof.

[0151] In some embodiments, the input data 118 may further include a weight value that identifies the weight of the animal. For example, the weight value may include a numerical value corresponding to the weight of the animal (in pounds or kilograms). In other examples, the weight value may be in any other suitable unit.

[0152] In some embodiments, the input data 118 may further include a gingivitis value of the animal. The gingivitis value is a numerical value related to the time of gingival bleeding when detecting the oral cavity of the animal. In some cases, the gingivitis value may be an average value related to multiple teeth in the oral cavity of the animal. In some cases, the gingivitis value may be based on a visual inspection of gingival inflammation.

[0153] In some embodiments, the input data 118 may further include a periodontitis value of the animal. The periodontitis value is a numerical value related to the amount of periodontitis present in the oral cavity of the animal. For example, the periodontitis value may correspond to the stage of periodontitis defined by the American Veterinary Dental College (AVDC) or the number / proportion of teeth with periodontitis in the oral cavity. In some cases, the periodontitis value may be based on a visual inspection of gingival recession (such as gingival atrophy), furcation exposure (such as root exposure), or tooth mobility or loss.

[0154] In some embodiments, the input data 118 may further include geographical location information identifying the physical location associated with the animal. For example, the geographical location information may identify the country or region where the animal is located. For example, the geographical location information may identify a country, such as China, Thailand, the United Kingdom, the United States. In other embodiments, the input data 118 may further include any other suitable type of information or combination of information related to the animal. In one embodiment, one-hot encoding may be used to identify the geographical location information. For example, the input data 118 may include an array associated with the geographical location information. The array includes multiple entries, each entry associated with a specific country or region. In this example, a value of zero for an entry indicates that the animal is not located within the country or region associated with that entry. A value of 1 for an entry indicates that the animal is located within the country or region associated with that entry. In other embodiments, the geographical location information may be a numerical value or code that uniquely identifies a specific country or region. For example, each country and region may be associated with a unique numerical value. In other embodiments, the geographical location information may use any other suitable type of format or data structure to identify the physical location of the animal.

[0155] At step 206, the network device 102 inputs the input data 118 of the animal into the machine learning model 112. Here, the network device 102 inputs any suitable combination of information in the input data 118 obtained in step 204 into the machine learning model 112. For example, the network device 102 may input the input data 118 into the machine learning model 112 as an array or a sequential combination or a parallel combination of values.

[0156] At step 208, the network device 102 receives the animal oral health status classification 120 of the animal from the machine learning model 112. The machine learning model 112 is configured to predict the animal oral health status classification of the animal based on microbial taxon values, animal breed, animal size, animal weight, animal health, gingivitis values related to the animal, periodontitis values related to the animal, geographical location information related to the animal, or any other suitable type of information or a combination of such information. In response to inputting the input data 118 into the machine learning model 112, the network device 102 receives the animal oral health status classification 120 as the output from the machine learning model 112.

[0157] At step 210, the network device 102 outputs the animal oral health status classification 120. Here, the network device 102 outputs the animal oral health status classification 120 for the user to view. For example, the network device 102 can output the animal oral health status classification 120 by displaying the animal oral health status classification 120 on a graphical user interface (e.g., a display). As another example, the network device 102 can output the animal oral health status classification 120 by writing and saving the animal oral health status classification 120 in a file document. As another example, the network device 102 can output the animal oral health status classification 120 by sending the animal oral health status classification to the user device 104. In this example, the network device 102 can send the animal oral health status classification 120 to the user device 104 in the form of a message, email, text document, file, link, or any other suitable format. After the network device 102 receives the animal oral health status classification 120, the user device 104 can then use a graphical user interface (e.g., a display) to display the animal oral health status classification 120 to the user. In other examples, the network device 102 can use any other suitable technology to output the animal oral health status classification 120.

[0158] At step 212, network device 102 determines whether to process additional animal information. Here, network device 102 determines whether there is any more animal information to be processed. For example, a user may provide network device 102 with the processing of samples of one or more other animals to determine their oral health status classification. When there is still one or more samples to be processed, network device 102 determines to process additional animal information. Network device 102 returns to step 204 in response to determining to process additional animal information. In this case, network device 102 returns to step 204 to obtain input data 118 of another animal and repeats the process of using machine learning model 112 to determine the oral health status classification based on the new input data 118. Otherwise, network device 102 terminates process 200. In this case, network device 102 determines that there are no more animals to be processed and terminates process 200.

[0159] In some embodiments, the machine learning model can identify specific microbial species in canine dental plaque that are significantly associated with health, gingivitis, and / or periodontitis to determine the oral health status of an animal.

[0160] Hardware configuration of the network device

[0161] Figure 6 is an embodiment of network device 102 of diagnostic system 100. As an example, network device 102 can be a server or a computer. Network device 102 includes a processor 302, a memory 110, and a network interface 304. Network device 102 can be configured as shown or in any other suitable configuration.

[0162] Processor

[0163] The processor 302 is a hardware device that includes one or more processors operably coupled to the memory 110. The processor 302 is any electronic circuit, including but not limited to a state machine, one or more CPU chips, logic units, cores (e.g., multi-core processors), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), or digital signal processors (DSPs). The processor 302 can be a programmable logic device, a microcontroller, a microprocessor, or any suitable combination of the foregoing. The processor 302 is communicatively coupled and signal-communicates with the memory 110 and the network interface 304. The one or more processors are configured to process data and can be implemented in hardware or software. For example, the processor 302 can be an 8-bit, 16-bit, 32-bit, 64-bit, or any other suitable architecture. The processor 302 can include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers for providing operands to the ALU and storing the results of ALU operations, and a control unit for fetching instructions from memory and executing those instructions by orchestrating the coordinated operation of the ALU, registers, and other components.

[0164] The one or more processors are configured to implement various instructions. For example, the one or more processors are configured to execute diagnostic instructions 306 to implement the diagnostic engine 108. In this way, the processor 302 can be a special-purpose computer designed to implement the functions disclosed herein. In one embodiment, the diagnostic engine 108 is implemented using a logic unit, FPGA, ASIC, DSP, or any other suitable hardware. The diagnostic engine 108 is configured to operate as Figure 1 described in Section 2. For example, the diagnostic engine 108 can be configured to execute the steps of process 200 as Figure 5 described.

[0165] Memory

[0166] The memory 110 is a hardware device that operably stores any information about the foregoing Figures 1 to 6 and any other data, instructions, logic, rules, or code such that, when executed by the processor 302, the functions described herein are operably implemented. The memory 110 includes one or more disks, tape drives, or solid state drives and can be used as an overflow data storage device to store programs when selected for execution, as well as instructions and data read during program execution. The memory 110 can be volatile or non-volatile and can include read-only memory (ROM), random access memory (RAM), ternary content addressable memory (TCAM), dynamic random access memory (DRAM), and static random access memory (SRAM).

[0167] Memory 110 is operably configured to store diagnostic instructions 306, machine learning model 112, training data 114, test data 116, health information 122, control data set 124, and / or any other data or instructions. The diagnostic instructions 306 may include any suitable set of instructions, logic, rules, or code that operably executes the diagnostic engine 108. The machine learning model 112, training data 114, test data 116, health information 122, and control data set 124 are respectively configured to be similar to the machine learning model 112, training data 114, test data 116, health information 122, and control data set 124 described in Figures 4 to 5 The machine learning model 112, training data 114, test data 116, health information 122, and control data set 124 described in

[0168] Network interface

[0169] Network interface 304 is a hardware device configured to enable wired and / or wireless communication. The network interface 304 is configured to transfer data between the user device 104 and other devices, systems, or domains. For example, the network interface 304 may include an NFC interface, a Bluetooth interface, a Zigbee interface, a Z-wave interface, a radio frequency identification (RFID) interface, a WIFI interface, a LAN interface, a WAN interface, a PAN interface, a modem, a switch, or a router. The processor 302 is configured to send and receive data using the network interface 304. The network interface 304 may be configured to use any suitable type of communication protocol, as understood by those skilled in the art.

[0170] Examples

[0171] For purposes of illustration and not limitation, the subject matter of the present disclosure will be better understood by reference to the following embodiments, which are provided by way of example and not limitation of the subject matter of the disclosure.

[0172] Example 1: Diagnostic value of species associated with periodontal disease in companion animals

[0173] This example evaluated bacterial species of the canine oral microbiome associated with periodontal health or periodontal disease. These involved qPCR measurements of the relative total sample abundance of individual species, followed by application of a machine learning model to determine their diagnostic potential.

[0174] Materials and methods

[0175] Sampling strategies and study cohort

[0176] The sample set for the study was generated in a previous study described by Davis et al. (2013). Briefly, subgingival plaque samples were collected from a cohort of 223 dogs, of which 72 dogs had healthy gingiva (clinically normal; periodontitis stage 0, PD0), 77 dogs had gingivitis (gingivitis only, no attachment loss; periodontitis stage 1, PD1), and 74 dogs had early periodontitis (attachment loss up to 25%; periodontitis stage 2, PD2), where the stage of periodontitis was defined according to the nomenclature of the American Veterinary Dental College (AVDC). Subgingival plaque samples were collected from multiple teeth of the same health status in the same dog. Specific details regarding these collections, including inclusion / exclusion criteria, assignment of clinical health status, and relevant metadata, can be found in Davis et al. (2013).

[0177] High - throughput sequencing (HTS) data

[0178] The plaque samples were analyzed by 454 pyrosequencing to identify microbial taxa, as described in Davis et al. (2013). Taxonomy was assigned, and the number of sequence reads assigned to a specific taxonomic classification at the species and genus levels was determined, as previously described by Davis et al. (2013).

[0179] Development and validation of qPCR assays

[0180] qPCR assays were designed based on the 16S rRNA sequence information of individual taxa. The taxa selected for assay development were prioritized based on their association with periodontal health or disease and their prevalence ranking (% of samples containing the species) and relative abundance (% of the species in the total bacterial population). All probes were designed with a fluorescein (FAM)-based reporter dye and either a TaqMan minor groove binder (MGB) or a black hole quencher (BHQ). Briefly, assays were designed using the full-length consensus 16S sequences in a clone library developed in a previous study to characterize the canine oral microbiota (Dewhirst et al., 2012). The sequences were aligned in Vector NTI using the AlignX tool (Invitrogen TM , Thermo Fisher Scientific Inc.) to identify regions of maximum variation around the 16S rRNA variable regions V1 and V2. Subsequently, candidate primer and probe selections were generated using Primer Express 3 (Applied Biosystems TM , Thermo Fisher Scientific Inc.).

[0181] The performance of all assays was initially evaluated by reaction efficiency, which was determined using a 10-fold dilution series of amplified target clone DNA. Any cut-off value of ≥80% efficiency was defined as a requirement for the assay to progress to the subsequent validation phase.

[0182] A clone library was used to evaluate assay specificity, where amplified clone DNA was pooled into groups of 10 clones, with equal amounts of DNA used for each clone. Each assay was tested against all clone pools. Any clone pool that did not contain the target sequence but showed amplification was further investigated, with each clone in the pool tested individually. Cross-reactivity with non-target clones was evaluated based on the possible maximum proportion of non-target clones as judged by 454 pyrosequencing data. For an assay to be acceptable for further use, the contribution of non-target signals could not exceed 10%, where non-target sequences were present at their maximum possible proportion.

[0183] qPCR analysis of plaque DNA samples

[0184] qPCR assays were performed on the same subset of DNA samples, which were extracted from 205 subgingival plaque samples (70 with healthy gingiva, 69 with gingivitis, 66 with early periodontitis) used for HTS (Davis et al., 2013). Briefly, the qPCR reactions were performed using a Biomark system with 48x48 assay chips (Standard BioTools Inc., formerly Fluidigm Corporation Ltd). Due to the high-throughput nature of this platform, a pre-amplification, enrichment step was performed. A pre-amplification mixture was prepared, which consisted of 25 μL TaqMan TM PreAmp Master Mix (Thermo Fisher Scientific Inc.), 12.5 μL pooled assay mix, and 12.5 μL DNA. The pre-amplification conditions were an initial denaturation at 95 °C for 10 minutes, followed by 14 cycles of denaturation at 95 °C for 15 seconds and annealing at 60 °C for 4 minutes. Then, the main qPCR amplification was performed on the Biomark instrument according to the manufacturer's guidelines. Briefly, the pre-amplified sample DNA, TaqMan TM Gene Expression Master Mix (Applied Biosystems TM , Thermo Fisher Scientific Inc.) and sample loading reagent (Standard BioTools Inc.) were mixed with individual qPCR assays to a total volume of 6 μL. The chips were prepared, loaded, and run according to the manufacturer's instructions.

[0185] qPCR data analysis

[0186] Before analyzing using GenEx TM v6.0 (MultiD Analyses AB, Sweden), export the Cq data. The same threshold is applied to all runs of the same qPCR assay on different Biomark chips. Briefly, use the outlier test included in GenEx TM to remove outlier data points, where the outlier test is based on the Grubb's test (Grubbs 1969) and has options to set the confidence level and the cut-off standard deviation (SD). The confidence level is set to 0.10 and the SD cut-off level is set to 0.01. Using the reaction efficiency levels previously determined for each individual assay (see Methods: qPCR assay development and validation), all data points are adjusted to account for these differences. The mean of all replicate data points is calculated. All qPCR data are normalized to a common assay level for each sample; this adjusts the data for differences in the total amount of bacterial DNA in each sample. The data are then linearized such that the final qPCR data output is a relative ratio (2 -(Cq.测试-Cq.总) ). In some embodiments, the Cq test refers to the Cq score associated with a microbial species. In some embodiments, the Cq test refers to the Cq score associated with a canine oral taxon (COT). If the Cq test value of a sample is outside the reliable range of the assay (where Cq > 21), its DNA content is considered undetectable and thus the relative ratio is estimated as 0.

[0187] Comparison of HTS and qPCR analysis techniques

[0188] Before analysis, take the log 10 transform (+0.0003 to allow zeros) of the relative ratios of the HTS and qPCR outputs. Perform principal component analysis (PCA) on the log 10 ratios to assess the bacterial species profiles and explore any potential clustering according to the analysis method or health status. Assuming a multivariate t-distribution (Fox & Weisberg 2011), calculate the ellipses representing the 95% bivariate confidence regions for PC1 and PC2 for each combination of analysis method and health status and include them in the PCA score plot. Perform the analysis in R v4.2.1 statistical software (R Core Team 2022), using the vegan (Oksanen et al., 2022) and ggplot2 libraries.

[0189] Then, the relative proportions of HTS and qPCR analysis methods for each bacterial species were compared using the Pearson correlation coefficient. Additionally, the non-parametric Spearman rank correlation coefficient was calculated to test the sensitivity of the correlation estimate to the estimated zero relative proportions in qPCR assays due to the limit of detection.

[0190] Modeling of microbial diagnostic biomarkers

[0191] The qPCR data for each microbial taxon were modeled to evaluate the prediction of a single species for periodontitis. Modeling was defined as classifying samples as "periodontitis" (PD2) or "non-periodontitis" (healthy, PD0 and gingivitis, PD1). Five classification machine learning methods were used to evaluate the diagnostic ability of each species: logistic regression (LR), linear discriminant analysis (LDA), random forest (RF), kernel support vector machine (KSVM), and weighted k-nearest neighbor (KKNN). Five-fold cross-validation was used and hyperparameter tuning was performed using grid search to optimize the model to maximize the accuracy parameter (total number of correctly classified samples / total number of samples).

[0192] Before modeling, the samples were divided into training and test subsets and stratified by health status to reduce over-specification of the "non-periodontitis" status. Specifically, 72 samples (34 PD2: 18 PD1: 20 PD0) were used to train the model, and the remaining samples were used for bootstrapping the test subset, where the proportion of 35 PD2: 20 PD1: 20 PD0 samples was repeated 1000 times. Then, the performance of the model was evaluated using the mean accuracy, sensitivity (true positive rate: % of periodontitis samples correctly classified as periodontitis), and specificity (true negative rate: % of non-periodontitis samples correctly classified as non-periodontitis) and their standard deviations across these 1000 bootstrap replicates.

[0193] The machine learning models were implemented on R v4.2.1 statistical software using the mlr (Bischl et al., 2016) library.

[0194] Results

[0195] Development and validation of single - species qPCR assays

[0196] Based on a collection of clinical insights derived from a series of cross-sectional and longitudinal HTS studies (Davis et al. 2013, Ruparell et al. 2021, Wallis et al. 2015, Wallis et al. 2021), a qPCR assay panel was developed to enable quantification of specific single bacterial species (Table 3).

[0197] Forty-one qPCR assays targeting single bacterial species were successfully developed and validated. The robustness of each qPCR assay was evaluated by efficiency and specificity parameters. For 32 out of 41 assays (78%), the efficiency, which measures the percentage of target molecules replicated in each PCR cycle, was determined to be 90% (Table 3). The remaining 9 assays (22%) showed efficiencies ranging from 79.21% to 89.54% (Table 3). The efficiency of 79.21% for Moraxella species COT-017 was accepted as it was close to the cut-off value (≥80%). Specificity of the target detection for each assay was determined by screening a library containing 415 clones representing different bacterial species of the canine oral microbiota (Dewhirst et al., 2012). All 41 assays met the threshold criteria, confirming that cross-reactivity was low enough to be considered negligible.

[0198] Table 3: Complete and truncated quantitative polymerase chain reaction (qPCR) assay names and efficiency data.

[0199]

[0200]

[0201] HTS and qPCR outputs show good agreement for overall and individual assays

[0202] To explore the potential of the selected set of taxa as microbial biomarkers in practical applications, the relationship between the qPCR assay outputs and the corresponding HTS outputs after analyzing a series of plaque samples was examined. PCA was used to explore the potential variability between the two analytical methods; the first component explained 24.4% of the variability in the qPCR and HTS data, and the second component explained 11.24% of the variability in the qPCR and HTS data ( Figure 1 ). Discrete clustering was observed by health status, where healthy and periodontitis samples formed discrete groups in PC1, although there was some overlap. Gingivitis samples showed the greatest variation, with samples scattered across the healthy and periodontitis clusters. This was evident regardless of the technique; both showed similarities with no distinct grouping.

[0203] To gain species-specific insights, the qPCR data for each assay was plotted against the HTS data for each corresponding plaque sample from Davis et al. (2013). Among the 41 qPCR assays, 30 were highly correlated (r > 0.8) and 10 were moderately correlated (0.8 > r > 0.5) according to the Pearson correlation coefficient (Table 4). According to the Spearman rank correlation coefficient, 25 out of 41 qPCR assays were highly correlated (r s > 0.8) and 13 were moderately correlated (0.8 > r s> 0.5) (Table 4). Highly correlated detection examples include the species COT-339 of the genus Capnocytophaga related to periodontal health, and the species COT-019 of the family Peptostreptococcaceae XI [G-4] and COT-028 of the order Clostridiales related to periodontal diseases. For these species, the HTS and qPCR detection techniques used the Pearson method (r = 0.874, r = 0.905, and r = 0.902, p < 0.001 respectively) (Table 4) and the Spearman rank method (r s = 0.805, r s = 0.902, and r s = 0.802, p < 0.001) (Figures 2A to Figure 2C ) were all highly correlated. For these three detections, the determined reaction efficiencies were 85.86%, 95.79%, and 83.21% respectively (Table 3). It is worth noting that many plaque samples showed a relative abundance of zero (i.e., below the detection limit) when detected using species-specific qPCR probes, but taxa could be detected using HTS.

[0204] Table 4: Pearson and Spearman rank correlation coefficients and their associated p-values between qPCR and HTS relative proportion data

[0205]

[0206]

[0207]

[0208] Differences in diagnostic potential among species associated with canine periodontal disease

[0209] Model the qPCR output of plaque samples for the detections developed for the species COT-339 of the genus Capnocytophaga, the species COT-019 of the family Peptostreptococcaceae XI [G-4], and COT-028 of the order Clostridiales to evaluate their sensitivity (correct classification of periodontitis samples) and specificity (correct classification of non-periodontitis samples) ( Figure 3 ). For the species COT-339 of the genus Capnocytophaga, the output sensitivities of the three models were estimated to be 82.9% to 88.6%, and the specificities were 25.0% to 27.5%. For the species COT-019 of the family Peptostreptococcaceae XI [G-4], the output sensitivities of all five models were estimated to be 60.0% to 74.3%, and the specificities were 67.5% to 85.0%. For COT-028 of the order Clostridiales, the output sensitivities of all five models were estimated to be 45.7% to 60.0%, and the specificities were 80.0% to 85.0%.

[0210] Discussion

[0211] A comprehensive assessment of the oral plaque microbiota in canine periodontal disease has enabled an understanding of the associations between specific bacterial taxa and periodontal health and disease in dogs. Based on these valuable insights, the inventors of the present disclosure established more than 40 qPCR assays that selectively target single bacterial species, enabling accurate quantification of their relative levels from a given plaque DNA sample. Establishing the assays complements knowledge of periodontal health status associations and adds a rigorously validated molecular tool, opening opportunities for diagnostic development.

[0212] The performance of the single-species targeted qPCR assays of the present disclosure can be evaluated by comparing the bacterial detection capabilities with equivalent results obtained by HTS methods. This indicates a moderate to strong overall agreement between the targeted qPCR method and the broad-spectrum HTS technology. HTS targets the ubiquitous 16S rDNA sequence, thus facilitating the amplification of all members of the microbial community in a given sample. In contrast, the method employed in the qPCR assay design utilizes the novelty of the 16S rRNA sequence region to target individual bacterial species. The qPCR assay is capable of differentiating subgingival plaque samples from dogs with healthy gingiva and periodontitis. The results are similar to those observed in a cross-sectional HTS study using the same clinical plaque samples (Davis et al., 2013).

[0213] Subsequent evaluation of the two technologies focused on individual qPCR assays and yielded different correlation coefficients. Approximately 60% of the validated assays showed an r s > 0.8 using the Spearman correlation coefficient method, including species COT-339 of the genus Capnocytophaga, which is associated with canine periodontal health, and species COT-019 of Peptostreptococcaceae XI [G-4] and COT-028 of Clostridiales, which are associated with periodontal disease. These findings further enhance the feasibility of using molecular tools such as qPCR to detect microbial biomarkers of canine periodontal disease and provide comparable quantifiable inter-sample discrimination capabilities to HTS methods. In addition, scientific publications have also shown differences in the correlations between the two technologies. One study reported substantial agreement in methods for the cheese microbiota (R 2 = 0.872 and R 2= 0.929) (Dreier et al., 2022). Another comparative report for characterizing vaginal lactobacilli reported mixed results; the proportional correlation of one lactobacillus, Lactobacillus crispatus, was good (r = 0.79, p < 0.001), while that of another lactobacillus, Lactobacillus iners, was poor (r = 0.13, p > 0.05) (Smidt et al., 2015). A robust correlation between qPCR and HTS methods was also observed in non-microbial targeted studies; for four fish species, fecal-based diet analysis of Little Penguins located in Western Australia showed a strong correlation (r ≥ 0.973) (Murray et al., 2011). In the analysis reported in this article, some species-specific explorations were found to indicate that the qPCR performance and related bacterial detection differed in sensitivity from that observed by HTS. This was most evident in bacterial species with relatively low abundances present in canine oral plaque. Many samples analyzed by the corresponding qPCR assays were determined to have no target taxon present, but quantifiable levels were detected after assessment using HTS. Although the qPCR assay design was based on consensus sequences for each bacterial target, at a similarity level of 99% in this case, the specificity of HTS amplification was lower, and a broader range of related 16S targets was detected. Optimization efforts for qPCR assay design can lead to improved performance and increase the potential of these assays as diagnostic tools.

[0214] The popularity of machine learning methods has grown exponentially in numerous scientific applications, where algorithms can provide more effective insights and make better decisions on the best subsequent steps. Here, using qPCR-derived data on three microbial biomarkers related to canine periodontal health and disease, machine learning models were used to measure sensitivity and specificity parameters. Similar performance was found for five classification model types in each assay, indicating the stability of model optimization. For the species COT-339 of the genus Capnocytophaga, statistical models showed that 82.9% to 88.6% of periodontitis samples were correctly classified, while 25.0% to 27.5% of non-periodontitis samples were correctly classified. For Clostridiales COT-028, modeling showed that 45.7% to 60.0% of periodontitis samples were correctly identified, while for the species COT-019 of the family Peptostreptococcaceae XI [G-4], the corresponding results were 60.0% to 74.3%. Given the disease associations of these two taxa identified (Davis et al., 2013, Wallis et al., 2015, Ruparell et al., 2021, Wallis et al., 2021), the ability of COT-019 to correctly identify periodontitis samples in a larger proportion of cases is a positive sign. For COT-028, 80.0% to 85.0% of the samples were correctly classified as non-periodontitis; for COT-019, 67.5% to 85.0% of the samples were correctly classified as non-periodontitis. The indications were more similar between these two assays. In the development of diagnostic tests, there is a trade-off between sensitivity and specificity, i.e., the test may be good at confirming healthy subjects at the cost of potentially missing disease cases, or alternatively good at diagnosing disease cases but misclassifying healthy cases as diseased. The consequences of these scenarios need to be considered and the most appropriate balance determined for each diagnostic test. Here, the grouping of gingivitis (PD1) and healthy samples of non-periodontitis (PD0) was the same as that used in a recent study by Kwon et al. (2022); these authors referred to the "healthy condition" as the reversible group as opposed to the irreversible group. There are other methods available for sample classification; however, this method may have particular practical value. Gingivitis (PD1) can be identified by visual, awareness assessment, while the diagnosis of periodontitis (PD2) usually requires more detailed examination and represents the stage where diagnostic tests for the disease may bring the greatest value. Sample size is also a consideration, and the larger the sample set for each class used to build the model, the higher the quality and relevance of the generated output. Additionally, although machine learning methods were applied to the qPCR data generated in this study to understand the diagnostic potential of single-species taxa, they can also estimate the accuracy parameters of combined multi-species models. Alternatively, similar algorithms can be applied to existing HTS data to extract additional value from historical work.However, any output from machine learning models or laboratory-based assessments with promising diagnostic potential needs to be tested in real-world scenarios, and clinical validation studies can confirm that the predicted sensitivity and specificity parameters can be achieved.

[0215] The methods employed in this study aim to demonstrate some initial steps that can be part of a strategy for developing a tool to support the diagnosis of periodontal diseases. Compared to other characterization techniques such as HTS, molecular methods such as qPCR offer efficiency in many key consideration areas of diagnostic development, including cost, time, and requirements for data processing and interpretation. Additionally, although the method described herein focuses on qPCR for a single microbial species, high-throughput opportunities can be employed to simultaneously quantify multiple species via multiplex qPCR and enable further improvement and optimization of diagnostic accuracy parameters. Thus, in combination with conscious plaque sampling and machine learning models, qPCR can provide a tool to help address the significant gap between the prevalence and diagnosis of canine periodontitis. It is noteworthy that subgingival plaque sample sets were used for this study. However, studies comparing subgingival and gingival margin plaque in canines have shown commonalities in the microbiota observed in health and early periodontitis (Ruparell et al., 2021), thus supporting the use of supra-gingival plaque for conscious sampling to obtain opportunities based on microbial biomarkers. There are existing products in the field of canine oral health diagnosis. One such is the periodontal diagnostic strip; it detects thiols produced by microorganisms, however, their mechanism of action is non-specific and not limited to periodontal pathogens (Manfra Marretta et al., 2012, Queck et al., 2018). To date, the work in this field most consistent with that described herein was done by Kwon et al. (2022), where 11 human periodontal pathogenic species were detected based on qPCR, and it was concluded that Treponema denticola is a prognostic biomarker for periodontitis in dogs. This exploration emphasizes the importance of considering the full spectrum of periodontal diseases (PD0 - 4) as much as possible; this was only partially covered in the study reported herein (PD0 - 2), using a historical sample set that only focused on the reversible and / or manageable disease spectrum (Davis et al., 2013). In addition to periodontal diseases, numerous applications have demonstrated the success and utility of qPCR as a diagnostic tool. Prominent examples include zoonotic leptospirosis and leishmaniosis, which are caused by the bacterium Leptospira interrogans and the protozoa Leishmania infantum and Leishmania donovani, respectively. The literature on qPCR for canine veterinary applications is extensive, covering broad areas of epidemiology (Flores et al., 2017, Scorza et al., 2021, Smith et al., 2021, Griebsch et al., 2022), as well as discussions on the selection of appropriate sample types (Cavalera et al., 2022, Peris et al., 2021) and the development of detection methods (Fink et al., 2015, Miotto et al., 2018).In the field of microbiology, canine-related explanations of qPCR applications include the detection of the bacterium Ehrlichia spp. for the diagnosis of ehrlichiosis (Qurollo et al., 2014, Thomson et al., 2018) and the atypical fungus Pneumocystis, which is the cause of Pneumocystis pneumonia in immunocompromised individuals (Danesi et al., 2017, Danesi et al., 2022).

[0216] This example reports a method for developing a qPCR-based diagnostic tool to detect microbial biomarkers of canine periodontitis in supragingival plaque. Based on the strong correlation with HTS data, qPCR assays targeting specific bacterial species provide an accurate, cost- and time-efficient strategy, promising to improve the diagnosis of this common but underreported condition.

[0217] References

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[0271] ***

[0272] Although many implementations are provided in the present disclosure, it should be understood that the disclosed systems and methods may be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present embodiment is considered illustrative rather than restrictive and is not intended to be limited to the details given herein. For example, various elements or components may be combined or integrated into another system, or certain features may be omitted or not implemented.

[0273] In addition, without departing from the scope of the present disclosure, the discrete or separate technologies, systems, subsystems, and methods described and illustrated in various implementations may be combined or integrated with other systems, modules, technologies, or methods. Other items shown or discussed as being coupled or directly coupled or communicating with each other may be indirectly coupled or communicating through some interface, device, or intermediate component, whether in electrical, mechanical, or other ways. Other instances of changes, substitutions, and alterations may be determined by those skilled in the art and may be made without departing from the spirit and scope of the disclosure herein.

Claims

1. A method for determining the oral health status of an animal, the method comprising: (a) Quantifying one or more microbial taxa in a sample to determine the abundance or relative abundance of the one or more microbial taxa, (b) Comparing the abundance or relative abundance of the one or more microbial taxa in the sample with the abundance or relative abundance of microbial taxa in a control dataset, and (c) Determining the oral health status of the animal.

2. The method according to claim 1, wherein the one or more microbial taxa are bacterial species.

3. The method according to claim 2, wherein the one or more microbial taxa are selected from the group consisting of species of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp., Fusobacterium sp., Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp., Porphyromonas sp., Synergistales sp., Tannerella sp., and combinations thereof.

4. The method according to claim 2 or 3, wherein the one or more microbial taxa are selected from the group consisting of Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp. COT-028, Desulfomicrobium orale COT-008, Filifactor sp. COT-064, Frigovirgula sp. COT-007, Fusobacterium sp. COT-169, Fusobacterium sp. COT-189, Gemella palaticanis COT-089, Granulicatella sp. COT-095, Helcococcus sp. COT-069, Lachnospiraceae XIVa [G-4] sp. COT-099, Lachnospiraceae XIVa [G-5] sp. COT-024, Leptotrichia sp. COT-345, Moraxella sp. COT-017, Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp.)Species COT-044 of Peptostreptococcaceae XI [G-1], species COT-004 of Peptostreptococcaceae XI [G-1], species COT-006 of Peptostreptococcaceae XI [G-1], species COT-104 of Peptostreptococcaceae XI [G-3], species COT-019 of Peptostreptococcaceae XI [G-4], species COT-068 of Peptostreptococcaceae XI [G-6], species COT-030 of Peptostreptococcaceae XIII [G-1], species COT-077 of Peptostreptococcaceae XIII [G-2], Peptostreptococcus sp. COT-033, Peptostreptococcus sp. COT-227, Porphyromonas cangingivalis COT-109, Porphyromonas cangingivalis COT-022, Porphyromonas gulae I COT-052, Porphyromonas macacae COT-192, Porphyromonas sp. COT-108, species COT-138 of Syntrophobacterales [F-2, G-1], Tannerella forsythus COT-023, and combinations thereof.

5. The method according to any one of claims 2 to 4, wherein the one or more microbial taxa are selected from the group consisting of the species Clostridiales sp. COT-028, the species COT-019 of Peptostreptococcaceae XI [G-4], the species Capnocytophaga sp. COT-339, and combinations thereof.

6. The method according to claim 1, wherein the one or more microbial taxa are fungal species, archaeal species, or protozoan species.

7. The method according to any one of claims 1 to 6, wherein the one or more microbial taxa are quantified using qPCR or DNA sequencing methods.

8. The method according to any one of claims 1 to 7, wherein the abundance, presence, or relative abundance of the one or more microbial taxa is determined by amplifying or sequencing 16S rRNA, 16S rDNA, ITS, 18S rRNA, or 18S rDNA.

9. The method according to any one of claims 1 to 8, wherein the animal is a domestic animal.

10. The method according to claim 9, wherein the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat.

11. The method according to any one of claims 1 to 8, wherein the animal is a wild animal.

12. The method according to claim 11, wherein the wild animal is a wolf, bison, elk, deer, lion, or tiger.

13. The method according to any one of claims 1 to 12, wherein the one or more microbial taxa are associated with periodontal health or periodontal disease.

14. The method according to any one of claims 1 to 13, wherein the one or more microbial taxa in the control dataset are associated with periodontal health or periodontal disease.

15. The method according to any one of claims 1 to 14, the method further comprising extracting DNA from the sample.

16. The method according to any one of claims 1 to 15, wherein the sample is one or more of a gingival area sample, a gingival margin sample, a subgingival area sample, a supragingival area sample, a saliva sample, a tongue sample, a buccal sample, or a combination thereof.

17. The method according to any one of claims 1 to 16, wherein the sample is obtained from a conscious animal or an unconscious animal.

18. The method according to any one of claims 1 to 17, wherein the animal has or is suspected of having gingivitis and / or periodontitis.

19. The method according to any one of claims 1 to 18, wherein the oral health condition includes periodontal disease.

20. The method according to any one of claims 1 to 19, wherein the one or more microbial taxa are present in the sample.

21. A method implemented by a computer system, the method comprising: Receive input data of an animal, wherein the input data includes at least a first array, the first array contains a first plurality of entries, and each entry contains a numerical value representing the amount of one or more microbial taxon types; Determine the oral health status of the animal based on the input data of the animal by a machine learning model, wherein the oral health status of the animal identifies the predicted oral health status classification of the animal; Obtain the oral health status of the animal by the machine learning model; and Output the oral health status of the animal.

22. The method according to claim 21, wherein the input data includes one or more microbial taxa quantified in relation to the periodontal health or periodontal disease of the sample.

23. The method according to claim 21 or 22, wherein the one or more microbial taxa are quantified using qPCR or DNA sequencing methods.

24. The method according to any one of claims 21 to 23, the method further comprises: Obtain training data of a plurality of animals, wherein the training data represents the abundance or relative abundance of microbial types present in the samples for each animal in the plurality of animals; Associate the training data with the oral health status classification of the animal, wherein associating the training data with the oral health status classification of the animal includes associating each animal in a second plurality of animals with the oral health status classification; and Train a machine learning model using the training data associated with the oral health status classification of the animal.

25. The method according to any one of claims 21 to 24, the method further comprises: Identify specific microbial taxa significantly associated with health, gingivitis, and / or periodontitis in canine dental plaque by a machine learning model to determine the oral health status of the animal.

26. The method according to any one of claims 21 to 25, wherein the input data further includes one or more of an animal breed identifier, animal size, animal weight, animal age, animal health information, animal diet, geographical location information, sample location, or a combination thereof.

27. The method according to any one of claims 21 to 26, wherein the input data includes the abundance, presence, or relative abundance of one or more microbial taxa.

28. The method according to claim 27, wherein the one or more microbial taxa are bacterial species.

29. The method according to claim 28, wherein the one or more microbial taxa are selected from the group consisting of species of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp., Fusobacterium sp., Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp., Porphyromonas sp., Synergistales sp., Tannerella sp., and combinations thereof.

30. The method according to claim 28 or 29, wherein the one or more microbial taxa are selected from the group consisting of Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp. COT-028, Desulfomicrobium orale COT-008, Filifactor sp. COT-064, Frigovirgula sp. COT-007, Fusobacterium sp. COT-169, Fusobacterium sp. COT-189, Gemella palaticanis COT-089, Granulicatella sp. COT-095, Helcococcus sp. COT-069, Lachnospiraceae XIVa [G-4] sp. COT-099, Lachnospiraceae XIVa [G-5] sp. COT-024, Leptotrichia sp. COT-345, Moraxella sp. COT-017, Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp.)COT-044, species COT-004 of Peptostreptococcaceae XI [G-1], species COT-006 of Peptostreptococcaceae XI [G-1], species COT-104 of Peptostreptococcaceae XI [G-3], species COT-019 of Peptostreptococcaceae XI [G-4], species COT-068 of Peptostreptococcaceae XI [G-6], species COT-030 of Peptostreptococcaceae XIII [G-1], species COT-077 of Peptostreptococcaceae XIII [G-2], Peptostreptococcus sp. COT-033, Peptostreptococcus sp. COT-227, Porphyromonas cangingivalis COT-109, Porphyromonas cangingivalis COT-022, Porphyromonas gulae ICOT-052, Porphyromonas macacae COT-192, Porphyromonas sp. COT-108, species COT-138 of Syntrophobacterales [F-2,G-1], Tannerella forsythus COT-023, and combinations thereof.

31. The method according to any one of claims 28 to 30, wherein the one or more microbial taxa are selected from the group consisting of Clostridiales sp. COT-028, Peptostreptococcaceae XI[G-4] species COT-019, Capnocytophaga sp. COT-339, and combinations thereof.

32. The method according to claim 27, wherein the one or more microbial taxa are fungal species, archaeal species, or protozoal species.

33. The method according to any one of claims 27 to 32, wherein the abundance, presence or relative abundance of the one or more microbial taxa is determined by amplifying or sequencing 16S rRNA, 16S rDNA, ITS, 18S rRNA or 18S rDNA.

34. The method according to any one of claims 21 to 33, wherein the animal is a domestic animal.

35. The method according to claim 34, wherein the domestic animal is a dog, a cat, a horse, a cow, a ferret, a rabbit, a pig, a rat, a mouse, a gerbil, a hamster or a goat.

36. The method according to any one of claims 21 to 33, wherein the animal is a wild animal.

37. The method according to claim 36, wherein the wild animal is a wolf, a bison, an elk, a deer, a lion or a tiger.

38. The method according to any one of claims 21 to 37, wherein the one or more microbial taxa are associated with periodontal health or periodontal disease.

39. The method according to any one of claims 21 to 38, wherein the one or more microbial taxa in the control data set are associated with periodontal health or periodontal disease.

40. The method according to any one of claims 21 to 39, wherein the one or more microbial taxa are present in the sample.

41. The method according to claim 40, wherein the sample is a gingival area sample, a gingival margin sample, a subgingival area sample, a supragingival area sample, a saliva sample, a tongue sample or a buccal sample.

42. The method according to claim 41, wherein the sample is obtained from a conscious animal or an unconscious animal.

43. The method according to any one of claims 21 to 42, wherein the animal has or is suspected of having gingivitis and / or periodontitis.

44. A system, which comprises: one or more non-transitory computer-readable storage media comprising instructions; and one or more processors coupled to the storage medium, the one or more processors being configured to execute the instructions to: receive input data of an animal, wherein the input data at least comprises a first array, the first array comprising a first plurality of entries, and each entry comprises a numerical value representing an amount of one or more microbial taxa types; determine an oral health state of the animal based on the input data of the animal by a machine learning model, wherein the oral health state of the animal identifies a predicted oral health state classification of the animal; obtain the oral health state of the animal by the machine learning model; and output the oral health state of the animal.

45. A system, which comprises: one or more non-transitory computer-readable storage media comprising instructions; and one or more processors coupled to the storage medium, the one or more processors being configured to execute the instructions to implement the method according to any one of claims 21 to 43.

46. A non-transitory computer-readable medium, which comprises: instructions which, when executed by one or more processors of a computing system, cause the one or more processors to: Receive input data of an animal, wherein the input data includes at least a first array, the first array includes a first plurality of entries, and each entry includes a numerical value representing an amount of one or more types of microbial taxa; Determine an oral health state of an animal based on the input data of the animal by a machine learning model, wherein the oral health state of the animal identifies a predicted oral health state classification of the animal; Obtain the oral health state of the animal by the machine learning model; and Output the oral health state of the animal.

47. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the one or more processors to implement the method according to any one of claims 21 to 43.

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