Classifications

• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
  • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6806—Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
  • G16B30/10—Sequence alignment; Homology search
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
  • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

• This disclosure relates to systems and methods for screening for, detecting, diagnosing, treating, and identifying dermatologic and respiratory disease states in domestic cats.
• the human oral microbiome’ s importance has been implicated in dental as well as systemic diseases. Some examples include chronic inflammatory diseases, such as inflammatory bowel disease (IBD), rheumatoid arthritis, bacterial endocarditis, atherosclerosis, etc. Changes in the oral microbiome have also recently been discovered in people with allergic diseases, including atopic dermatitis.
• IBD inflammatory bowel disease
• rheumatoid arthritis bacterial endocarditis
• atherosclerosis etc.
• Changes in the oral microbiome have also recently been discovered in people with allergic diseases, including atopic dermatitis.
• atopic dermatitis is estimated to affect 12.5% of all cats, while around 1% of all feline vet visits are associated with food allergic dermatitis.
• Atopic dermatitis, food allergic dermatitis, flea allergic dermatitis and environmental allergies often present with similar symptoms, making it challenging to distinguish between them.
• the symptoms may include pruritus, scabbing and hair loss, but there is no consistent disease presentation from one case to the next. No single clinical diagnostic test can currently reliably distinguish between these four dermatologic conditions.
• Embodiments of the present disclosure include computer systems, systems and methods for screening for, detecting, diagnosing, treating, and/or identifying dermatologic and/or respiratory disease states in cats. Using such tools to guide and complement veterinary health assessment can significantly improve dermatologic, respiratory, and allergic health outcomes, by leading to precise diagnosis and earlier implementation of treatment compared to relying on veterinary visits alone.
• Embodiments of the disclosed subject matter describe a method for interrogating the oral microbiome of a cat. The disclosed methods interrogate the oral microbiome to detect microbe compositional abundance trends that may be associated with dermatologic disease states in cats.
• Detecting, identifying and/or quantifying microbial compositional abundance trends enables a practitioner to screen for and/or indicate whether a cat has a particular dermatologic and/or respiratory disease. Detecting and identifying dermatologic and/or respiratory disease states enables the practitioner and/or the cat’s owner to treat and/or prevent the dermatologic and/or respiratory disease state.
• a method for detecting and/or indicating dermatologic and/or respiratory disease in cats.
• the method may include receiving an oral swab sample taken from a cat; manipulating the sample, such as heat treatment of the oral sample; and extracting microbial deoxyribonucleic acids (DNA) from the heat-treated sample.
• the method may additionally include sequencing the microbial DNA to identify which specific one or more microbes are present in the oral sample (and in what relative proportions), wherein identifying the specific one or more microbes enables generation of an oral microbial profile for the cat.
• the method may additionally include comparing the oral microbial profile for the cat against a reference database including defined microbial profiles, wherein the database identifies correlations between (i) profiles that include one or more microbes, and (ii) corresponding dermatologic and/or respiratory diseases; and based on a result of comparing the oral microbial profile against the database of defined microbial profiles, generating a risk score indicating a likelihood that the cat has a specific dermatologic and/or respiratory disease.
• the dermatologic and/or respiratory disease state is selected from the group consisting of asthma, atopic dermatitis, flea allergic dermatitis, environmental allergic dermatitis, and food allergic dermatitis.
• the method may further include treating the specific dermatologic and/or respiratory disease and/or administering a therapeutic treatment.
• the therapeutic treatment may include administering a therapeutic compound, such as a compound designed to inhibit or encourage growth of a specific one or more microbes present in the oral microbiome of the cat.
• the therapeutic compound includes a pre-biotic, a post-biotic, a pro-biotic, a medicament or a combination thereof.
• the therapeutic compound may include an antibiotic, a corticosteroid, a bronchodilator, or a combination thereof.
• the therapeutic treatment may include a topical treatment or bath to alleviate itching and promote healing of the skin.
• a method for indicating dermatologic and/or respiratory diseases in cats includes receiving an oral swab sample taken from a cat and performing heat treatment on the oral sample. The method may also include performing magnetic beads-based deoxyribonucleic acid (DNA) extraction on the heat treated oral sample to extract microbial DNA that is present in the oral swab sample, and sequencing the microbial DNA to identify which specific one or more microbe(s) are present in the oral sample (and in what compositional abundance(s)), wherein identifying the specific one or more microbe(s) enables generation of an oral microbial profile for the cat.
• DNA deoxyribonucleic acid
• the method may additionally include comparing the oral microbial profile for the cat against a database of defined microbial profiles, wherein the database identifies correlations between (i) profiles that include one or more microbes (and their compositional abundance), and (ii) corresponding dermatologic and/or respiratory diseases; and based on a result of comparing the oral microbial profile against the database of defined microbial profiles, generating a risk score indicating a likelihood that the cat has a dermatologic and/or respiratory disease.
• the method may include, in response to generating the risk score and identifying the specific dermatologic and/or respiratory disease, administering a therapeutic treatment designed to treat the specific dermatologic and/or respiratory disease, recommending veterinary attention or follow-up examination, and/or recommending at-home care for the specific dermatologic and/or respiratory disease.
• the dermatologic and/or respiratory diseases are selected from the group consisting of asthma, atopic dermatitis, flea allergic dermatitis, environmental allergic dermatitis, and food allergic dermatitis.
• a computer system is configured to indicate dermatologic and/or respiratory disease states in cats, and includes one or more processors and one or more computer-readable hardware storage devices that store instructions executable by the one or more processors.
• the instructions may configure the computer system to receive sequenced microbial DNA data from an oral swab sample taken from a cat; map the sequenced microbial DNA to identify which specific one or more microbial species are present in the oral sample, wherein identifying the specific one or more microbial species results in generation of an oral microbial profile for the cat; calculate relative abundance of different microbial species to further build the oral microbial profile; compare the oral microbial profile against a database of defined microbial profiles, wherein the database identifies correlations between (i) profiles that include one or more microbial species and their relative abundance, and (ii) corresponding dermatologic and/or respiratory diseases; and based on a result of comparing the oral microbial profile against the database of defined microbial profiles, generate a risk score indicating a likelihood that the cat has a specific dermatologic and/or respiratory disease.
• the instructions may further configure the computer system to generate a report outlining and/or presenting the risk score and prescribing a therapeutic treatment and/or an at-home treatment protocol suitable for addressing (e.g., treating, arresting, and/or preventing) the specific dermatologic and/or respiratory disease.
• the therapeutic treatment protocol may be influenced by the stage or severity of the dermatologic and/or respiratory disease state, which is indicated by or correlated to the risk score.
• the dermatologic and/or respiratory disease states are selected from the group consisting of asthma, atopic dermatitis, flea allergic dermatitis, environmental allergic dermatitis, and food allergic dermatitis.
• the risk score may incorporate or correlate to approximately three (3) risk assessment categories based on the risk/probability score generated: a 0.0 - 0.33 bracket is classified as Tow risk’ of having a dermatologic condition; >0.33 - 0.66 is classified as ‘medium risk’ for having a dermatologic condition; and >0.66 - 1.0 is classified as ‘high risk’ for having a dermatologic condition.
• a risk score of 0.34 would meet the threshold for categorizing a cat as being at medium risk for having a dermatologic condition.
• the granularity of the risk score and/or the number of categories may change as more data is added to the systems and methods.
• the therapeutic treatment or at-home care protocol can alter the composition of the oral microbiome of the cat directly or as a byproduct of the treatment of a specific dermatologic and/or respiratory disease.
• altering the composition of the cat’s oral microbiome treats and/or addresses the specific dermatologic and/or respiratory disease.
• the therapeutic treatment repairs the cat’s oral microbiome.
• repairing the cat’s oral microbiome brings the cat’s oral microbiome more in line with the oral microbiome (or defined oral microbial profile) of a healthy cat - both in terms of the specific one or more microbial species present and their relative abundance.
• the therapeutic treatment or at-home care protocol is designed to maintain the composition of the oral microbiome of the cat.
• the therapeutic treatment protocol is designed to stimulate a metabolic output of the cat’s oral microbiome. Stimulating a metabolic output of the cat’s oral microbiome may include using known enzymatic pathway analysis tools to provide an additional dimension to the existing microbial composition data to further characterize disease signatures and improve predictive disease models.
• Illustrative embodiments and non-limiting examples of the present disclosure include:
• Example 1 A method for screening for, detecting, and/or preventing dermatologic and/or respiratory disease in domestic cats, the method comprising: obtaining an oral microbial profile for a cat, the oral microbial profile comprising one or more microbial species present in an oral sample from the cat and a quantity or abundance of the one or more microbial species in the oral sample; comparing the oral microbial profile to information in a database that identifies weighted correlations between:
• Example 2 The method of Example 1 further comprising administering the therapeutic treatment protocol to the cat or confirming that the therapeutic treatment protocol has been administered to the cat, wherein the therapeutic treatment protocol is sufficient to alter the oral microbial profile of the cat.
• Example 3 The method of Example 1, wherein obtaining the oral microbial profile for the cat comprises: obtaining nucleic acid sequence data corresponding to microbial nucleic acid obtained from the oral sample; analyzing the nucleic acid sequence data to identify the one or more microbial species present in the oral sample and, optionally, quantifying the one or more microbial species; and generating the oral microbial profile for the cat based on the identified and, optionally, quantified one or more microbial species.
• Example 4 The method of Example 3, wherein obtaining the microbial nucleic acid sequence data comprises: sequencing microbial nucleic acid from the oral sample; and, optionally, isolating the microbial nucleic acid from the oral sample.
• Example 5 The method of Example 4, wherein isolating the microbial nucleic acid from the oral sample comprises: performing heat treatment on the oral sample; and performing magnetic SPRI beads-based nucleic acid extraction on the heat-treated oral sample, with or without the addition of protein digesting reagents and detergents, to extract the microbial nucleic acid from the oral sample.
• Example 6 The method of Example 3, wherein analyzing the microbial nucleic acid sequence data comprises one or more of: demultiplexing the nucleic acid sequence data; trimming the nucleic acid sequence data; mapping one or more unmapped reads onto a reference genome of the cat and/or onto existing microbial reference genomes; classifying one or more reads as feline from the nucleic acid sequence data after mapping; classifying one or more reads as microbial from the nucleic acid sequence data after mapping; quantifying the one or more microbial reads; transforming the quantified one or more microbial reads to account for sequence coverage biases using methods such as pairwise log ratio transformation; and comparing compositional abundance patterns in the transformed one or more microbial reads against compositional abundance patterns in transformed data in a reference database comprising samples from cats that do not suffer from dermatologic and/or respiratory diseases, as well as samples from cats that suffer from specific dermatologic and/or respiratory diseases.
• Example 7 The method of Example 1, wherein comparing the oral microbial profile for the cat to the information in the database comprises one or more of: calculating the abundance of the one or more microbial species in the oral sample; identifying the one or more microbial species in the oral sample; and comparing the abundance of the identified one or more microbial species in the oral sample to the presence and/or abundance of various microbial species in the oral microbiome of cats contained in the database.
• Example 8 The method of Example 1, wherein generating the risk score comprises one or more of: identifying one or more similarities between compositional abundance(s) of the one or more microbial species in the oral sample and compositional abundance(s) of various microbial species in the oral microbiomes of cats contained in the database; identifying one or more matches between the identities of the one or more microbial species in the oral sample and the presence of various microbial species in the oral microbiome of cats contained in the database; quantifying the identified one or more similarities between the compositional abundance(s) of the one or more microbial species in the oral sample and the compositional abundance(s) of the one or more microbial species in the oral microbiomes of cats contained in the database; and identifying a presence of one or more predictive microbial species in the oral sample.
• Example 9 The method of Example 1, wherein the one or more dermatologic and/or respiratory diseases are selected from the group consisting of asthma, atopic dermatitis, food allergic dermatitis, flea allergic dermatitis and environmental allergic derma
• Example 10 The method of Example 1 further comprising: generating a report presenting (i) the risk score, (ii) an indication of developing the one or more dermatologic and/or respiratory diseases when the risk score meets or exceeds the predetermined threshold, (iii) a timing recommendation, (iv) optionally, one or more at home practices to improve dermatologic and/or respiratory health, (v) optionally, one or more diagnostic steps to diagnose the one or more dermatologic and/or respiratory diseases when the risk score meets or exceeds the predetermined threshold, and (vi) optionally, a prescription for the therapeutic treatment protocol; and, optionally, communicating the generated report electronically to an owner of the cat and/or their veterinarian.
• Example 11 The method of Example 1 , wherein the therapeutic treatment protocol is sufficient to alter the oral microbial profile of the cat.
• Example 12 A computer system configured to indicate or predict dermatologic and/or respiratory disease in cats, the computer system comprising: one or more processors; and one or more computer-readable hardware storage devices having stored thereon instructions that are executable by the one or more processors to configure the computer system to: receive microbial nucleic acid sequence data corresponding to microbial nucleic acid obtained from an oral sample taken from a cat; analyze the microbial nucleic acid sequence data to identify one or more microbial species present in the oral sample and quantify the one or more microbial species; generate an oral microbial profile for the cat based on the identified one or more microbial species and their respective abundances; compare the oral microbial profile to information in a database that identifies weighted correlations between:
• Example 13 The computer system of Example 12, wherein the instructions further configure the computer system to analyze metagenomic sequence data from the oral sample and map one or more unmapped sequence reads to a feline reference genome and/or map one or more sequence reads to microbial reference genomes and, optionally, classify the reads as microbial or feline.
• Example 14 The computer system of Example 13, wherein the instructions further configure the computer system to identify at least one unmapped sequence read of the metagenomic sequence data and, optionally, classify the at least one unmapped read.
• Example 15 The computer system of Example 13, wherein feline oral microbiome samples having fewer than 10,000 classified microbial reads or more than 500,000 classified microbial reads are excluded from the comparison of the oral microbial profile for the cat against a database of defined microbial profiles.
• Example 16 The computer system of Example 12, wherein the instructions further configure the computer system to calculate an abundance of the one or more microbial species present in the oral sample.
• Example 17 The computer system of Example 16, wherein the abundance of the specific one or more microbial species present in the oral sample correlates to whether the specific one or more microbial species is a predictive microbial species for the specific dermatologic and/or respiratory disease.
• Example 18 The computer system of Example 16, wherein the instructions further configure the computer system to perform a pairwise log ratio comparison of the microbial abundance of the oral sample against the information in the database.
• Example 19 The system of Example 18, wherein the specific one or more microbial species is a predictive microbial species when 50% or more of the maximum possible pairwise log ratio comparisons involving this microbe are significantly different when compared between a disease and a control cohort.
• Example 20 A method for predicting the development of a dermatologic and/or respiratory disease in a cat, the method comprising: obtaining an oral sample from a cat, the oral sample comprising one or more microbial species; isolating, from the oral sample, microbial nucleic acid of the one or more microbial species; obtaining microbial nucleic acid sequence data corresponding to the microbial nucleic acid; analyzing the microbial nucleic acid sequence data to identify the one or more microbial species present in the oral sample and, optionally, quantifying the one or more microbial species; generating an oral microbial profile for the cat based on the identified and, optionally, quantified one or more microbial species, the oral microbial profile comprising the one or more microbial species and, optionally, a quantity or relative abundance of the one or more microbial species in the oral sample; comparing the oral microbial profile to information in a database that identifies weighted correlations between:
• Example 21 A method for diagnosing a dermatologic and/or respiratory disease in a cat, the method comprising: obtaining an oral sample from a cat, the oral sample comprising one or more microbial species; isolating, from the oral sample, microbial nucleic acid of the one or more microbial species; obtaining microbial nucleic acid sequence data corresponding to the microbial nucleic acid; analyzing the microbial nucleic acid sequence data to identify the one or more microbial species present in the oral sample and, optionally, quantifying the one or more microbial species; generating an oral microbial profile for the cat based on the identified and, optionally, quantified one or more microbial species, the oral microbial profile comprising the one or more microbial species and, optionally, a quantity or relative abundance of the one or more microbial species in the oral sample; comparing the oral microbial profile to information in a database that identifies weighted correlations between:
• Example 22 A method for treating a dermatologic or respiratory disease in a cat, the method comprising: obtaining an oral sample from a cat, the oral sample comprising one or more microbial species; isolating, from the oral sample, microbial nucleic acid of the one or more microbial species; obtaining microbial nucleic acid sequence data corresponding to the microbial nucleic acid; analyzing the microbial nucleic acid sequence data to identify the one or more microbial species present in the oral sample and, optionally, quantifying the one or more microbial species; generating an oral microbial profile for the cat based on the identified and, optionally, quantified one or more microbial species, the oral microbial profile comprising the one or more microbial species and, optionally, a quantity or relative abundance of the one or more microbial species in the oral sample; comparing the oral microbial profile to information in a database that identifies weighted correlations between:
• Example 23 The method of Example 22, wherein the dermatologic or respiratory disease is selected from the group consisting of asthma, atopic dermatitis, food allergic dermatitis, flea allergic dermatitis and environmental allergic dermatitis.
• Figures 1A-1B illustrate a dermatologic health test workflow and oral microbiome reference database construction.
• Figures 2A-2D illustrate a distribution of the average log ratio difference scores between pairwise microbial interactions associated with healthy cohorts and (A) atopic dermatitis, (B) food allergic dermatitis, (C) flea allergic dermatitis, (D) environmental allergic dermatitis.
• Figures 3A-3D illustrate sensitivity and specificity of the feline dermatologic health test based on a 2-component Gaussian mixture model.
• Sensitivity refers to the ability of the disclosed embodiments to detect cats known to suffer from a dermatologic condition.
• Specificity refers to the ability of the disclosed embodiments to detect cats in the healthy control cohorts as not suffering from a dermatologic condition.
• Figure 4 illustrates overlap of oral microbiome predictive microbes characteristic of feline atopic dermatitis, food allergic dermatitis, flea allergic dermatitis or environmental allergic dermatitis.
• Figure 5 illustrates microbial species richness as a function of number of sequencing reads, comparing data from two different types of metagenomic whole genome sequencing
• Figure 6 illustrates a distribution of the average log ratio difference scores between pairwise microbial interactions associated with healthy cohorts and asthma cohorts.
• Figure 7 illustrates sensitivity and specificity of the feline health test for asthma and healthy cohorts based on a 2-component Gaussian mixture model.
• Variations in the microbial composition of the mouth may have associations with certain dental and systemic diseases. This research area is still young and studies on human subjects demonstrating these associations in a comprehensive manner have only been published in the last decade or less. Studies on this topic in companion animals, such as cats and dogs, have been limited. Nutritional and environmental factors, as well as present disease states, may play an important role in the dynamic microbial composition of a cat’s mouth
• the oral microbiome has evolved to be competitive and territorial. It is comprised of microbes that excel at defending their territory and typically avoid being replaced by foreign invaders, including pathogens.
• dysbiosis inducing events such as poor diet, poor dental hygiene, the onset of systemic diseases or environmental changes, can lead to pathogenic microbes colonizing disproportionately large parts of the oral cavity (and, thus, altering the oral microbiome). This colonization can be associated with pathological disease states.
• Understanding the composition of the oral microbiome can provide information not only about the health of the oral tissues, but also about the general health of the animal or human.
• oral microbiome characteristics have been linked with diseases such as inflammatory bowel disease (IBD), various cancers, and atopic dermatitis, among others.
• Oral microbiome characteristics may also be linked with asthma, another inflammatory and allergenic condition.
• the information provided by the state of the oral microbiome may also be used to manage the health and wellbeing of a pet.
• Interrogating the oral microbiome of a cat can be accomplished by using an oral (saliva) sample.
• Saliva sampling kits have gained popularity in recent years as tests for ancestry and microbial infection have become more prevalent.
• Available direct-to-consumer microbiome tests typically rely on a technique called ‘16S rRNA gene sequencing,’ which utilizes Next Generation Sequencing (NGS). While this technique provides substantially more information than early bacterial culturing efforts, it can only be used for identifying bacterial species (and some archaea) present in the microbiome. In most cases, these tests do not provide sufficient resolution to reliably, and consistently, identify bacteria beyond the genus level of taxonomic classification.
• NGS Next Generation Sequencing
• test results do not provide the exact species or strain of bacteria comprising the microbiome.
• data-driven conclusions using these results are vague and rely on approximation.
• the microbiomes of different sites of the body can be composed of viruses, protozoa, and fungal species, in addition to bacteria and archaea. This means that the 16S rRNA gene sequencing approach zooms in on just one part of the microbiome, ignoring the rest.
• Embodiments of the disclosed subject matter describe a method for interrogating the oral microbiome of a domestic cat for the purpose of detecting microbe compositional abundance trends associated with dermatologic and/or respiratory diseases in cats. Detecting, identifying and/or quantifying microbe compositional abundance trends enables a practitioner to screen for and/or indicate whether a cat has a particular dermatologic and/or respiratory disease state.
• Disclosed methods may compare, for example, a cat’s oral microbiome to the oral microbiomes of cats reported by their owners and/or a veterinary professional to have been diagnosed with asthma, atopic dermatitis, food allergic dermatitis, flea allergic dermatitis, or environmental allergic dermatitis. The comparison is carried out using a reference database containing defined microbial profiles, associating one or more microbial species and their respective compositional abundance(s) with one or more dermatologic conditions.
• Disclosed systems and methods can comprise a painless oral swab sample collection. Accordingly, the oral microbiome can be surveyed via buccal, supragingival or subgingival sampling. Such sampling does not require anesthetizing the animal and can be performed by the pet owner at their home or by the veterinarian at the clinic.
• the disclosed systems and methods can potentially serve as an early indicator of dermatologic disease-associated processes not yet formally diagnosed. Routine use may enable identification of early-stage dermatologic diseases, driving more cats to the veterinary office early on and reducing animal suffering. Earlier identification of dermatologic disease states beneficially saves costs and improves the quality of life of cats. Earlier identification of dermatologic and respiratory disease states also means more treatment options are available when the dermatologic and respiratory disease is diagnosed and identified.
• the oral microbiome With the mouth being the first line of defense from constant exposure to foreign microbes, the oral microbiome has evolved to be competitive and territorial. It is comprised of microbes that excel at defending their territory and are typically able to resist being replaced by foreign invaders, including pathogens. These microbes are generally present when a cat is healthy and would represent a healthy microbial profile of the oral microbiome.
• the composition of the oral microbiome may be altered by the presence of foreign or pathogenic microbial species and/or altered abundance ratios of and between different microbes. Such an alteration in the composition of the oral microbiome might be represented by a pathogenic profile. In some cases, the presence of particular foreign and/or pathogenic microbial species, and their abundance relative to other microbes in the oral cavity, is correlated with the cat suffering from a particular dermatologic and/or respiratory condition.
• Identification of the particular (one or more) microbial species (and their respective relative abundance(s)) correlated with particular dermatologic and/or respiratory disease states enables pre-diagnostic screening for the dermatologic and/or respiratory disease state in a cat exhibiting the presence of the identified (one or more) microbial species.
• identification and/or indication of the dermatologic and/or respiratory disease state may be correlated to the cat exhibiting a particular pathogenic profile.
• the gold standard for the comprehensive study of the microbiome is shotgun metagenomic sequencing, which allows capturing complete or near-complete genomes of organisms across all domains of life, not just bacteria and archaea.
• the gold standard for metagenomic DNA extraction includes a process called bead-beating.
• the disclosed systems and methods do not use bead-beating for metagenomic DNA extraction and purposefully abandon such a process. The reason for this is that bead-beating can also introduce significant DNA degradation that interferes with downstream sample processing and can therefore lower the quality of the generated metagenomic sequencing library.
• the disclosed systems and methods do not use bead-beating, it is likely that the oral microbiome data in the resulting analyses suffer from under-representation of gram-positive bacteria. Nonetheless, it allows the recognition of disease-characteristic patterns.
• the disclosed systems and methods also enable microbial identification and classification down to the species or, in some instances, the strain level, unlike 16S rRNA gene sequencing.
• the reference database is a weighted correlation database and contains at least the identified 8,344 microbial species present in the feline oral microbiome. On average, 606 microbial species per cat were identified, 97% of which were classified as bacteria and archaea, 0.27% as DNA viruses (RNA viruses cannot be detected with shotgun metagenomic sequencing), 0.02% as phages and ⁇ 2% as fungi.
• the various microbial species identified as being involved in and contributing to a specific dermatologic or respiratory disease are compiled into a “defined microbial profile.”
• the defined microbial profile is a list or collection of identified one or more microbial species, and their respective relative abundances, known to contribute to and/or be involved in a specific dermatologic or respiratory disease condition.
• defined microbial profiles may include percentages of gram-positive microbes and ratios of gram-positive microbes to gram negative microbes, in addition to the identities (i.e., genus and species) of microbes. In some embodiments, defined microbial profiles may indicate the relative abundance (increased or decreased) of the one or more microbial species. (See Tables 1-16 below).
• a defined microbial profile may include a set of seventy (70) microbes that are predictive for four dermatologic conditions (atopic dermatitis, food allergic dermatitis, flea allergic dermatitis, or environmental allergic dermatitis), as well as microbes specifically predictive for one of the four dermatologic conditions (atopic dermatitis, food allergic dermatitis, flea allergic dermatitis, environmental allergic dermatitis).
• a defined microbial profile may also be compiled for respiratory conditions, such as asthma. “Predictive microbes” are discussed more fully below.
• the defined microbial profile may rank and/or weigh each included microbial species by how frequently and in what proportions a certain microbe is observed in animals suffering from the specific dermatologic or respiratory condition, as deduced by consulting a reference database. How much any one microbial species contributes to a specific dermatologic and/or respiratory disease condition is correlated to how often a microbial species shows up (or is present) in the oral microbiome while an animal is suffering from a specific dermatologic and/or respiratory disease condition. How much any one microbial species contributes to a specific dermatologic and/or respiratory disease condition also correlates to how consistently such microbial species demonstrates significantly different relative abundances from other oral microbes when compared to healthy control samples.
• the defined microbial profiles contained in the reference database also include defined microbial profiles of healthy cats that are not suffering from a dermatologic and/or respiratory condition.
• the defined microbial profile of healthy cats lists and identifies the microbial species present in the oral microbiome, as well as their relative abundances, when no dermatologic and/or respiratory condition is present.
• a healthy defined microbial profile may establish a baseline or control for the microbial species present and their relative abundance. Any deviations from this profile may enable a practitioner to predict and/or indicate, for example, a cat’s likelihood of suffering from a dermatologic and/or respiratory condition.
• deviations from the healthy defined microbial profile may enable a practitioner in diagnosing a cat as suffering from a dermatologic and/or respiratory condition prior to the onset of symptoms for that dermatologic and/or respiratory condition.
• the defined microbial profile for each dermatologic and/or respiratory disease state is compared to the defined microbial profile for a healthy cat to determine any differences between the dermatologic and/or respiratory disease states and a healthy state.
• the comparisons are pairwise log ratio comparisons. For example, there may be some overlap in the oral microbiome of a healthy cat and a cat suffering from atopic dermatitis.
• a comparison of the healthy defined microbial profile to the atopic dermatitis defined microbial profile would identify common microbial species seen in similar abundances between the two. Any microbial species not common between the two microbial profiles, or any microbial species seen in significantly different proportions between the two profiles, would confirm the involvement of that microbial species in the development of atopic dermatitis. Identification of such a microbial species in a cat’s oral microbiome would be indicative of the cat having atopic dermatitis.
• Figures 1 A-1B illustrate a dermatologic health test workflow and construction of the oral microbiome reference database using feline subjects.
• the feline dermatologic health test workflow includes collecting an oral swab from the cat in a DNA preservation solution, extracting and preparing the DNA for shotgun metagenomic next generation sequencing (NGS), sequencing the DNA, data analysis and the generation of a report presenting risk assessment for different dermatologic diseases based on the state of the oral microbiome. The report may be accompanied by treatment recommendations tailored to the results.
• the feline oral microbiome reference database was constructed through applying sequential filters on the initial database of 38,000 cats. First, all data from tagmentation-based NGS library preparation samples was removed.
• Figures 1A-1B illustrate a dermatologic health test workflow and construction or the oral microbiome reference database
• the same health test workflow was performed for respiratory conditions (e.g., asthma).
• respiratory conditions e.g., asthma
• Use of the oral microbiome reference database in conjunction with the disclosed computer systems, systems and methods enables a practitioner to screen for, indicate, identify, diagnose, and/or treat disease states in cats.
• the disease states include, at least, AD, FAD, FLAD, EAD, and asthma.
• Identifying Predictive Microbes As a first step towards identifying microbes significantly correlated with each dermatologic and/or respiratory condition, Pairwise Log-Ratio (PLR) transformation was performed on the Bracken output species level read counts. Next, the significant PLR comparisons (p-value ⁇ 0.01) were identified between the control and a condition by performing a z-test. The healthy cohort was compared to the AD, FAD, FLAD and EAD cohorts. A healthy cohort was also compared to an asthmatic cohort. (See Figures 6-7).
• PLR Log-Ratio
• each sample was scored by comparing the predictive pairwise log-ratios (pPLRs) of the sample to the mean pPLRs of controls, taking into account the direction and magnitude of the difference.
• Figures 2A-2D illustrate a distribution of the average log ratio difference scores between pairwise microbial interactions associated with healthy cohorts and atopic dermatitis, food allergic dermatitis, flea allergic, environmental allergic dermatitis.
• Figure 6 illustrates a distribution of the average log ratio difference score between pairwise microbial interactions associated with healthy cohorts and asthmatic cohorts.
• the defined microbial profile for each dermatologic and/or respiratory disease state is compared to the defined microbial profile for a healthy cat to determine and quantify differences and commonalities in microbial species and their abundance between the dermatologic disease states and a healthy state.
• the defined microbial profiles for each dermatologic disease state are also compared to each other to identify overlapping microbial species common to each dermatologic disease state.
• the defined microbial profile for asthma underwent similar comparisons to determine and quantify differences and commonalities in microbial species and their abundance between asthma and a healthy state, as well as to identify overlapping microbial species common to each disease state.
• the defined microbial profiles for each dermatologic or respiratory disease state and a healthy control state undergo a pairwise log ratio (PLR) transformation.
• the PLR transformation corrects for potential sequencing coverage differences between samples by scaling microbial abundances relative to each microbe instead of a constant scaling factor.
• a z-test between PLRs from each disease state versus the control state is performed.
• a p-value of approximately ⁇ 0.01 serves as a threshold value for significant PLR comparisons.
• the number of significant PLR comparisons (as defined by the p-value) that microbial species shows up in is counted.
• the microbial species is deemed a “predictive microbe.” This process may be repeated for each dermatologic or respiratory disease state of interest.
• predictive microbes can be identified for asthma, atopic dermatitis, food allergic dermatitis, flea allergic and/or environmental allergic dermatitis.
• Table 1 provides examples of identified predictive microbes for atopic dermatitis, food allergic dermatitis, flea allergic and environmental allergic dermatitis.
• Table 2 provides examples of identified predictive microbes for asthma.
• 86 predictive microbes for atopic dermatitis, 122 for food allergic dermatitis, 99 for flea allergic dermatitis, and 110 for environmental allergic dermatitis were identified.
• the predictive microbes for each dermatologic disease were identified based on PLR microbial abundance comparisons between healthy/control defined microbial profiles and the defined microbial profiles of cats suffering from one of four dermatologic conditions (See Figure 4). Seventy (70) microbes were identified as predictive for the four dermatologic conditions (atopic dermatitis, food allergic dermatitis, flea allergic and environmental allergic dermatitis), though each condition has its own specific set of predictive microbes, differentiating it from other conditions.
• Tables 8-12 outline the relative increased or decreased abundance for each predictive microbe for each dermatologic and/or respiratory disease state of interest. This data (regarding relative abundances, percentages, and ratios of gram-positive bacteria present) may also be included in the defined microbial profiles for each dermatologic and/or respiratory disease state. Detection of one or more gram-positive bacteria (or, obtaining a ratio or percentage of one or more of these gram-positive bacteria) in the oral microbiome of a cat may enable the systems and methods to indicate or diagnosis the cat as suffering from a dermatologic or respiratory disease. [0054] The same process (comparison to the healthy cohort, PLR transformations, z-test, etc.) was performed for the asthma cohort.
• Figure 6 illustrates a distribution of the average log ratio difference scores between pairwise microbial interactions associated with healthy cohorts and the asthmatic cohort.
• Figure 7 illustrates sensitivity and specificity of the feline respiratory health test based on a 2-component Gaussian mixture model.
• predictive is not meant to be interpreted as ‘causative’, it simply reflects the fact that a microbe has a significantly different compositional abundance in a particular dermatologic or respiratory condition compared to control. This could either mean that the microbe has an active role in the disease’s pathology or that the changes of its compositional abundance are a byproduct of pathology. In either scenario, presence of the microbe in a specific abundance relative to other microbes directly correlates with a dermatologic or respiratory disease state.
• the algorithms and disclosed methods of identifying predictive microbes may be continually evolving.
• a set or grouping of identified predictive microbes may slightly change and evolve as the populations of the cohorts (healthy cats and cats suffering from dermatologic conditions) change and evolve.
• the set of identified predictive microbes will change and evolve.
• the new set of identified predictive microbes may not be 100% different from the initial set, rather a variance of approximately 25% to 85% may be expected.
• the new set of identified predictive microbes may be 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, or 80% different from the initial set of identified predictive microbes, or a variance defined by any two of the foregoing values. As more cats are added to the cohorts, the set of identified predictive microbes will change and evolve.
• At least one oral swab of a cat may be taken to provide a sample for testing.
• the oral swabs may target the gum lines of the animal (top and bottom) and/or target the entire mouth of the animal.
• Microbial DNA may be extracted from the oral swab samples in order to identify which microbial species, and in what relative abundance, are present in the cat’s oral microbiome.
• Metagenomic DNA may be extracted from the oral samples via heat treatment for approximately one hour on a shaker, with or without bead-beating or the addition of detergents and protein degradation reagents such as proteinase K.
• the oral samples are heat treated at approximately 45°C to 75°C, such as 50°C, 55°C, 60°C, 65°C, 70°C or within a range defined by any two of the foregoing values.
• the gold standard for the comprehensive study of the microbiome is shotgun metagenomic sequencing, which allows capturing complete or near-complete genomes of organisms across all domains of life, not just bacteria and archaea.
• the gold standard for metagenomic DNA extraction includes a process called bead-beating. It is recommended for complete microbial cell lysis when studying the abundance and composition of the microbiome. The process helps break apart thicker cell walls, such as those of gram-positive bacteria. It is achieved by rapidly agitating samples with grinding media (balls or beads) in a bead beater.
• the disclosed systems and methods do not use bead-beating for metagenomic DNA extraction and purposefully abandon such a process.
• bead-beating can also introduce significant DNA degradation that interferes with downstream sample processing and can therefore lower the quality of the generated metagenomic sequencing library. Since the disclosed systems and methods, according to one embodiment, do not use bead-beating, it is likely that the oral microbiome data in the resulting analyses suffers from under-representation of gram-positive bacteria. Nonetheless, it enables the recognition of disease-characteristic patterns.
• metagenomic DNA may be extracted by SPRI magnetic beads-based DNA extraction (MCLAB, MBC-200) using 80% ethanol for purification.
• the DNA may be quantified using a GloMax Plate Reader (Promega).
• the oral samples may be prepared for NGS using the LOTUS DNA library prep kit (IDT), the Next Ultra II FS DNA library prep kit (NEB), or another ligation or tagmentation based DNA library prep kit, following the manufacturer’s instructions.
• the oral samples may be dual-barcoded with iTRU indices.
• the prepared sequencing libraries may be quantified using a GloMax Plate Reader (Promega) and equal-mass pooled into 96-sample pools.
• the pools may then be visualized (to assess fragment size distribution) and quantified using a 2100 Bioanalyzer instrument (Agilent). Following standard QC steps, the 96-sample pools may be loaded onto an Illumina HiSeq X or NovaSeq 6000 Next Generation Sequencing machine. [0062] The raw sequencing data may be demultiplexed and trimmed to remove low-quality data using, for example, the program Trimmomatic 0.32. The data may then be mapped to the latest version of, for example, the feline genome Felis_catus_9.0. For every oral sample, there may be approximately 5-7% sequencing reads that do not map to the feline genome.
• the unmapped reads may be classified using the KRAKEN2 metagenomic sequence classifier (or a suitable alternative) to identify the microbial organisms present in each sample.
• Bracken a statistical method for calculating species abundance in DNA sequencing data from a metagenomic sample, may be used on the sequenced data in conjunction with the KRAKEN2 analysis. Bracken may output species level read counts.
• an oral microbial profile for the cat may be generated.
• the oral microbial profile generated may include data regarding the identity of the microbial species present as well as their relative abundance.
• the oral microbial profile generated may also include data regarding the relative abundance (increased or decreased) of the microbial species present, as well as a percentage of gram-positive bacteria present.
• a confidence score of approximately 0.1 may be used as a cutoff (or threshold value) for the KRAKEN2 classification algorithm. All samples with fewer than 10,000 classified microbial reads or more than 500,000 classified microbial reads may be filtered out. The reads for microbial species with a non-zero mean of fewer than 10 reads may also be filtered out.
• Indication of whether a cat is suffering from a dermatologic or respiratory disease relies on a comparison of the cat’s current oral microbiome state to the oral microbiomes of cats reported by their pet owners to have been diagnosed by a veterinarian with asthma, atopic dermatitis, food allergic dermatitis, flea allergic dermatitis, or environmental allergic dermatitis.
• the comparison is based on the compositional abundance of microbes determined by the analysis to be predictive of each of the dermatologic or respiratory conditions.
• Computational analysis of the compositional abundance of different microbes present in the oral microbiome involves comparison of the oral sample against a database of samples from cats known to suffer from different dermatologic or respiratory conditions, as well as cats who do not suffer from any known dermatologic or respiratory conditions.
• the computational analysis compares the oral microbiome identified/obtained from the oral swab sample to the defined microbial profiles contained in the reference database (discussed more fully above).
• a method for indicating dermatologic or respiratory disease in cats includes receiving an oral swab sample taken from a cat; performing heat treatment on the oral sample; and performing magnetic beads-based deoxyribonucleic acid (DNA) extraction on the heat-treated oral sample to extract microbial DNA that is present in the oral swab sample.
• DNA deoxyribonucleic acid
• the method may also include sequencing the microbial DNA to identify which specific one or more microbes are present in the oral sample and in what proportions (i.e., abundance), wherein identifying the specific one or more microbes and their abundances results in generation of an oral microbial profile for the cat; and comparing the oral microbial profile for the cat against a database of defined microbial profiles, wherein the database identifies correlations between (i) profiles that include one or more microbes, and (ii) corresponding dermatologic or respiratory diseases.
• the method may further include generating a risk score indicating a likelihood that the cat has a specific dermatologic or respiratory disease.
• the risk score may be correlated to a stage or severity of the disease state.
• a method for indicating dermatologic or respiratory disease in cats includes receiving an oral swab sample taken from a cat; performing heat treatment on the oral sample; and performing magnetic beads-based deoxyribonucleic acid (DNA) extraction on the heat-treated oral sample to extract microbial DNA that is present in the oral swab sample.
• the method may also include sequencing the microbial DNA to identify which specific one or more microbes are present in the oral sample, wherein identifying the specific one or more microbes and their abundance results in generation of an oral microbial profile for the cat.
• the method may further include comparing the oral microbial profile for the cat against a database of defined microbial profiles, wherein the database identifies correlations between (i) profiles that include one or more microbes, and (ii) corresponding dermatologic or respiratory diseases; based on a result of comparing the oral microbial profile against the database of defined microbial profiles, generating a risk score indicating a likelihood that the cat has a specific dermatologic or respiratory disease; and in response to generating the risk score and identifying the specific dermatologic or respiratory disease, administering a therapeutic treatment designed to treat the specific dermatologic or respiratory disease.
• the therapeutic treatment may be an at-home protocol.
• the therapeutic treatment may include administering a therapeutic compound, such as a compound designed to inhibit or encourage growth of a specific one or more microbial species present in the oral microbiome of the cat.
• a therapeutic compound such as a compound designed to inhibit or encourage growth of a specific one or more microbial species present in the oral microbiome of the cat.
• the therapeutic compound includes a pre-biotic, a post-biotic, a pro-biotic, a medicament or a combination thereof.
• the therapeutic compound may include an antibiotic, a corticosteroid, a bronchodilator, or a combination thereof.
• the therapeutic treatment includes a topical treatment or bath to help alleviate itching and promote healing of skin.
• the therapeutic treatment or at-home care protocol can alter the composition of the oral microbiome of the cat directly or as a byproduct of the treatment of a specific dermatologic or respiratory disease.
• altering the composition of the cat’s oral microbiome treats and/or addresses the specific dermatologic or respiratory disease.
• the therapeutic treatment repairs the cat’s oral microbiome.
• repairing the cat’s oral microbiome brings the cat’s oral microbiome more in line with the oral microbiome (or defined oral microbial profile) of a healthy cat - both in terms of the specific one or more microbial species present and their relative abundance.
• the therapeutic treatment protocol is designed to maintain the composition of the oral microbiome of the cat. In some embodiments, the therapeutic treatment protocol is designed to stimulate a metabolic output of the cat’s oral microbiome. Stimulating a metabolic output of the cat’s oral microbiome may include using known enzymatic pathway analysis tools to provide an additional dimension to the existing microbial composition data to further characterize disease signatures and improve predictive disease models.
• PLR Log-Ratio
• the healthy cohort was compared to the AD, FAD, FLAD and EAD cohorts.
• the healthy cohort was also compared to the asthma cohort. (See Figures 6-7).
• the frequency of each microbial species in all significant PLRs was assessed. Only microbial species where 50% or more of their maximum possible comparisons with other species were significant were kept. This measure was used as a proxy for the importance of different microbial species in the four dermatologic and one respiratory condition of interest. These microbial species are “predictive microbial species” for each dermatologic or respiratory condition.
• each sample was scored by comparing the predictive pairwise log-ratios (pPLRs) of the sample to the mean pPLRs of controls, taking into account the direction and magnitude of the difference.
• Figures 2A-2D illustrate a distribution of the average log ratio difference scores between pairwise microbial interactions associated with atopic dermatitis and healthy controls, food allergic dermatitis and healthy controls, flea allergic dermatitis and healthy controls, and environmental allergic dermatitis and healthy controls.
• Figure 6 illustrates a distribution of the average log ratio difference scores between pairwise microbial interactions associated with asthma and healthy cohorts.
• Figure 7 plots the probability that samples belong to the respiratory disease cohort and the control samples would be classified as belonging to their respective cohorts based on each sample’s compositional abundance of predictive microbes.
• the sensitivity (ability to detect cats known to suffer from a dermatologic or respiratory condition) and specificity (ability to detect cats in the control cohort as not suffering from a dermatologic or respiratory condition) of the risk classification method for each dermatologic and respiratory condition was tested (see Figures 3A-3D and 7).
• the method’s sensitivity is highest for flea allergic dermatitis and lowest for environmental allergic dermatitis, while the specificity is highest for asthma and atopic dermatitis, and lowest for food allergic dermatitis.
• age of the cat is included as a factor in identifying the cat’s risk for having or developing a dermatologic or respiratory disease condition.
• age may impact the grouping of the cohorts, with older cats being in a separate cohort from younger cats, even for the same dermatologic or respiratory condition.
• age is a factor applied to a cat’s risk assessment after comparison of the cat’s oral microbial profile to the cohorts (healthy and pathological).
• age is incorporated into the oral microbial profile obtained and generated for the cat.
• microbes identified as associated with or predictive for a dermatologic condition may be further predictive for stages or grades of the dermatologic or respiratory condition.
• the risk score generation methodology disclosed herein is based on oral microbiome compositional analysis.
• Other embodiments of the disclosed methods may also include incorporating predictions of the metabolic output of the oral microbiome (generated by enzymatic pathway analysis tools or metabolomics), alongside the oral microbiome compositional abundance analysis for the purpose of predictive risk of dermatologic or respiratory conditions.
• the words “can” and “may” are used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must).
• the terms “including,” “having,” “involving,” “containing,” “characterized by,” variants thereof (e.g., “includes,” “has,” and “involves,” “contains,” etc.), and similar terms as used herein, including the claims, shall be inclusive and/or open-ended, shall have the same meaning as the word “comprising” and variants thereof (e.g., “comprise” and “comprises”), and do not exclude additional, un-recited elements or method steps, illustratively.
• condition refers to any disorder, disease, injury, or illness, as understood by those skilled in the art, that is manifested or anticipated in a patient. Manifestation of such a condition can be an early, middle, or late stage manifestation, as known in the art, including pre condition symptoms, signs, or markers. Anticipation of such a condition can be or include the predicted, expected, envisioned, presumed, supposed, and/or speculated occurrence of the same, whether founded in scientific or medical evidence, risk assessment, or mere apprehension or trepidation.
• patient is synonymous with the term “subject” and generally refers to any animal under the care of a medical professional, as that term is defined herein, with particular reference to (i) humans (under the care of a doctor, nurse, or medical assistant or volunteer) and (ii) non-human animals, such as non-human mammals (under the care of a veterinarian or other veterinary professional, assistant, or volunteer).
• Treating” or “treatment” as used herein covers the treatment of the disease or condition of interest in a cat, having the disease or condition of interest, and includes: (i) preventing the disease or condition from occurring in a cat, in particular, when such cat is actually starting to develop the condition but has not yet been diagnosed as having it; (ii) inhibiting the disease or condition, i.e., arresting its development; (iii) relieving the disease or condition, i.e., causing regression of the disease or condition; or (iv) relieving the symptoms resulting from the disease or condition, i.e., relieving pain without addressing the underlying disease or condition.
• the terms “disease” and “condition” may be used interchangeably or may be different in that the particular malady or condition may not have a known causative agent (so that etiology has not yet been worked out) and it is therefore not yet recognized as a disease but only as an undesirable condition or syndrome, wherein a more or less specific set of symptoms have been identified by clinicians.
• embodiments described herein may also include properties and/or features (e.g., ingredients, components, members, elements, parts, and/or portions) described in one or more separate embodiments and are not necessarily limited strictly to the features expressly described for that particular embodiment. Accordingly, the various features of a given embodiment can be combined with and/or incorporated into other embodiments of the present disclosure. Thus, disclosure of certain features relative to a specific embodiment of the present disclosure should not be construed as limiting application or inclusion of said features to the specific embodiment. Rather, it will be appreciated that other embodiments can also include such features.
• Table 1 Predictive microbes for atopic dermatitis (AD), food allergic dermatitis (FAD), flea allergic dermatitis (FLAD) and environmental allergic dermatitis (EAD).
• AD atopic dermatitis
• FAD food allergic dermatitis
• FLAD flea allergic dermatitis
• EAD environmental allergic dermatitis
• NLF-7-7 2597701 jAcidovorax carolinensis:553814 !Melaminivora sp.
• T1 1858609 I Ralstonia mannitolilytica:105219 j Pseudomonas denitrificans (nom.
• Table 3 Predictive microbes alongside their taxonomic classification for asthma. Of the 112 total predictive microbes (see Table 2), 12.5% are gram-positive bacteria. j . : . : . : j bacteria jproteobacteria j 55.36% j bacteria j fusobacteria 6% j bacteria jbacteroidetes i 20% bacteria ! spirochaetes 1% bacteria j actinobacteria : 6%
• Table 4 Predictive microbes alongside their taxonomic classification for food allergic dermatitis. Of the 122 total predictive microbes for food allergic dermatitis (see Table 1), approximately 10.66% are gram-positive bacteria. Note that 'candidatus' stands for well- characterized, but yet uncultured bacteria.
• Table 5 Predictive microbes alongside their taxonomic classification for flea allergic dermatitis. Of the 99 total predictive microbes for flea allergic dermatitis (see Table 1), approximately 11.11% are gram-positive bacteria. Note that 'candidatus' stands for well- characterized, but yet uncultured bacteria.
• Table 7 Predictive microbes alongside their taxonomic classification for environmental allergic dermatitis. Of the 110 total predictive microbes for environmental allergic dermatitis (see Table 1), approximately 7.27% are gram-positive bacteria. Note that 'candidatus' stands for well-characterized, but yet uncultured bacteria.
• Citrobacter sp. RHBSTW-01044:2742678 increased increased increased increased
• Citrobacter sp. RHB36-C 18:2742627 increased Serratia sp. JKS000199: 1938820 increased Klebsiella sp. ⁇ P3-W 18-HSBI.-02:2675710 increased Citrobacter sp. RHBSTW-00229:2742641 increased Citrobacter sp. RI IBSTW-00570:2742655 increased Bacteroides zoogleoformans:28119 increased Alicy cliphilus denitrificans K601: 179636 increased
• Diaphorobacter sp. JS3050:2735554 increased
• Acidovorax ebreus TPSY:721785 increased
• Table 9 The relative increased or decreased abundance for each predictive microbe for food allergic dermatitis.
• PRwf-l:349106 decreased Fusobacterium nucleatum subsp. vincentii ChDC F8:851 decreased Leptotrichia sp. oral taxon 212:712357 decreased Psychrobacter sp.
• P2G3: 1699622 decreased Lachnoanaerobaculum umeaense:617123 decreased Psychrobacter sp.
• EGD-AKN5 1524461 increased Citrobacter sp.
• RHBSTW-01044:2742678 increased Klebsiella sp.
• WP3- ⁇ Y18-HSBF-02:2675710 increased Serratia sp.
• LS-F2485839 increased Bacteroides sp. HF-162:2785531 increased Pseudomonas sp.
• FDAARGOS_761:2545800 increased Burkholderia sp. 2002721687:1468409 increased Xanthomonas perforans 91-118:442694 increased Actinomyces sp.
• oral taxon 169:712116 increased Corynebacterium mycetoides:38302 increased Actinomyces sp. oral taxon 171 str.
• F0337:706438 increased Xanthomonas euroxanthea: 2259622 increased Arcobacter thereius
• LMG 24486:544718 increased Bacteroides sp.
• A1CF2528203 increased Treponema pedis str.
• T A4:409322 increased Streptococcus intermedius JTH08:1338 increased Flavonifractor plautii:292800 increased Campylobacter sp.
• Aeromonas sp. ASNIH3: 1636608 increased increased increased increased
• Cardiobacterium hominis:2718 increased increased increased increased increased increased increased increased increased increased
• Acidovorax ebreus TPSY:721785 increased
• Diaphorobacter sp. JS3050:2735554 increased
• Table 10 The relative increased or decreased abundance for each predictive microbe for flea allergic dermatitis.
• Table 11 The relative increased or decreased abundance for each predictive microbe for atopic dermatitis.
• Capnocytophaga sp. H2931:1945657 decreased Capnocytophaga canimorsus Cc5:28188 decreased Neisseria shayeganii:607712 decreased
• EGD-AKN5 1524461 increased Tessaracoccus lapidicaptus: 1427523 increased Xanthomonas perforans 91-118:442694 increased Arcobacter thereius LMG 24486:544718 increased
• Treponema sp. OMZ 804:120683 increased Stenotrophomonas nitritireducens : 83617 increased Campylobacter rectus: 203 increased Porphy romonas gingi vali s W83 : 837 increased Comamonas aquatica:225991 increased
• Acidovorax carolinensis 553814 increased Acidovorax sp.
• Tl 1858609 increased Ralstonia mannitolilytica: 105211 increased Pseudopropionibacterium propio increased
• Table 12 The relative increased or decreased abundance for each predictive microbe for environmental allergic dermatitis.
• any feature herein may be combined with any other feature of a same or different embodiment disclosed herein. It will be appreciated that while features may be optional in certain embodiments, when features are included in such embodiments, they can be required to have a specific configuration as described in the present disclosure.
• any steps recited in any method or process described herein and/or recited in the claims can be executed in any suitable order and are not necessarily limited to the order described and/or recited, unless otherwise stated (explicitly or implicitly). Such steps can, however, also be required to be performed in a specific order or any suitable order in certain embodiments of the present disclosure.

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