Oral swab-based tests for detecting various disease conditions in domestic cats

Abstract

Systems and methods for screening, diagnosing, indicating, treating and confirming renal / urinary, inflammatory and / or endocrine disease conditions in domestic cats.

Description

[Technical field]

[0001] This application claims the benefit of and priority to U.S. Provisional Application No. 63 / 221,559, filed July 14, 2021. This application also claims the benefit of and priority to U.S. Provisional Application No. 63 / 221,558, filed July 14, 2021. The entirety of each of the above references is incorporated herein by specific reference.

[0002] The present invention relates to systems and methods for screening, detecting, diagnosing and confirming renal and / or urinary, inflammatory or endocrine disease conditions in domestic cats. [Background technology]

[0003] Disease states as well as nutritional and environmental factors have important effects on the dynamic microbial composition of the oral cavity (i.e., the oral microbiome). Because the mouth is the first line of defense against the constant onslaught of foreign microorganisms, the oral microbiome has evolved to be competitive and territorial. The state of the oral microbiome has shown strong correlations with both dental and systemic health. For example, existing human studies have shown an association or correlation between the human oral microbiome and the presence of chronic kidney disease (CKD). Domestic animals such as cats are also at risk for developing renal and / or urinary diseases, e.g., CKD.

[0004] Many cats do not receive regular veterinary care, so early signs of kidney and / or urinary disease are often overlooked. Early signs or symptoms of some diseases, such as CKD, may go unrecognized and undiagnosed because they do not usually exhibit clinical symptoms. Furthermore, because the early symptoms of some kidney and / or urinary diseases, such as CKD, are non-specific (e.g., lethargy, weakness, vomiting), pet owners may not recognize the symptoms as indicative of a condition that requires veterinary assistance or diagnosis.

[0005] Compounding these problems is the difficulty of obtaining a urine sample from a feline, so urine testing, important for diagnosing kidney and / or urinary disease, is rarely performed as part of a routine veterinary visit. Obtaining a urine sample requires cystocentesis, a procedure in which urine is collected from the bladder using a sterile needle and syringe. The reason cystocentesis is not always effective is because the cat's bladder may not be full at the time of the veterinary visit. To ensure that early signs of the development of kidney and / or urinary disease are not missed during the examination, it is necessary to have regular blood and urine tests on the animal. Due to the difficulty of urine testing already mentioned and the cost of preventive veterinary care, it is rare for cats to have these tests regularly. As a result, most cases of chronic kidney disease (CKD) are discovered at a later stage, when treatment options are limited and disease progression is rapid. Urinary crystals and urinary stones are also often not diagnosed until the cat is in severe pain, so urethral blockage may occur before a veterinary visit is made.

[0006] Current methods for early diagnosis or detection of kidney / urinary disease only require or depend on the pet owner to continue veterinary visits every 6-12 months. As already mentioned, many pet owners do not continue regular veterinary visits. In addition, current methods also require the veterinarian to perform serum and urine diagnostic screening during regular veterinary visits. This is currently not common unless the cat is about 6-8 years of age or older or the cat is already showing symptoms of CKD. Other diseases such as inflammatory bowel disease (IBD) and diabetes mellitus (DM) can also be problematic in cats. These diseases may also be overlooked by veterinarians and go undiagnosed until the disease has progressed to a later stage where treatment options are scarce. Summary of the Invention [Problem to be solved by the invention]

[0007] There is therefore a need for a robust and accurate, yet safe, painless and affordable means that can be used for routine detection of a variety of feline diseases, including renal and / or urinary, inflammatory and / or endocrine diseases. [Means for solving the problem]

[0008] Embodiments of the present invention include systems and methods for screening, detecting, diagnosing, treating and / or confirming one or more disease states in cats. For example, embodiments of the present invention include systems and methods for screening, detecting, diagnosing and / or confirming kidney disease, urinary disease, inflammatory disease and / or endocrine disease. Using such tools to guide and complement a veterinarian's health evaluation can significantly improve kidney and / or urinary health. Embodiments of the present invention can allow for earlier detection and earlier treatment of deteriorating kidney or urinary function compared to relying solely on veterinary clinic visits. Embodiments of the disclosed subject matter describe methods for investigating the oral microbiome of a cat. The disclosed methods investigate the oral microbiome to detect trends in the compositional abundance of microorganisms that may be associated with kidney and / or urinary disease in a cat. Detecting, confirming and / or quantifying trends in the compositional abundance of microorganisms can allow a practitioner to screen and / or indicate whether a cat is in a particular kidney and / or urinary disease state. By detecting and confirming renal and / or urinary disease conditions, a physician and / or cat owner can treat and / or prevent the renal / urinary disease condition in question.

[0009] In some embodiments, a method for detecting and / or indicating feline renal / urinary disease is disclosed. The method includes receiving an oral swab sample from a cat, manipulating the sample, e.g., heat-treating the oral sample, and extracting microbial deoxyribonucleic acid (DNA) from the heat-treated sample. The method can further include sequencing the microbial DNA to determine whether any one or more specific microorganisms are present in the oral sample (and in what relative proportions), where the determination of the one or more specific microorganisms allows for the generation of an oral microbial profile for the cat. The method can further include comparing the oral microbial profile of the cat to a reference database containing established microbial profiles, the database identifying correlations between (i) a profile containing one or more microorganisms and (ii) a corresponding renal / urinary disease, and generating a risk score indicative of the likelihood that the cat is suffering from a specific renal / urinary disease based on the comparison of the oral microbial profile to the database of established microbial profiles.

[0010] The method may further include a step of treating a particular renal / urinary disease and / or performing a therapeutic treatment. In some embodiments, the therapeutic treatment may include administering a therapeutic compound, for example, a compound designed to inhibit or promote the growth of one or more particular microorganisms present in the oral microbiome of the mammal. In some embodiments, the therapeutic compound includes a prebiotic, a postbiotic, a probiotic, a drug, or a combination thereof. In some embodiments, the therapeutic compound includes a phosphate binder, an antibiotic, a compound that regulates feline hypertension and / or blood pressure, and erythropoietin, among other therapeutic compounds. In some embodiments, the therapeutic treatment may also include brushing the mammal as a topical treatment.

[0011] In some embodiments, the therapeutic treatment may include a diet designed to resolve and / or alleviate the renal / urinary disease condition. For example, a diet with restricted protein, phosphorus, and sodium content and high concentrations of water-soluble vitamins, fiber, and antioxidants may extend the lifespan and improve the quality of life of cats suffering from CKD. In some embodiments, the diet may include switching to wet food to help maintain the cat's adequate hydration. In some embodiments, the diet may be designed to treat or manage IBD and / or DM. Therapeutic treatment may include potassium supplementation, and supplementation with other nutrients or vitamins.

[0012] In some embodiments, a method for indicating a disease in a cat (e.g., renal / urological disease, IBD, and / or diabetes) includes receiving an oral swab sample from a cat and performing a heat treatment on the oral sample. The method may also include performing a magnetic bead-based deoxyribonucleic acid (DNA) extraction on the heat-treated oral sample to extract microbial DNA present in the oral swab sample, and sequencing the microbial DNA to identify whether any one or more specific microorganisms are present (and present at any compositional abundance) in the oral sample, whereby identification of the one or more specific microorganisms can generate an oral microbial profile for the cat. The method may further include comparing the oral microbial profile of the cat to a database of established microbial profiles that identifies a correlation between (i) a profile including one or more microorganisms (and their compositional abundance) and (ii) a disease of interest (e.g., renal / urological disease, IBD, and / or diabetes), and generating a risk score indicative of the likelihood that the cat has a disease based on the comparison of the oral microbial profile to the database of established microbial profiles. The method may include, in response to generating a risk score and identifying the particular condition (e.g., renal / urinary disease, IBD, and / or diabetes), administering a therapeutic treatment designed to treat the particular condition, recommending veterinary attention or follow-up examination, and / or recommending home care for the particular condition (e.g., renal / urinary disease, IBD, and / or diabetes).

[0013] Also disclosed is a computer system, in some embodiments, configured to display one or more diseases in a cat (e.g., renal / urinary disease, IBD, and / or diabetes), the computer system including one or more processors and one or more computer readable hardware storage devices storing instructions executable by the one or more processors. The instructions can configure the computer system to receive sequenced microbial DNA data of an oral swab sample taken from the cat, map the sequenced microbial DNA to identify whether any one or more specific microbial species are present in the oral sample (through identification of the one or more specific microbial species, generating an oral microbial profile for the cat), calculate the relative abundance of different microbial species to further construct the oral microbial profile, compare the oral microbial profile to a database of established microbial profiles (the database identifies a correlation between (i) a profile including one or more microbial species and their relative abundance, and (ii) one or more corresponding diseases (e.g., renal / urinary disease, IBD, and / or diabetes)), and generate a risk score indicative of the likelihood that the cat is suffering from a specific disease (e.g., renal / urinary disease, IBD, and / or diabetes) based on a comparison of the oral microbial profile with the database of established microbial profiles. The instructions may further configure the computer system to generate a report in response to generating a risk score that summarizes and / or provides the risk score and prescribes appropriate therapeutic treatment and / or home treatment protocols to address (e.g., treat, inhibit, and / or prevent) a particular disease. Treatment protocols may be influenced by the severity of the disease state (as indicated by or associated with the risk score).

[0014] For example, in some embodiments, the risk score may include or be associated with approximately three risk assessment categories based on the generated risk / probability score: 0.0-0.33 group is classified as "low risk" for kidney or urinary disease, >0.33-0.66 is classified as "medium risk" for kidney or urinary disease, and >0.66-1.0 is classified as "high risk" for kidney or urinary disease. For example, a risk score of 0.34 meets the threshold for classifying a cat as medium risk for kidney or urinary disease. The granularity of the risk score and / or the number of categories may be altered as more data is added to the system and method.

[0015] In some embodiments, the therapeutic treatment or home care protocol may directly alter the composition of the feline oral microbiome or may alter the composition of the feline oral microbiome as a secondary consequence of treatment for a particular disease (e.g., renal / urological disease, IBD, and / or diabetes). In some embodiments, alteration of the feline oral microbiome composition treats and / or resolves a particular disease or condition. In some embodiments, the therapeutic treatment restores the feline oral microbiome. In some embodiments, restoring the feline oral microbiome makes the feline oral microbiome more similar (both in terms of the one or more specific microbial species present and their relative abundance) to a healthy feline oral microbiome (or an established oral microbiome). In some embodiments, the therapeutic treatment or home care protocol is designed to maintain the composition of the feline oral microbiome. In some embodiments, the therapeutic treatment protocol is designed to stimulate the metabolic output of the feline oral microbiome. Stimulating the metabolic output of the feline oral microbiome may involve using known enzyme pathway analysis tools to provide additional perspective to existing microbial composition data to further characterize disease manifestations and improve predictive disease models.

[0016] Illustrative embodiments and non-limiting examples of the present invention include the following. Example 1. A method for screening, detecting and / or preventing one or more diseases in a domestic cat, the method comprising: obtaining an oral microbial profile for the feline, the oral microbial profile comprising one or more microbial species present in an oral sample from the feline and a quantity or abundance of the one or more microbial species within the oral sample; The oral microbial profile is (i) the incidence and / or prevalence of one or more diseases in cats (e.g., renal / urinary disease, IBD, and / or diabetes); and (ii) the presence and / or abundance of different microbial species within the oral microbiome of the feline, wherein the different microbial species comprise one or more microbial species within the oral sample; comparing the weighted correlation between the generating a risk score indicative of the likelihood that the cat suffers from one or more renal / urinary diseases based on one or more matches between the oral microbial profile and information in the database; classifying the cat as suffering from one or more diseases (e.g., renal / urinary disease, IBD and / or diabetes) if the risk score meets or exceeds a predetermined threshold, and optionally prescribing an appropriate therapeutic treatment protocol to treat, ameliorate or prevent the onset, progression or recurrence of the one or more diseases if the risk score meets or exceeds a predetermined threshold.

[0017] Example 2. The method of Example 1, further comprising administering a therapeutic treatment protocol to the cat or verifying that said therapeutic treatment protocol has been administered to the cat, wherein said therapeutic treatment protocol is sufficient to alter the oral microbial profile of the cat.

[0018] Example 3. Obtaining a feline oral microbial profile includes: obtaining nucleic acid sequence data corresponding to microbial nucleic acids obtained from the oral sample; analyzing the nucleic acid sequence data to identify and optionally quantify one or more microbial species present in the oral sample; and generating an oral microbial profile for the feline based on the identified and optionally quantified one or more microbial species.

[0019] Example 4. The step of obtaining microbial nucleic acid sequence data comprises: sequencing microbial nucleic acid of the oral sample; and optionally, and isolating microbial nucleic acid from the oral sample.

[0020] Example 5. The step of isolating microbial nucleic acids from the oral sample comprises: performing a heat treatment on the oral cavity sample; and performing magnetic SPRI bead-based nucleic acid extraction on the heat-treated oral sample in the presence or absence of a protein digestion reagent and a surfactant to extract microbial nucleic acids from the oral sample.

[0021] Example 6. The step of analyzing the microbial nucleic acid sequence data comprises: demultiplexing the nucleic acid sequence data; trimming the nucleic acid sequence data; Mapping one or more unmapped reads to a feline reference genome and / or an existing microbial reference genome; classifying one or more reads from the mapped nucleic acid sequence data as feline; classifying one or more reads from the mapped nucleic acid sequence data as a microorganism; quantitating the one or more microbial reads; transforming the quantified one or more microbial reads using a method such as pairwise log ratio transformation to account for sequence coverage bias; comparing the compositional abundance pattern of the transformed one or more microbial reads against compositional abundance patterns of transformed data from a reference database that includes samples from cats that are not affected by renal / urinary disease, as well as samples from cats that are affected by a particular disease (e.g., renal / urinary disease, IBD, and / or diabetes); The method of Example 3, comprising one or more steps of:

[0022] Example 7. The step of comparing the oral microbial profile of the cat with information in a database comprises: calculating the abundance of one or more microbial species in the oral sample; identifying one or more microbial species in the oral sample; comparing the abundance of one or more microbial species identified in the oral sample with the presence and / or abundance of various microbial species in the feline oral microbiome contained in the database; The method of Example 1, comprising one or more steps of:

[0023] Example 8. The step of generating a risk score comprises: determining one or more similarities between the compositional abundance of one or more microbial species in the oral sample and the compositional abundance of various microbial species in the feline oral microbiome contained in the database; identifying one or more matches between the identity of one or more microbial species in the oral sample and the presence of various microbial species in the feline oral microbiome contained in the database; Quantifying one or more similarities identified between the compositional abundance of one or more microbial species in the oral sample and the compositional abundance of one or more microbial species in the feline oral microbiome contained in the database; confirming the presence of one or more predicted microbial species in the oral sample; The method of Example 1, comprising one or more steps of:

[0024] Example 9. The method of Example 1, wherein the one or more diseases are selected from the group consisting of IBD, DM, CKD, struvite urinary crystals or stones, urinary calcium oxalate crystals or stones, cystine urinary crystals or stones, or idiopathic cystitis.

[0025] Example 10. A method for treating a disease comprising: (i) generating a report providing said risk score; (ii) an indication for developing one or more diseases (e.g., kidney / urinary disease, IBD, and / or diabetes) if said risk score meets or exceeds a predetermined threshold; (iii) a timing recommendation; (iv) optionally, one or more home practice methods for improving kidney / urinary health; (v) optionally, one or more diagnostic steps for diagnosing one or more kidney / urinary diseases if said risk score meets or exceeds a predetermined threshold; and (vi) optionally, generating a report providing a prescription for a therapeutic treatment protocol. and transmitting the generated report to the cat's owner and / or their veterinarian in an electronic document.

[0026] 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 one or more disease states in a cat, comprising: one or more processors; and one or more computer readable hardware storage devices; The one or more computer readable hardware storage devices include: The computer system comprises: receiving microbial nucleic acid sequence data corresponding to microbial nucleic acid from an oral sample taken from the cat; analyzing the microbial nucleic acid sequence data to identify one or more microbial species present in the oral sample and to quantify the one or more microbial species; generating an oral microbial profile for the feline based on the identified one or more microbial species and their respective abundances; The oral microbial profile is (i) the incidence and / or prevalence of one or more diseases in cats; and (ii) the presence and / or abundance of different microbial species within the oral microbiome of the feline, wherein the different microbial species comprise one or more microbial species within the oral sample; Compare with database information to confirm weighted correlation between identifying one or more matches between the oral microbial profile and information in a database; generating a risk score indicative of the likelihood that the cat has one or more renal / urinary diseases based on one or more matches between the oral microbial profile and information in the database; and optionally, If the risk score meets or exceeds a predetermined threshold, the cat is diagnosed as having one or more of the disease conditions; if the risk score meets or exceeds a predetermined threshold, prescribing an appropriate therapeutic treatment protocol to treat or prevent the one or more disease states; and / or generating a report displaying (i) the risk score, (ii) an indication for developing one or more disease states if the risk score meets or exceeds a predetermined threshold, (iii) a timing recommendation, (iv) optionally, one or more home practice measures to improve health, (v) optionally, one or more diagnostic steps to diagnose one or more disease states if the risk score meets or exceeds a predetermined threshold, and (vi) a prescription for a therapeutic treatment protocol; and / or and storing instructions executable by the one or more processors configured to transmit the generated report in an electronic document to the cat owner and / or their veterinarian. Computer system.

[0027] Example 13. The computer system of Example 12, wherein the instructions further configure the computer system to analyze the metagenomic sequence data of the oral sample to map one or more unmapped sequence reads to a feline reference genome and / or map one or more sequence reads to a microbial reference genome, and optionally classify the reads as microbial or feline.

[0028] Example 14. The computer system of Example 13, wherein the instructions further configure the computer system to: verify at least one unmapped sequence read in the metagenomic sequence data; and optionally classify the at least one unmapped read.

[0029] 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 feline's oral microbial profile to the database of established microbial profiles.

[0030] Example 16. The computer system of Example 12, wherein the instructions further configure the computer system to calculate the abundance of one or more microbial species present in the oral sample. Example 17. The computer system of Example 16, wherein the abundance of one or more specific microbial species present in the oral sample correlates with whether the one or more specific microbial species are predictive microbial species for a particular disease state.

[0031] 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 corresponding feline oral sample against information in the database.

[0032] Example 19. The computer system of Example 18, wherein one or more particular microbial species are predictive microbial species if 50% or more of the maximum possible pairwise log ratio comparisons associated with that microbial species are significantly different when comparing diseased and control cohorts.

[0033] Example 20. A method for predicting the onset of a disease state in a cat, the method comprising: obtaining an oral sample from a cat, the oral sample comprising one or more microbial species; isolating microbial nucleic acid of the one or more microbial species from the oral sample; obtaining microbial nucleic acid sequence data corresponding to said microbial nucleic acid; analyzing the microbial nucleic acid sequence data to identify and optionally quantify one or more microbial species present in the oral sample; generating an oral microbial profile for the cat based on the identified (optionally quantified) one or more microbial species, an oral microbial profile comprising the one or more microbial species, and optionally, the amount or relative abundance of the one or more microbial species in the oral sample; The oral microbial profile is (i) the incidence and / or prevalence of one or more diseases in cats; and (ii) the presence and / or abundance of different microbial species within the oral microbiome of the feline, wherein the different microbial species include one or more microbial species within the oral sample; comparing the weighted correlation between the generating a risk score indicative of the likelihood that the cat will develop one or more diseases based on one or more matches between the oral microbial profile and information in the database; and indicating that the cat will develop the one or more diseases if the risk score meets or exceeds a predetermined threshold.

[0034] Example 21. A method for diagnosing a disease in a cat, the method comprising: obtaining an oral sample from a cat, the oral sample comprising one or more microbial species; isolating microbial nucleic acid of the one or more microbial species from the oral sample; obtaining microbial nucleic acid sequence data corresponding to said microbial nucleic acid; analyzing the microbial nucleic acid sequence data to identify and optionally quantify one or more microbial species present in the oral sample; generating an oral microbial profile for the cat based on the identified (optionally quantified) one or more microbial species, an oral microbial profile comprising the one or more microbial species, and optionally, the amount or relative abundance of the one or more microbial species in the oral sample; The oral microbial profile is (i) the incidence and / or prevalence of one or more diseases in cats; and (ii) the presence and / or abundance of different microbial species within the oral microbiome of the feline, wherein the different microbial species include one or more microbial species within the oral sample; comparing the weighted correlation between the generating a risk score indicative of the likelihood that the cat will develop one or more diseases based on one or more matches between the oral microbial profile and information in the database; and diagnosing the cat as suffering from the one or more diseases if the risk score meets or exceeds a predetermined threshold.

[0035] Example 22. The method of Example 21, wherein the one or more diseases are selected from the group consisting of inflammatory bowel disease, diabetes mellitus, chronic kidney disease, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, or idiopathic cystitis.

[0036] Example 23. A method for treating a renal and / or urinary disease, an inflammatory disease or an endocrine disease in a cat, the method comprising: obtaining an oral sample from a cat, the oral sample comprising one or more microbial species; isolating microbial nucleic acid of the one or more microbial species from the oral sample; obtaining microbial nucleic acid sequence data corresponding to said microbial nucleic acid; analyzing the microbial nucleic acid sequence data to identify and optionally quantify one or more microbial species present in the oral sample; generating an oral microbial profile for the cat based on the identified (optionally quantified) one or more microbial species, an oral microbial profile comprising the one or more microbial species, and optionally, the amount or relative abundance of the one or more microbial species in the oral sample; The oral microbial profile is (i) the incidence and / or prevalence of one or more renal and / or urinary, inflammatory or endocrine diseases in cats; and (ii) the presence and / or abundance of different microbial species within the oral microbiome of the feline, wherein the different microbial species include one or more microbial species within the oral sample; comparing the weighted correlation between the generating a risk score indicative of the likelihood of the cat developing one or more renal and / or urinary diseases, inflammatory diseases, or endocrine diseases based on one or more matches between the oral microbial profile and information in the database; If the risk score meets or exceeds a predetermined threshold, diagnosing the cat as having one or more of the renal and / or urinary diseases, inflammatory diseases, or endocrine diseases; administering a therapeutic treatment sufficient to treat the one or more renal and / or urinary disorders, inflammatory disorders or endocrine disorders.

[0037] This Summary is provided to introduce some concepts of the invention in a simplified form that are more fully described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to delineate the scope of the claimed subject matter.

[0038] Various objects, features, characteristics and advantages of the present invention will become apparent and be more readily understood from the following description of embodiments, taken in conjunction with the accompanying drawings and claims, which form a part of this specification. In the drawings, like reference numerals may be used to indicate corresponding or similar parts in the various drawings, and the various components shown are not necessarily drawn to scale. [Brief description of the drawings]

[0039] [Figure 1A] The workflow of renal / urinary health testing and oral microbiome reference database construction are shown. [Figure 1B] Same as above. [Figure 2A] Distributions of mean log ratio difference scores between healthy cohorts and pairwise microbial interactions associated with (A) CKD, (B) struvite crystals or stones, (C) calcium oxalate crystals or stones, (D) cystine crystals or stones, and (E) idiopathic cystitis. [Figure 2B] Same as above. [Figure 2C] Same as above. [Figure 2D] Same as above. [Figure 2E] Same as above. [Figure 3A] 1 illustrates the sensitivity and specificity of a feline renal / urinary health test based on a two-component Gaussian mixture model. Sensitivity refers to the ability of the disclosed embodiments to detect cats known to have renal / urinary disease. Specificity refers to the ability of the disclosed embodiments to detect cats in a healthy control cohort as not having renal / urinary disease. [Figure 3B] Same as above. [Figure 3C] Same as above. [Figure 3D] Same as above. [Figure 3E] Same as above. [Figure 4A] Shown are (A) feline CKD and periodontal disease, and (B) oral microbiome predicted microbial intersections with features of feline CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, or idiopathic cystitis. [Figure 4B] Same as above. [Diagram 5] Microbial species abundance as a function of sequence read counts, where sequence read counts are compared from two different types of metagenomic whole genome sequencing (WGS) library preparations: a ligation-based approach and a tagmentation-based approach (e.g., Illumina Nextera DNA Flex library preparation kit). [Figure 6] We provide clinical record validation for diagnosis and present oral microbiome-based CKD risk assessment for a citizen science-recruited cohort in which cats were either diagnosed with CKD or deemed healthy (no chronic or acute health problems in the past 6 months). [Figure 7] Figure 1 shows oral microbiome-based CKD risk assessment for five clinically recruited cats with known CKD stage at the time of oral sample collection. [Figure 8A] Distribution of mean log ratio difference scores between healthy cohorts and pairwise microbial interactions associated with (A) diabetes mellitus (DM) and (B) inflammatory bowel disease (IBD). [Figure 8B] Same as above. [Figure 9A] 1 shows sensitivity and specificity for (A) feline diabetes mellitus and (B) IBD testing based on a two-component Gaussian mixture model. Sensitivity refers to the ability of the disclosed embodiments to detect cats known to have IBD or diabetes. Specificity refers to the ability of the disclosed embodiments to detect cats in a healthy control cohort as not having IBD or diabetes. [Figure 9B] Same as above. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0040] Changes in the microbial composition of the mouth (i.e., the oral microbiome) may be associated with certain dental and systemic diseases. As this field of research is still in its infancy, human studies that comprehensively document these associations have only been published within the past decade. There has also been limited research on this topic in pets such as cats and dogs. Currently, disease states as well as nutritional and environmental factors may play a key role in the dynamic microbial composition of the oral cavity (i.e., their oral microbiome). Because the mouth is the first line of defense against constant exposure to foreign microorganisms, the oral microbiome has evolved to be competitive and rope-conscious. The oral microbiome is composed of microorganisms that are good at defending their own domain, and are usually able to avoid being replaced by foreign invaders such as pathogens. However, events that induce dysbiosis, such as improper diet, poor dental hygiene, the onset of systemic diseases, or changes in the environment, can cause pathogenic microorganisms to colonize many parts of the oral cavity (thereby altering the oral microbiome), which may be associated with pathology. Understanding the composition of the oral microbiome can provide information not only about the health of oral tissues, but also about the general health of animals and humans. For example, oral microbiome characteristics are associated with diseases such as inflammatory bowel disease (IBD), various cancers, and chronic kidney disease (CKD), among others. Information provided by the status of the oral microbiome can also be used to manage the health and well-being of pets.

[0041] The field of pet oral microbiome research has received little attention and is still in its infancy. Existing studies draw conclusions based on small sample sizes and outdated culture-based techniques to verify the authenticity of the microbiome. It is estimated that only about 2% of all existing bacteria can be cultured in the laboratory, meaning that studies that rely on such methods to classify microorganisms are likely to miss many important microbial organisms and may falsely emphasize certain species simply because the microorganisms can be cultured and measured. These challenges are compounded by the fact that laboratory cultures provide a very bacteria-centric view of the microbiome, with other microorganisms, such as fungi, protozoa, archaea, and viruses, often being ignored.

[0042] Investigations into the oral microbiome of cats can be performed by using oral (saliva) samples. Saliva sampling kits have gained popularity in recent years as testing for ancestry and microbial infections has become more widespread. Direct-to-consumer microbiome tests typically rely on a technique called 16S rRNA gene sequencing (NGS). This technique provides substantially more information than earlier bacterial culture efforts, but can only be used to confirm the bacterial species (and some archaea) present in the microbiome. In most cases, these tests do not provide sufficient resolution to reliably and consistently confirm bacteria beyond the genus level of taxonomic classification. Thus, in most cases, test results do not provide the exact species or strains of bacteria that comprise the microbiome. For this reason, database conclusions using these results are ambiguous and rely on approximations. Furthermore, it is well known that the microbiomes of various body sites can consist of viral, protozoan, and fungal species in addition to bacteria and archaea. This means that 16S rRNA gene sequencing approaches focus on only one part of the microbiome and ignore the rest.Embodiments of the present invention address these and other issues.

[0043] Before describing various embodiments of the present invention in detail, it should be understood that the present invention is not limited to specific parameters, terms, and descriptions of various specifically exemplified systems, methods, and / or products for each embodiment. Thus, although specific embodiments of the present invention will be described in detail with reference to specific features (e.g., configurations, parameters, characteristics, steps, components, ingredients, members, elements, parts, and / or portions, etc.), this description is illustrative and should not be construed as limiting the scope of the present invention and / or the claimed invention. Furthermore, the terms used in this specification are used to describe the embodiments and are not intended to limit the scope of the present invention and / or the claimed invention.

[0044] Disclosed herein are computer systems, systems and methods for the confirmation, screening, indication, diagnosis and / or treatment of feline renal and / or urinary disease conditions. Embodiments of the disclosed subject matter describe methods for surveying the oral microbiome of domestic cats to detect microbial compositional abundance trends associated with feline renal / urinary disease. By detecting, confirming and / or quantifying microbial compositional abundance trends, a physician can screen and / or indicate whether a cat has a particular renal and / or urinary disease condition. Detecting and confirming a renal / urinary disease condition allows a physician or pet owner to treat the disease to slow its progression and potentially prevent future recurrence of the renal / urinary disease condition.

[0045] The disclosed methods can, for example, compare the oral microbiome of a cat to that of a cat that has reportedly been diagnosed by the cat's owner and / or veterinarian with IBD, DM, CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, or idiopathic cystitis, using a reference database that includes established microbial profiles to associate one or more microbial species and their respective compositional abundances with one or more renal / urological diseases.

[0046] The disclosed systems and methods can include painless oral swab sample collection, whereby the oral microbiome can be investigated via buccal, supragingival and / or subgingival sampling. Such sampling does not require anesthesia of the animal and can be performed by the pet owner at home or by a veterinarian in a clinic. The disclosed systems and methods can potentially act as early indicators of renal / urinary disease-related processes that have not yet been officially diagnosed or show clinical signs. With regular use, early-stage renal / urinary disease can be identified, resulting in more cats being brought to the veterinary clinic earlier, ultimately reducing the number of emergency veterinary visits. Early identification of renal / urinary, inflammatory and / or endocrine disease conditions can beneficially reduce emergency visit costs and even save the cat's life. Identifying one or more disease conditions early means that more treatment options are available if the one or more diseases are diagnosed and confirmed.

[0047] [Established microbial profiles included in reference databases] The oral microbiome has evolved to be competitively territorial, since the mouth is the first line of defense against constant exposure to foreign microorganisms. The oral microbiome is composed of microorganisms that excel at defending their territory and usually resist being replaced by foreign invaders, including pathogens. Such microorganisms are usually present when the cat is healthy, and represent a healthy microbial profile of the feline oral microbiome. If the cat suffers from renal / urinary disease, inflammatory disease (e.g., IBD) or endocrine disease (e.g., DM), the composition of the oral microbiome may be altered by the presence of foreign or pathogenic microbial species and / or changes in the abundance ratios between the various microorganisms. Such changes in the oral microbiome composition may be indicated by a pathogenicity profile. In some cases, the presence of certain foreign and / or pathogenic microbial species and their abundance relative to other microorganisms in the oral cavity correlates with cats suffering from certain renal / urinary diseases.

[0048] By identifying specific microbial species(s) (and their respective relative abundances) that correlate with a particular renal / urinary disease state, pre-diagnostic screening for renal / urinary disease state is possible in cats exhibiting the presence of said identified microbial species(s). In other words, identification and / or indication of renal / urinary disease state can be associated with cats exhibiting a particular pathogenicity profile.

[0049] The gold standard for comprehensive studies of the microbiome is shotgun metagenomic sequencing, which can capture whole or near-complete genomes of organisms across all life domains, not just bacteria and archaea. The gold standard for metagenomic DNA extraction involves a process called bead-beating, which is recommended for complete microbial cell lysis during microbiome abundance and composition studies. This process helps to break down thick cell walls, such as those of gram-positive bacteria. This process is performed by rapidly agitating the sample using grinding media (balls or beads) in a bead beater.

[0050] In an exemplary embodiment of the present invention, the disclosed system and method do not use bead-beating for metagenomic DNA extraction, and such a process has been intentionally abandoned. The reason is that bead-beating also introduces a significant amount of DNA degradation that interferes with downstream sample processing, which may reduce the quality of the generated metagenomic sequencing library. According to an embodiment, since the disclosed system and method do not use bead-beating, the oral microbiome data in the result analysis may experience the problem of Gram-positive bacteria being underrepresented. Nevertheless, characteristic patterns of disease can be recognized. In some embodiments, the disclosed system and method, unlike 16S gene sequencing, allows microbial confirmation and classification down to the species level, or in some cases, down to the strain level.

[0051] In veterinary practice, dental diseases such as periodontal disease are common comorbidities in cats with CKD. The reasons are not well known, but some theories suggest that the progression of untreated periodontal disease allows pathogenic microorganisms to pass through the gums into the bloodstream and migrate to various organs of the body where their presence is associated with pathology. The theory suggests that CKD pathology can often be traced back to untreated periodontal disease. This theory is supported by the fact that some overlap is observed in the microbial species important to the two diseases. There is also some overlap between the microbial species associated with various feline urinary / kidney diseases. However, a number of microorganisms whose compositional abundance in the oral microbiome is particularly predictive of CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, or idiopathic cystitis have also been identified. These facts suggest that in addition to the presence of a core set of microorganisms usually associated with kidney / urinary diseases, there are microbial profiles associated with specific kidney / urinary pathologies. This also suggests that there may be microbial profiles associated with other diseases such as IBD and DM.

[0052] Using shotgun metagenomic oral microbiome sequencing and compositional data analysis techniques for 38,000 domestic cats, a comprehensive survey of feline oral microbiomes was conducted to identify 8,344 microbial species present in the feline oral microbiome. Whether the domestic cats included in the shotgun metagenomic sequences had a specific renal / urinary disease was determined by asking the owners of the cats through a survey whether the cats had been officially diagnosed by a veterinarian as having a specific renal / urinary disease, inflammatory disease, or endocrine disease (e.g., IBD, DM, CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, or idiopathic cystitis).

[0053] The reference database is a weighted correlation database and includes at least the 8,344 confirmed microbial species present in the feline oral microbiome. On average, 606 microbial species were identified per cat, of which 97% were classified as bacteria and archaea, 0.27% as DNA viruses (RNA viruses cannot be detected by shotgun metagenomic sequencing), 0.02% as phages, and <2% as fungi. The various microbial species identified as being involved in and contributing to a particular renal / urinary disease are compiled into an "established microbial profile." The established microbial profile is a list or collection of one or more confirmed microbial species and their respective relative abundances that are known to contribute to and / or contribute to a particular renal / urinary disease condition. In some embodiments, the established microbial profile can also include the percentage of gram-positive microorganisms and the ratio of gram-positive to gram-negative microorganisms in addition to the identity of the microorganisms (i.e., genus and species). In some embodiments, the established microbial profile can indicate the relative abundance (increase or decrease) of one or more microbial species (see Tables 1-16 below).

[0054] For example, the established microbial profile includes 38 microorganisms predictive of five kidney / urinary diseases (CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, idiopathic cystitis), as well as a set of microorganisms that specifically predict one of the five kidney / urinary diseases (CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, idiopathic cystitis). The "predictive microorganisms" are described in more detail below. The established microbial profile can be inferred by referencing a reference database to rank and / or weight each included microbial species according to how frequently and in what proportion a particular microorganism is observed in felines suffering from a particular kidney / urinary disease. The extent to which any one microbial species contributes to a particular renal / urinary disease state correlates with how frequently that microbial species appears (or is present) in the oral microbiome while the feline has the particular renal / urinary disease state, and also correlates with how consistently and significantly such microbial species exhibits a different relative abundance to other oral microbes when compared to healthy control samples.

[0055] The established microbial profiles included in the reference database also include established microbial profiles of healthy cats that do not suffer from renal / urinary disease. For example, the established microbial profile of a healthy cat is identified by juxtaposing the microbial species present in the oral microbiome and their relative abundance in the absence of renal / urinary disease. The established healthy microbial profile can establish a standard or control group for the microbial species present and their relative abundance. Deviations from this profile can allow a physician to predict and / or indicate, for example, that a cat may suffer from a renal / urinary disease. Similarly, deviations from the established healthy microbial profile can allow a physician to diagnose a cat as suffering from a renal / urinary disease before symptoms for that disease appear.

[0056] The microbial profile established for each renal / urological disease state is compared with the microbial profile established for a healthy cat to identify differences between the renal / urological disease state and the healthy state. In some embodiments, the comparison is a pairwise logarithmic ratio comparison. For example, there may be some overlap between the oral microbiomes of healthy cats and cats suffering from CKD. By comparing the established healthy microbial profile with the established CKD microbial profile, common microbial species that appear in similar abundance between the two can be identified. Any microbial species that are not shared between the two microbial profiles, or that appear in significantly different proportions between the two profiles, can be identified as being involved in the development of CKD. Identification of such microbial species in the oral microbiome of a cat indicates that the cat suffers from CKD.

[0057] Figures 1A-1B show the workflow of the renal / urinary health test and the construction of an oral microbiome reference database using feline subjects. In Figure 1A, the workflow of the feline renal / urinary health test includes the steps of collecting cat oral swabs in a DNA preservation solution, extracting and preparing DNA for shotgun metagenomic next-generation sequencing (NGS), sequencing the DNA, analyzing the data, and generating a report that shows a risk assessment for various renal / urinary diseases based on the status of the oral microbiome and provides tailored treatment recommendations. In Figure 1B, the feline oral microbiome reference database was constructed by sequentially applying filters to an initial database of 38,000 cats. First, all data was removed from the tagmentation-based NGS library preparation samples. This step was done due to the observed effect of library preparation methods on the abundance of microbial species (see Figure 5). The ligation-based method was preferred because the number of sequence reads per sample has a minimal impact on the number of microbial species detected. Additionally, Tn5 transposase-assisted tagmentation is known to introduce GC sequencing bias, especially in metagenomic communities, however, in some embodiments, tagmentation-based NGS library preparation may be included.

[0058] Next, samples without associated phenotype / health history records for the cat were removed. The microbial sequence data was confirmed, classified and mapped from the metagenomic sequence data of the samples. After classifying the microbial reads for each sample using KRAKEN2 and Bracken, samples with less than 10,000 classified microbial reads and samples with more than 500,000 classified microbial reads were removed. The remaining cats / samples were placed into cohorts. This generated the chronic kidney disease cohort (CKD; n=201), struvite urinary crystals or stones cohort (SUCS; n=207), calcium oxalate urinary crystals or stones cohort (UCOCS; n=89), cystine urinary crystals or stones cohort (CUCS; n=109), idiopathic cystitis cohort (IC; n=178) and healthy cohort (n=3,081).

[0059] While Figures 1A-1B show the renal / urinary health test workflow and the construction of the oral microbiome reference database, it should be understood that the same health test workflow was also performed for inflammatory diseases (e.g., IBD) and endocrine diseases (e.g., DM). This resulted in an IBD cohort (n=279) and a DM cohort (n=33) that were classified, mapped, and added to the oral microbiome reference database as well. Using the oral microbiome reference database in conjunction with the disclosed computer system, systems, and methods, a physician can screen, indicate, confirm, diagnose, and / or treat disease conditions in cats. Disease conditions include at least IBD, DM, CKD, SUCS, UCOCS, CUCS, and IC.

[0060] [Confirmation of predicted microorganisms] As a first stage to identify microorganisms significantly correlated with each renal and / or urological disease, pairwise log-ratio (PLR) transformation was performed on the species-level read counts output by Bracken. A z-test was then performed to identify significant PLR comparisons (p-values ​​<0.01) between control groups (i.e., healthy cohorts) and disease. The healthy cohorts were compared to CKD, SUCS, UCOCS, CUCS, and IC cohorts. The healthy cohorts were also compared to IBD and DM cohorts (see Figures 8A-9B).

[0061] The frequency of each microbial species in all significant PLRs was assessed. Only microbial species with more than 50% of the maximum possible comparisons with other species that were significant were retained. This measure was used as a proxy for the importance of various microbial species in the five renal / urological diseases of interest. These microbial species are the "predicted microbial species" for each renal / urological disease.

[0062] To identify microbial compositional abundance patterns across distinct populations of CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis, each sample was scored by comparing its predicted pairwise logarithmic ratio (pPLR) to the mean pPLR of the control group, taking into account the direction and magnitude of the difference. Figures 2A-2E show the distribution of mean logarithmic ratio difference scores between pairwise microbial interactions associated with CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis and healthy cohorts.

[0063] Next, we fitted five Gaussian mixture models (one model per each kidney / urological disease), each containing two components (healthy cohort and urological and / or kidney disease cohort), to the distribution of mean log ratio difference scores between pairwise microbial interactions. This modeling approach generates a score between 0 and 1 for each sample, indicating the probability that the sample belongs to the control cohort or each kidney / urological disease cohort. Figures 3A-E show graphically the likelihood that a sample belonging to the five kidney / urological disease cohorts (CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis) and a control sample would be classified as belonging to each of these cohorts based on the compositional abundances for each sample's predicted microorganisms. In all cases, a bimodal probability distribution consistent with sample identity was observed between kidney / urological disease and control groups. There were a few disease samples that formed a small peak close to 0 and a small set of control samples that formed a slight peak close to 1.

[0064] The microbial profiles established for each kidney / urinary disease state (CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis) were compared to the microbial profiles established for healthy cats to measure and quantify the differences and commonalities of microbial species and their abundance in the kidney / urinary disease state and the healthy state. The microbial profiles established for each kidney / urinary disease state were also compared to each other to identify overlapping microbial species commonly represented in each kidney / urinary disease state. The microbial profiles established for IBD and DM underwent similar comparisons to measure and quantify the differences and commonalities of microbial species and their abundance in the IBD / DM and the healthy state, and to identify overlapping microbial species commonly represented in each disease state.

[0065] The microbial profiles established for each disease state and healthy control state are subjected to a pairwise log-ratio (PLR) transformation. The PLR ​​transformation scales the microbial abundance for each microorganism, instead of a constant scaling factor, to correct for differences in potential sequencing coverage between samples. A z-test between the PLRs of each disease state and the control state is then performed. A p-value of less than about 0.01 serves as a threshold function for significant PLR comparisons. For each microbial species identified in the microbial profile established for a renal / urological disease state, the number of significant PLR comparisons (defined by the p-value) in which that microbial species appears is multiplied. If the number of significant PLR comparisons is at least 50% of all possible PLR ​​comparisons for that microorganism, the microbial species is considered the "predicted microorganism." This process can be repeated for each renal / urological disease state of interest. In other words, through z-test confirmation of significant PLR comparisons, predictive microorganisms for IBD, DM, CKD, struvite urinary crystals / stones, calcium oxalate urinary crystals / stones, cystine urinary crystals / stones, and idiopathic cystitis can be confirmed. Table 1 shows examples of predictive microorganisms confirmed for CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis. Table 2 shows examples of predictive microorganisms confirmed for IBD and DM.

[0066] As summarized in Table 1, 110 predictive microorganisms were identified for CKD, 94 for struvite urinary crystals or stones, 56 for calcium oxalate urinary crystals or stones, 90 for cystine urinary crystals or stones, and 94 for idiopathic cystitis. Predictive microorganisms for each kidney / urinary disease were identified based on a comparison of PLR microbial abundance between the established microbial profile in healthy controls and the established microbial profile of cats suffering from one of the five kidney / urinary diseases (see Figure 4). Thirty-eight microorganisms were identified as predictive of the five kidney / urinary diseases (CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis), although each disease has its own specific set of predictive microorganisms to distinguish it from the others. By plotting the average log ratio differences between significant pairwise microbial interactions in renal / urological disease samples and control samples, we were able to separate the sample populations based on their renal / urological disease status (see Figures 2A-2E). However, some overlap was observed between the populations, meaning that for a given sample set, the predicted microbial compositional abundances can be interpreted as consistent with either the control population or each renal / urological disease population.

[0067] Tables 3-9 summarize the percentage of microorganisms associated with or confirmed by various disease states of interest (e.g., IBD, DM, CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis). Tables 10-16 summarize the relative increased or decreased abundance of each predicted microorganism for each disease state of interest. This data (relative to the relative abundance, percentage and proportion of gram-positive bacteria present) can also be included in the microbial profile established for each disease state. Upon detection of one or more Gram-positive bacteria in the cat's oral microbiome (or acquisition of one or more proportions or percentages of these Gram-positive bacteria), the systems and methods can indicate or diagnose that the cat is suffering from a particular disease (e.g., IBD, DM, CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis).

[0068] The same procedures (comparisons, PLR transformations, z-tests, etc.) were performed for the IBD and DM cohorts. Figures 8A-8B show the distribution of mean log-ratio difference scores between pairwise microbial interactions associated with healthy cohorts and (A) diabetes mellitus (DM), and (B) inflammatory bowel disease (IBD). Figures 9A-9B show the sensitivity and specificity for feline IBD and diabetes mellitus health tests based on two-component Gaussian mixture models. Table 2 shows the predicted microorganisms associated with IBD and DM. Tables 3 and 4 summarize the percentage of Gram-positive predicted bacteria associated or confirmed with DM and IBD, respectively, along with disease-specific descriptions for predicted microorganisms belonging to various taxonomic classes (various bacterial, fungal, and viral genera). Tables 10 and 11 summarize the relative increased or decreased abundance of each predicted microorganism for DM and IBD, respectively.

[0069] It is important to note that the use of the word "predictive" is not to be construed as "causing," but simply reflects the fact that a microorganism has a significantly different compositional abundance in a particular renal / urological disease when compared to a control group. This can mean that the microorganism plays an active role in the pathology of the disease, or that the change in the compositional abundance of the microorganism is a by-product of the pathology. In either scenario, the presence of a microorganism at a particular abundance relative to other microorganisms directly correlates with the state of the renal / urological disease.

[0070] The algorithm and disclosed method of confirming predicted microorganisms can be continuously developed. The confirmed set or group of predicted microorganisms can change and evolve slightly as the population of the cohort (healthy cats and cats with renal or urological disease) changes and evolves. As more information about microorganisms and their presence or contribution to renal / urological, inflammatory or endocrine disease states becomes available, the confirmed set of predicted microorganisms will also change and evolve. The confirmed new set of predicted microorganisms will not be 100% different from the initial set, but rather a difference of about 25% to 85% may be expected. For example, the confirmed new set of predicted microorganisms may be 30, 35, 40, 45, 50, 55, 60, 65, 70, 75 or 80% different from the confirmed initial set of predicted microorganisms, or may differ by a difference defined by any two of the aforementioned values. By adding more cats to the cohort, the confirmed set of predicted microorganisms will change and evolve.

[0071] [Sequencing and extraction protocol] At least one feline oral swab can be taken to provide a sample for testing. The oral swab can be targeted to the animal's gum line (top and bottom) and / or can be targeted to the animal's entire mouth. Microbial DNA can be extracted from the oral swab sample to determine which microbial species are present in the feline oral microbiome and in what relative abundance.

[0072] Metagenomic DNA can be extracted from oral samples through heat treatment on a shaker for about 1 hour with or without bead beating, or with or without the addition of a proteolytic reagent such as protease K and a surfactant. In some embodiments, oral samples are heat treated at about 45°C to 75°C, e.g., 50°C, 55°C, 60°C, 65°C, 70°C, or within a range defined by any two of the preceding values.

[0073] The gold standard for comprehensive studies of the microbiome is shotgun metagenomic sequencing, which can capture whole or near-complete genomes of organisms across all life domains, not just bacteria and archaea. The gold standard for metagenomic DNA extraction involves a process called bead-beating. This process is recommended for complete microbial cell lysis when studying microbiome abundance and composition. This process helps to break down thick cell walls, such as those of Gram-positive bacteria. This process is performed by rapidly agitating the sample using grinding media (balls or beads) in a bead beater.

[0074] In an exemplary embodiment, the disclosed system and method do not use bead-beating for metagenomic DNA extraction, and such a process has been intentionally abandoned. The reason is that bead-beating also introduces a significant amount of DNA degradation that interferes with downstream sample processing, which may reduce the quality of the generated metagenomic sequencing library. According to an embodiment, since the disclosed system and method do not use bead-beating, the oral microbiome data in the result analysis may suffer from the problem of Gram-positive bacteria being underrepresented. Nevertheless, it becomes possible to recognize characteristic patterns of diseases.

[0075] After heat treatment of the oral samples, metagenomic DNA can be extracted by SPRI magnetic bead-based DNA extraction (MCLAB, MBC-200) using 80% ethanol for purification. The DNA can be quantified using a GloMax plate reader (Promega). After extraction and quantification of metagenomic DNA, oral samples can be prepared for NGS using LOTUS DNA Library Preparation Kit (IDT), Next Ultra II FS DNA Library Preparation Kit (NEB) or another ligation or tagmentation-based DNA library preparation kit according to the manufacturer's instructions. Oral samples can be dual barcoded using iTRU indexes. Prepared sequencing libraries can be quantified using a GloMax plate reader (Promega) and collected into 96 sample pools with equal mass. The pools can then be visualized (assessing fragment size distribution) and quantified using a 2100 Bioanalyzer instrument (Agilent). Following standard QC steps, the 96 sample pool can be loaded onto an Illumina HiSeq X or NovaSeq 6000 next generation sequencing machine.

[0076] The raw sequence data can be demultiplexed and trimmed to remove low quality data, for example, using the Trimmomatic 0.32 program. The data can then be mapped to the latest version of the Felis_catus_9.0 genome, for example. In all oral samples, there may be approximately 5-7% sequence reads that are not mapped to the Felis genome. The unmapped reads can 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 sequence analysis data of metagenomic samples, can be used on the sequenced data in conjunction with the KRAKEN2 analysis. Bracken can output total read counts to the species level. Based on the results of the KRAKEN2 metagenomic sequence classifier and the Bracken calculation, a feline oral microbial profile can be generated. The generated oral microbial profile can include data regarding the identity of the microbial species present and their relative abundance. The generated oral microbial profile can also include data regarding the percentage of gram-positive bacteria.

[0077] A confidence score of about 0.1 (e.g., 0.08-0.15) can be used as a cutoff (or threshold) for the KRAKEN2 classification algorithm. All samples with less than 10,000 or more than 500,000 classified microbial reads can be filtered out. Reads for microbial species with an average of less than 10 reads but not 0 can also be filtered out.

[0078] [Display and comparison method] An indication as to whether a cat is suffering from one or more diseases (e.g., renal / urinary disease, inflammatory disease, or endocrine disease) relies on a comparison of the pet's current oral microbiome status to the oral microbiomes of cats whose owners have reported that their cats have veterinary-diagnosed IBD, DM, CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, or idiopathic cystitis. Such comparisons are based on the compositional abundance of microorganisms measured by assays that are predictive of each disease.

[0079] A computational analysis of the compositional abundance of various microorganisms present in the oral microbiome involves comparing the sample to a database of samples from cats known to suffer from various diseases, as well as cats that do not suffer from any known renal / urological diseases, IBD or DM. In other words, the computational analysis compares the oral microbiome identified from the oral swab sample to established microbial profiles contained in a reference database (discussed in detail above).

[0080] In some embodiments, a method for indicating feline renal / urinary disease includes receiving an oral swab sample from a cat, heat-treating the oral sample, and performing a magnetic bead-based deoxyribonucleic acid (DNA) extraction on the heat-treated oral sample to extract microbial DNA present in the oral swab sample, sequencing the microbial DNA to identify which specific one or more microorganisms are present in the oral sample and in what proportion (i.e., abundance), generating a microbial profile for the cat with the identification of the specific one or more microorganisms and their abundance, and comparing the oral microbial profile for the cat to a database of established microbial profiles, wherein the database identifies a correlation between (i) a profile including one or more microorganisms and (ii) a corresponding renal / urinary disease.

[0081] Based on the comparison of the feline's oral microbial profile to a database of established microbial profiles, the method may further include generating a risk score indicative of the likelihood that the cat has a particular renal / urinary disease. The risk score may correlate to a stage or severity of the disease state (e.g., a higher risk score associated with stage 2 CKD).

[0082] In some embodiments, a method for indicating feline renal / urinary disease includes receiving an oral swab sample from a cat, heat-treating the oral sample, and performing a magnetic bead-based deoxyribonucleic acid (DNA) extraction on the heat-treated oral sample to extract microbial DNA present in the oral swab sample. The method may also include sequencing the microbial DNA to identify the presence of any one or more specific microorganisms in the oral sample, where the identification of the one or more specific microorganisms and their abundance generates an oral microbial profile for the cat.

[0083] The method may further include the steps of comparing the oral microbial profile of the cat with a database of established microbial profiles, the database identifying a correlation between (i) a profile comprising one or more microorganisms and (ii) a corresponding renal / urinary tract disease; generating a risk score indicating the likelihood that the cat is suffering from a specific renal / urinary tract disease based on a result of comparing the oral microbial profile with the database of established microbial profiles; and, having generated the risk score and identified the specific renal / urinary tract disease, administering a therapeutic treatment designed to treat the specific renal / urinary tract disease.

[0084] In some embodiments, the therapeutic treatment can include administering a therapeutic compound, for example a compound designed to inhibit or promote the growth of one or more specific microbial species present in the oral microbiome of the cat. In some embodiments, the therapeutic compound includes a prebiotic, a postbiotic, a probiotic, a drug, or a combination thereof. In some embodiments, the therapeutic treatment can include brushing the cat's teeth with a topical treatment.

[0085] In some embodiments, the therapeutic compounds include phosphate binders, antibiotics, compounds that regulate feline hypertension and / or blood pressure, and erythropoietin, among other therapeutic compounds. In some embodiments, the therapeutic treatment can include a diet designed to resolve and / or alleviate the condition of renal / urinary disease. For example, a therapeutic diet with restricted protein, phosphorus, and sodium content and high water-soluble vitamins, fiber, and antioxidant concentrations can extend the lifespan and improve the quality of life of cats suffering from CKD. In some embodiments, the diet can include switching to wet food to help maintain the cat's adequate hydration. The therapeutic treatment can include potassium supplementation.

[0086] In some embodiments, the therapeutic treatment protocol is designed to modify the composition of the feline oral microbiome. In some embodiments, modifying the feline oral microbiome composition treats and / or resolves certain renal / urinary diseases. In some embodiments, the therapeutic treatment restores the feline oral microbiome. In some embodiments, restoring the feline oral microbiome makes the feline oral microbiome more consistent with a healthy feline oral microbiome (or an established oral microbiome) in terms of both the specific microbial species or species present and their relative abundance. In some embodiments, the therapeutic treatment protocol is designed to maintain the composition of the feline oral microbiome. In some embodiments, the therapeutic treatment protocol is designed to stimulate the metabolic output of the feline oral microbiome. Stimulating the metabolic output of the feline oral microbiome may include using known enzyme pathway analysis tools to provide additional perspective to existing microbial composition data to further characterize disease indications and improve predictive disease models. EXAMPLES

[0087] To disclose the construction of a computational renal / urological, inflammatory and endocrine disease classification algorithm, pairwise log-ratio (PLR) transformation was performed on the species-level read total counts output by Bracken. Bracken is a statistical method to calculate species abundance in DNA sequence data of metagenomic samples. Significant PLR comparisons (threshold p-value < 0.01) between the control and disease groups were then confirmed by performing a z-test. The transformed data can be stored in a database. The healthy cohort was compared with the CKD, SUCs, UCOCS, CUCS and IC cohorts. The healthy cohort was also compared with the IBD and DM cohorts (see Figures 8A-9B).

[0088] The frequency of each microbial species in all significant PLRs was assessed. Only microbial species that were significant in more than 50% of the maximum possible comparisons with other species were retained. This measure was used as a surrogate for the importance of various microbial species in the five renal / urological disease states of interest, inflammatory diseases (IBD) and endocrine diseases (DM). These microbial species are the "predicted microbial species" for each renal / urological disease.

[0089] To identify microbial compositional abundance patterns across distinct populations of CKD, struvite urinary crystals or stones, calcium oxalate urinary crystals or stones, cystine urinary crystals or stones, and idiopathic cystitis, each disease was scored by comparing the predicted pairwise logarithmic ratio (pPLR) of each sample to the mean pPLR of the control group, taking into account the direction and magnitude of the difference. Figures 2A-2E show the distribution of the mean logarithmic ratio difference scores between pairwise microbial interactions associated with CKD and healthy cohorts, struvite urinary crystals or stones and healthy cohorts, calcium oxalate urinary crystals or stones and healthy cohorts, cystine urinary crystals or stones and healthy cohorts, and idiopathic cystitis and healthy cohorts. Figures 8A-8B show the distribution of the mean logarithmic ratio difference scores between pairwise microbial interactions associated with DM and healthy cohorts, and IBD and healthy cohorts.

[0090] Next, we fitted five Gaussian mixture models (one model per kidney / urological disease), each with two components (healthy and kidney / urological disease cohorts), to the distribution of mean log ratio difference scores between pairwise microbial interactions. This modeling approach produces a score between 0 and 1 for each sample, indicating the probability that the sample belongs to the control cohort or each kidney / urological disease cohort. Figure 3A-E graphically illustrates the likelihood that a sample belonging to the five kidney / urological disease cohorts and a control sample would be classified as belonging to each of these cohorts based on the compositional abundances for each sample's predicted microbes. In all cases, we observed a bimodal probability distribution between kidney / urological disease and control groups that was consistent with sample identity. In all five cases, there were a small number of diseased samples forming a small peak close to 0 and a small set of control samples forming a slight peak close to 1. 9A-9B graphically illustrate the likelihood that a sample belonging to an IBD or DM cohort and a control sample would be classified as belonging to each of these cohorts based on the compositional abundance of the predicted microorganisms in each sample.

[0091] This graph suggests that a small number of cats in the renal / urological disease, IBD and DM cohorts may actually be healthy or in remission (due to old, incorrect or incomplete health information provided by pet owners), while some cats in the control cohort may have renal / urological, inflammatory or endocrine diseases that have not yet been diagnosed or detected. The sensitivity (ability to detect cats known to have the disease) and specificity (ability to detect cats in the control cohort as not having the disease) of the risk classification method for each disease was tested (see Figures 3A-E and 9A-B). The sensitivity of the method was highest for cystine urinary crystals or stones and lowest for IBD, while the specificity was highest for DM and lowest for cystine urinary crystals or stones.

[0092] A sizable cohort of domestic cats (n=3,929) was used to develop the reference database, whose health history data was provided by the pet owners. Pet owners were asked whether their cats had been diagnosed by a veterinarian with CKD, SUCS, CUCS, UCOCS, or IC, and so some diagnostic accuracy must have been compromised, as some of the diagnostic accuracy was conveyed by the pet owners. To mitigate this issue and limit cases in which cats reported by their pet owners as healthy (i.e., without known systemic or renal / urinary disease) had actually begun to develop an undiagnosed disease, an age limit of 1–3 years was placed on the control healthy cohort. This limit was set because the association between age and renal / urinary and systemic disease is well established. Cats under 1 year of age were purposely excluded from this group to avoid any potential oral microbiome bias specific to kittens. The healthy control cohort may be biased towards the oral microbiome of younger cats and may not be representative of older cats without renal / urinary or systemic disease.

[0093] While an age limit was set for the control healthy cohort, in some embodiments of the present invention, no age limit is set. In some embodiments, the age of the cat is included as a factor to distinguish the risk of the cat having or developing a renal / urinary disease condition. In some embodiments, age can affect the grouping of the cohorts, with older cats potentially being in a different cohort than younger cats for the same renal / urinary disease. In some embodiments, age is a factor that is applied to the risk assessment of the cat after comparing the oral microbial profile of the cat to the (healthy and pathological) cohorts. In some embodiments, age is included in the oral microbial profile acquired and generated for the cat.

[0094] In addition to the above, microorganisms identified as associated with or predictive of disease can further predict the stage or grade of the disease. For example, a predictive microorganism subgroup for CKD can be indicative of stage 2 CKD. Early detection of disease stages can provide more treatment options. Thus, the use of predictive microorganism subgroups for early detection of disease stages is beneficial for cats and their owners, as unhealthy cats can be brought to the hospital before the disease progresses to an untreatable level. Veterinarians can also benefit by being able to better select treatment options for cats and their owners based on the stage or grade of the disease.

[0095] [Study 1] After obtaining written consent (via email) from the pet owners, 32 feline oral swab samples were collected from cats with various stages of CKD, collected at home by the pet owners using a DNAGenotek PERFORMAGENE P-100 collection device. The same approach was used to collect oral swab samples from 15 healthy cats. Each cat participating in the study was accompanied by up-to-date veterinary records. To participate in the study, cats in the CKD cohort were required to have clinical records clearly mentioning a CKD diagnosis, and cats in the healthy cohort were required to have clinical records within the past 6 months documenting the absence of any diagnosis for chronic or acute disease.

[0096] After DNA extraction from these samples, shotgun metagenomic sequencing was performed and the data analyzed using the computational renal / urological disease risk assessment methods and / or computer systems described above. The algorithms generated CKD risk assessments for the two cohorts.

[0097] The mean-generated oral microbiome-based CKD risk assessment (i.e., risk score) was significantly higher in the CKD cohort compared to the healthy cohort (p<0.05). Figure 6 illustrates these results. Horizontal lines indicate the mean risk score for each cohort (risk scores range from 0 to 1, with higher values ​​indicating increased risk of disease), and error bars indicate the standard error of the mean (SEM). Two-tailed t-tests assuming unequal variance were used for each comparison. *p<0.05.

[0098] [Study 2] After obtaining written consent from pet owners, licensed veterinary technicians collected oral swab samples during veterinary clinic visits from cats diagnosed with stage 1 or stage 2 CKD. A DNAGenotek PERFORMAGENE P-100 collection device was used. After DNA was extracted from these samples, shotgun metagenomic sequencing was performed and the data was analyzed using the computational renal / urological disease risk assessment methods and / or computer systems previously described. The algorithm generated a CKD risk assessment for each sample. The algorithm classified stage 1 CKD cats as at low risk for the disease and stage 2 CKD cats as at medium or high risk for the disease. The results are summarized in Figure 7.

[0099] In addition to the above, for example, microorganisms identified as associated with or predictive of CKD can further predict the stage or grade of CKD. For example, a predictive microorganism subgroup for CKD can be indicative of stage 2 CKD. Early detection of renal / urological disease stage can further expand treatment options. Thus, the use of predictive microorganism subgroups for early detection of renal / urological disease stage is useful for cats and their owners, as unhealthy cats can be brought to the hospital before the disease progresses to an untreatable level. Veterinarians can also benefit by being able to better select treatment options for cats and their owners based on the stage or grade of the disease. Similarly, predictive microorganism subgroups for IBD and DM can be used to indicate various stages or severity of the disease.

[0100] [Discussion] Various inflammatory, endocrine, renal and urinary diseases progress through stages or grades. Diseases such as IBD are known to gradually worsen with the onset of more severe symptoms and become more difficult to treat. CKD is usually in 4 stages, with stage 3 usually being the stage at which a cat is officially diagnosed with the disease. To officially diagnose stages 1 and 2 of CKD, your veterinarian will perform a physical exam and may also perform blood or other tests. During the physical exam, your veterinarian may look for obvious kidney abnormalities, evidence of weight loss, dehydration, pale mucous membranes, uremic ulcers, and evidence of hypertension (i.e. retinal hemorrhage / detachment). Your veterinarian may also measure symmetric dimethylarginine (SDMA) levels in the blood, as SDMA is considered an early detection blood marker.

[0101] To officially diagnose the later stages of CKD (i.e., stages 3 and 4), your veterinarian may measure creatine and SDMA levels in the blood. The specific gravity of your cat's urine may also be measured as part of the diagnosis. Depending on the stage of the disease, treatment protocols may vary. For example, once stage 1 CKD is diagnosed, there are a variety of treatment and prevention options. Specifically, SDMA and creatine levels can be monitored over time, dietary adjustments can be made to manage high blood pressure and phosphorus levels, and investigations can be performed for underlying causes. Treatment options may change as your cat progresses through the many stages of CKD.

[0102] The disclosed method and system was successfully used to distinguish cats with no diagnosed renal / urological or systemic disease from cats diagnosed with CKD. Study 1 used citizen science-sponsored feline oral samples, but disease status of all samples was confirmed by the cat's clinical records. The disclosed algorithm generated a significantly higher mean CKD risk assessment (i.e., risk score) for cats diagnosed with CKD compared to CKD risk assessments generated for healthy cats. The fact that a small number of CKD samples were classified as low risk and a small number of healthy samples were classified as high risk in Study 1 likely reflects some of the pitfalls associated with using citizen science data to train disease prediction algorithms. Such pitfalls include the possibility that pet owners may not be fully aware of their cat's disease status and may report cats with undiagnosed disease (e.g., stage 1 CKD) as healthy or report cats in remission as suffering from a particular disease. Future iterations of the CKD training algorithm will include a larger number of clinically recruited samples with reported animal disease status provided directly by veterinarians, which will result in improved specificity and sensitivity of the disclosed prediction algorithm.

[0103] Study 2 established that the disclosed algorithm was unable to classify cats with stage 1 CKD as at risk for the disease. As discussed above, the inability to classify stage 1 cats as having CKD is likely related to the fact that the healthy training cohort used to develop the CKD prediction algorithm may have included cats with early-stage CKD where the cat owners were not yet aware that their cats had kidney disease. However, the disclosed algorithm was able to classify cats with stage 2 CKD as at risk for the disease. Given the fact that most cats with CKD are officially diagnosed with stage 3 disease, the disclosed CKD risk prediction algorithm may be a useful pre-clinical tool that can be used by pet owners to screen for the disease at home or as part of regular veterinary visits by veterinarians. This tool can be used to detect CKD earlier, which can help to envision timely targeted treatment plans to slow the progression of the disease. It is well known that cats diagnosed with stage 2 CKD respond well to renal prescription diets, which can significantly slow the progression of the disease in many cases without the need for further treatment.

[0104] Studies 1 and 2 focused on CKD as a case study. The results of Studies 1 and 2 demonstrate that the disclosed computer systems, systems, algorithms and methods can detect disease states and classify cats according to the disease state and / or the severity or grade of the disease state. It should be understood that the disclosed methods have similar utility and clinical utility in detecting and classifying cats with inflammatory bowel disease, diabetes mellitus, calcium oxalate urinary crystals / stones, struvite urinary crystals / stones, cystine urinary crystals / stones and idiopathic cystitis.

[0105] The risk score generation methodology disclosed herein is based on compositional analysis of the oral microbiome. Other embodiments of the disclosed methods may also include integrating predictions on the metabolic output of the oral microbiome (generated by enzymatic pathway analysis tools or metabolomics) with analysis of the compositional abundance of the oral microbiome for the purpose of predicting risk of renal / urinary diseases. Other embodiments of the disclosed methods may include age as a factor in risk assessment.

[0106] [Additional Terms and Definitions] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0107] Various "aspects" of the invention, including systems, methods and / or articles of manufacture, may be described with reference to one or more "embodiments" that are exemplary in nature. As used herein, the terms "aspect" and "embodiment" may be used interchangeably. The term "embodiment" may also mean "serving as an example, instance, or illustration" and should not necessarily be construed as preferred or advantageous over other aspects disclosed herein. Moreover, references to "embodiments" of the disclosure or invention are intended to provide illustrative examples without limiting the scope of the invention, which is set forth by the appended claims.

[0108] As used herein and in the appended claims, the singular forms "a," "an," and "the" each contemplate, include, and specifically disclose both the singular and plural forms, unless the context clearly indicates otherwise. For example, a reference to a "protein" contemplates and specifically discloses a plurality (e.g., two or more, three or more, etc.) of proteins, as well as one protein. Similarly, the use of a plural referent does not necessarily require a plurality of such referents, but contemplates, includes, specifically discloses, and / or supports a plurality of such referents as well as a single referent, unless the context clearly indicates otherwise.

[0109] As used throughout this specification, the terms "can" and "may" are used in an permissive sense (i.e., meaning that there is a possibility) rather than an obligatory sense (i.e., meaning that there is a need). Additionally, the terms "including," "having," "involving," "containing," "characterized by," and variations thereof (e.g., "includes," "has," "involves," "contains," etc.) and similar terms used herein, including the claims, are inclusive and / or open-ended and have the same meaning as "comprising" and variations thereof (e.g., "comprise" and "comprises") and do not exclude additional elements or method steps not specifically recited by way of example.

[0110] The term "disease" refers to any disorder, disease, injury or illness found or predicted in a patient, as understood by one of skill in the art. Such indications of disease may be early, intermediate or late indications, including pre-disease symptoms, signs or markers, as known in the art. Such predictions of disease may be, but may include, predictions, forecasts, assumptions, estimates, hypothesized and / or inferred onset of the disease, whether based on scientific or medical evidence, risk assessment or mere fear or anxiety.

[0111] The term "patient" as used herein is synonymous with the term "subject" and generally refers to any animal under the care of a medical professional, which term specifically refers to (i) humans (under the care of a doctor, nurse or medical assistant or volunteer) and (ii) non-human animals (under the care of a veterinarian or other veterinary professional, medical assistant or volunteer), e.g., non-human mammals, as defined herein.

[0112] As used herein, "treating" or "treatment" encompasses the treatment of a disease or condition of interest in a cat having the disease or condition of interest, and includes (i) preventing the onset of the disease or condition in such cat, particularly if the cat has actually begun to develop the disease but has not yet been diagnosed with the disease; (ii) inhibiting the disease or condition, i.e., arresting its onset; (iii) relieving the disease or condition, i.e., inducing regression of the disease or condition; or (iv) relieving symptoms caused by the disease or condition, i.e., relieving pain without a solution to the underlying disease or condition. As used herein, the terms "disease" and "disease" may be used interchangeably, or may differ in that a particular disease or condition does not have a known causative agent (and thus the etiology has not yet been resolved) and is therefore not recognized as a disease, but only as an undesirable disease or syndrome in which a specific set of symptoms may be identified by a clinician to a greater or lesser extent.

[0113] For brevity, the invention may refer to lists or ranges of numerical values. However, when such a list or range of numerical values ​​(e.g., greater than, less than, up to, at least, and / or about a particular value and / or between two recited values) is disclosed or recited, any particular value or range of values ​​falling within the disclosed value or recited list or range is also specifically disclosed and contemplated herein.

[0114] To facilitate understanding, similar reference numbers (i.e., similar names of components and / or components) have been used, where possible, to designate similar components common to different embodiments of the present invention. Similarly, similar components or components having similar functions will be provided with similar reference designators, where possible. Specific language will be used herein to describe exemplary embodiments. However, no attempt is made to limit the scope of the present invention. Rather, the language used to describe exemplary embodiments is merely exemplary and should not be construed as limiting the scope of the present invention (unless such language is expressly set forth herein as essential).

[0115] Although the detailed description has been divided into sections, the section titles and tables of contents within each section are for organizational purposes only and are not intended as stand-alone detailed descriptions and embodiments, or as limiting the scope of the detailed description or the claims. Rather, the contents of each section within the detailed description are intended to be read and understood as a collective whole, where elements of one section may relate to and / or provide information for other sections. Thus, an embodiment specifically disclosed in one section may also relate to and / or serve as additional and / or alternative embodiments in another section having the same and / or similar products, methods and / or terminology.

[0116] Although certain embodiments of the present invention will be described in detail with reference to specific configurations, parameters, components, ingredients, and the like, these descriptions are illustrative and should not be construed as limiting the scope of the claimed invention.

[0117] Furthermore, for any given component of the components of the described embodiments, it should be understood that any possible alternatives listed for that component or components can generally be used separately or in combination with each other, unless otherwise indicated, either implicitly or explicitly.

[0118] Furthermore, unless otherwise expressly stated, numbers expressing quantities, components, distances or other measurements used in the specification and claims should be understood as being optionally modified by the term "about" or its equivalents. When terms such as "about," "approximately," "substantially," and the like are used in conjunction with a stated quantity, value, or condition, they can be deemed to mean an amount, value, or condition that deviates from the stated quantity, value, or condition by less than 20%, less than 10%, less than 5%, less than 1%, less than 0.1%, or less than 0.01%. At the very least, and without any attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

[0119] Any headings and subheadings used herein are for organizational purposes only and are not intended to limit the scope of the description or the claims. It should also be noted that the singular forms "a," "an," and "the" used in this specification and the appended claims do not exclude plural referents unless the context clearly dictates otherwise. Thus, for example, an embodiment referring to a single referent (e.g., a "widget") may also include two or more such referents.

[0120] It will also be understood that the embodiments described herein may include attributes and / or features (e.g., components, components, parts, elements, parts and / or portions) described in one or more individual embodiments, and are not necessarily strictly limited to the features explicitly described in that particular embodiment. Thus, various features of the provided embodiments can also be combined and / or integrated with other embodiments of the invention. Thus, the disclosure of specific features for a particular embodiment of the invention should not be construed as limiting the application or inclusion of said features for that particular embodiment. Rather, it will be understood that other embodiments can also include such features.

[0121] [table] Table 1: Microorganisms predicting chronic kidney disease (CKD), cystine urinary crystals / stones (CUCS), struvite urinary crystals / stones (SUCS), calcium oxalate urinary crystals / stones (UCOCS), and idiopathic cystitis (IC).

[0122] [Table 1-1]

[0123] [Table 1-2]

[0124] [Table 1-3]

[0125] [Table 1-4]

[0126] [Table 1-5]

[0127]

Table 1-6

[0128]

Table 1-7

[0129]

Table 1-8

[0130]

Table 1-9

[0131]

Table 1-10

[0132] Table 2: Microorganisms predicting true diabetes mellitus (DM) and inflammatory bowel disease (IBD).

[0133]

Table 2-1

[0134]

Table 2-2

[0135]

Table 2-3

[0136]

Table 2-4

[0137]

Table 2-5

[0138] Table 3: Predicted microorganisms for DM and their taxonomic classification. Of the total 53 predicted microorganisms for DM (see Table 2), about 9.43% are Gram-positive bacteria. "Candidatus" refers to bacteria that are well characterized but have not yet been cultured.

[0139] [Table 3]

[0140] Table 4: Predicted microorganisms for IBD and their taxonomic classification. Of the total 116 predicted microorganisms for IBD (see Table 2), approximately 18.1% are Gram-positive bacteria. "Candidatus" refers to bacteria that are well characterized but have not yet been cultured.

[0141] [Table 4]

[0142] Table 5: Predicted microorganisms for struvite urinary crystals / stones (SUCS) and their taxonomic classification. Of the total 94 predicted microorganisms for SUCS (see Table 1), approximately 13.83% are Gram-positive bacteria. "Candidatus" refers to bacteria that are well characterized but have not yet been cultured.

[0143] [Table 5]

[0144] Table 6: Predicted microorganisms for idiopathic cystitis (IC) and their taxonomic classification. Of the total 94 predicted microorganisms for IC (see Table 1), approximately 8.51% are Gram-positive bacteria. "Candidatus" refers to bacteria that are well characterized but have not yet been cultured.

[0145] [Table 6]

[0146] Table 7: Predicted microorganisms for cystine urinary crystals or stones (CUCS) and their taxonomic classification. Of the total 90 predicted microorganisms for CUCS (see Table 1), about 12.22% are Gram-positive bacteria. "Candidatus" refers to bacteria that are well characterized but have not yet been cultured.

[0147] [Table 7]

[0148] Table 8: Predicted microorganisms for chronic kidney disease (CKD) and their taxonomic classification. Of the total 110 predicted microorganisms for CKD (see Table 1), about 14.55% are Gram-positive bacteria. "Candidatus" refers to bacteria that are well characterized but have not yet been cultured.

[0149] [Table 8]

[0150] Table 9: Predicted microorganisms for calcium oxalate urinary crystals or stones (UCOCS) and their taxonomic classification. Of the total 56 predicted microorganisms for UCOCS (see Table 1), about 5.36% are Gram-positive bacteria. "Candidatus" refers to bacteria that are well characterized but have not yet been cultured.

[0151] [Table 9]

[0152] Table 10: Increase or decrease in relative abundance of each predictive microorganism for diabetes mellitus (DM).

[0153] [Table 10-1]

[0154] [Table 10-2]

[0155] Table 11: Increase or decrease in relative abundance of each predictive microorganism for inflammatory bowel disease (IBD).

[0156] [Table 11-1]

[0157] [Table 11-2]

[0158] [Table 11-3]

[0159] [Table 11-4]

[0160] Table 12: Increase or decrease in relative abundance of each predictive microorganism for chronic kidney disease (CKD).

[0161] [Table 12-1]

[0162] [Table 12-2]

[0163] [Table 12-3]

[0164] Table 13: Increase or decrease in relative abundance of each predictive microorganism for struvite urinary crystals / stones (SUCS).

[0165] [Table 13-1]

[0166] [Table 13-2]

[0167] [Table 13-3]

[0168] Table 14: Increase or decrease in relative abundance of each predictive microorganism for calcium oxalate urinary crystals or stones (UCOCS).

[0169] [Table 14-1]

[0170] [Table 14-2]

[0171] Table 15: Increase or decrease in relative abundance of each predictive microorganism for cystine urinary crystals or stones (CUCS).

[0172] [Table 15-1]

[0173] [Table 15-2]

[0174] [Table 15-3]

[0175] Table 16: Increase or decrease in relative abundance of each predictive microorganism for idiopathic cystitis (IC).

[0176] [Table 16-1]

[0177] [Table 16-2]

[0178] [Table 16-3]

[0179] [Conclusion] Although the foregoing detailed description refers to specific exemplary embodiments, the present invention may be embodied in other specific forms without departing from the concept or essential characteristics thereof. The described embodiments are therefore to be considered in all respects only as illustrative and not restrictive. For example, various substitutions, modifications, and / or variations to the features of the present invention described and / or illustrated herein, and additional applications of the principles described and / or illustrated herein, may occur to those skilled in the relevant art and those in possession of the present invention, and may be made to the described and / or illustrated embodiments without departing from the concept and scope of the present invention as defined by the appended claims. Such substitutions, modifications, and / or variations are considered to be within the scope of the present disclosure.

[0180] Accordingly, the scope of the present invention is indicated by the appended claims, rather than by the above detailed description. The limitations set forth in the claims should not be limited to the specific embodiments described in the above detailed description, but should be interpreted broadly in accordance with the language used in the claims, and such embodiments should be interpreted as being exclusive and not exhaustive. All changes that come within the meaning and range of equivalence of the claims are intended to be embraced within their scope.

[0181] It will also be understood that various features of particular embodiments may be compatible with, combined with, included in, and / or integrated with other embodiments of the invention. For example, systems, methods, and / or products according to particular embodiments of the invention may include, integrate, or be configured differently from features described in other embodiments disclosed and / or described herein. Thus, the disclosure of specific features with respect to a particular embodiment of the invention should not be construed as limiting the application or inclusion of said features to that particular embodiment.

[0182] Moreover, features described in various embodiments may be optional and may not be included in other embodiments of the present invention, unless a feature is described as required in a particular embodiment. Furthermore, any feature herein may be combined with any other feature of the same or different embodiments disclosed herein, unless a feature is described as requiring other features in that combination. It will also be understood that while a given feature may be optional in a particular embodiment, if such an embodiment includes a given feature, it is required that they have the particular configuration as described in the present invention.

[0183] Similarly, any steps recited in any method or process described and / or claimed herein may be performed in any suitable order, and are not necessarily limited to the order described and / or recited, unless otherwise indicated (expressly or implicitly). However, such steps may be required to be performed in a particular order, or in any suitable order, in particular embodiments of the invention.

[0184] Moreover, various well-known aspects of example systems, methods, articles of manufacture, etc. have not been described in particular detail herein to avoid obscuring aspects of the example embodiments, but such aspects are nevertheless contemplated herein.

Claims

1. 1. A method for screening, detecting, and / or preventing one or more diseases in a cat, said method comprising: obtaining an oral microbial profile of the cat, the oral microbial profile comprising one or more microbial species present in an oral sample of the cat and the amount or abundance of the one or more microbial species within the oral sample; The oral microbial profile is (i) the incidence and / or prevalence of one or more diseases in cats; and (ii) the presence and / or abundance of different microbial species within the oral microbiome of the feline, wherein the different microbial species comprise one or more microbial species within the oral sample; comparing the weighted correlation between the generating a risk score indicating the likelihood that the cat suffers from one or more diseases based on one or more matches between the oral microbial profile and information in the database; classifying the cat as one that will develop the one or more diseases if the risk score meets or exceeds a predetermined threshold, and optionally prescribing an appropriate therapeutic treatment protocol to treat, alleviate or prevent the onset, progression or recurrence of the one or more diseases if the risk score meets or exceeds a predetermined threshold.

2. The method described in claim 1, wherein the disease is a renal / urinary disease.

3. obtaining an oral microbial profile of the feline includes: acquiring nucleic acid sequence data corresponding to microbial nucleic acids obtained from the oral sample; analyzing the nucleic acid sequence data to identify and quantify one or more microbial species present in the oral sample; generating an oral microbial profile for the feline based on the identified and optionally quantified one or more microbial species; 3. The method of claim 1 or 2, comprising:

4. The step of obtaining microbial nucleic acid sequence data comprises: sequencing the microbial nucleic acids of the oral sample; and optionally, isolating microbial nucleic acids from the oral sample; and optionally, The step of isolating microbial nucleic acids from the oral sample comprises: performing a heat treatment on the oral cavity sample; performing magnetic SPRI bead-based nucleic acid extraction on the heat-treated oral sample in the presence or absence of a protein digestion reagent and a surfactant to extract microbial nucleic acids from the oral sample; The method of claim 3, comprising:

5. The step of comparing the oral microbial profile with information in a database includes: calculating the abundance of one or more microbial species in the oral sample; Identifying one or more microbial species in the oral sample; comparing the abundance of one or more microbial species identified in the oral sample to the presence and / or abundance of various microbial species in the feline oral microbiome; or The step of generating the risk score comprises: Identifying one or more similarities between the compositional abundance of one or more microbial species in the oral sample and the compositional abundance of various microbial species in the feline oral microbiome contained in the database; identifying one or more matches between the identity of one or more microbial species in the oral sample and the presence of various microbial species in the feline oral microbiome contained in the database; Quantifying one or more similarities identified between the compositional abundance of one or more microbial species in the oral sample and the compositional abundance of one or more microbial species in the feline oral microbiome contained in the database; confirming the presence of one or more predicted microbial species in the oral sample; 3. The method of claim 1, comprising one or more steps of:

6. The step of analyzing the microbial nucleic acid sequence data includes: demultiplexing the nucleic acid sequence data; trimming the nucleic acid sequence data; Mapping one or more unmapped reads to a feline reference genome and / or an existing microbial reference genome; classifying one or more reads from the mapped nucleic acid sequence data as feline; classifying one or more reads from the mapped nucleic acid sequence data as a microorganism; quantitating the one or more microbial reads; transforming the quantified one or more microbial reads using a method such as pairwise log-ratio transformation to account for sequence coverage bias; comparing the compositional abundance patterns of the transformed one or more microbial reads against compositional abundance patterns of transformed data from a reference database that includes samples from cats that are not affected by the one or more diseases, as well as samples from cats that are affected by the particular disease; The method of claim 3 , comprising one or more steps of:

7. A method as described in claim 1, comprising the following (a), (b) or (c): (a) the one or more diseases are selected from the group consisting of inflammatory bowel disease, diabetes mellitus, chronic kidney disease, cystine urinary crystals or stones, calcium oxalate urinary crystals or stones, struvite urinary crystals or stones, and idiopathic cystitis. (b) generating a report that provides (i) the risk score, (ii) an indication for developing one or more diseases if the risk score meets or exceeds a predetermined threshold, (iii) a timing recommendation, (iv) optionally, one or more home practice methods for improving health, (v) optionally, one or more diagnostic steps for diagnosing one or more diseases if the risk score meets or exceeds a predetermined threshold, and (vi) optionally, a prescription for a therapeutic treatment protocol; and, optionally, transmitting the generated report to the cat's owner and / or their veterinarian in an electronic document; Further comprising: (c) the therapeutic treatment protocol is sufficient to alter the oral microbial profile of the cat.

8. A method as described in claim 2, comprising the following (a), (b) or (c): (a) the one or more kidney / urinary diseases are selected from the group consisting of chronic kidney disease, cystine urinary crystals or stones, calcium oxalate urinary crystals or stones, struvite urinary crystals or stones, and idiopathic cystitis. (b) generating a report that provides (i) the risk score, (ii) an indication for developing one or more kidney / urinary diseases if the risk score meets or exceeds a predetermined threshold, (iii) a timing recommendation, (iv) optionally, one or more home practice methods for ameliorating the kidney / urinary diseases, (v) optionally, one or more diagnostic steps for diagnosing one or more kidney / urinary diseases if the risk score meets or exceeds a predetermined threshold, and (vi) optionally, a prescription for a therapeutic treatment protocol; and, optionally, transmitting the generated report to the cat's owner and / or their veterinarian in an electronic document; Further comprising: (c) the therapeutic treatment protocol is sufficient to alter the oral microbial profile of the cat.

9. 1. A computer system configured to display or predict one or more disease states of a cat, comprising: one or more processors; and one or more computer-readable hardware storage devices; The one or more computer-readable hardware storage devices include: the computer system, receiving microbial nucleic acid sequence data corresponding to microbial nucleic acids obtained from an oral sample collected from the cat; analyzing 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; generating an oral microbial profile for the feline based on the identified one or more microbial species and their respective abundances; The oral microbial profile is (i) the incidence and / or prevalence of one or more disease states in cats; and (ii) the presence and / or abundance of different microbial species within the oral microbiome of the feline, wherein the different microbial species comprise one or more microbial species within the oral sample; Compare with database information to check weighted correlations between identifying one or more matches between the oral microbial profile and information in a database; generating a risk score indicative of the likelihood that the cat has one or more disease states based on one or more matches between the oral microbial profile and information in the database; and optionally, If the risk score meets or exceeds a predetermined threshold, the cat is diagnosed as having one or more of the disease states; If the risk score meets or exceeds a predetermined threshold, prescribing an appropriate therapeutic treatment protocol to treat or prevent the one or more disease states; generating a report displaying (i) the risk score, (ii) an indication for developing one or more disease states if the risk score meets or exceeds a predetermined threshold, (iii) a timing recommendation, (iv) optionally, one or more home practice regimens for ameliorating the disease state, (v) optionally, one or more diagnostic steps for diagnosing one or more disease states if the risk score meets or exceeds a predetermined threshold, and (vi) a prescription for a therapeutic treatment protocol; and / or and instructions executable by the one or more processors for configuring transmission of the generated report to the cat owner and / or their veterinarian in an electronic document. Computer system.

10. The disease state is a renal / urinary disease.

10. The computer system of claim 9.

11. the instructions further configure the computer system to map one or more unmapped reads to a feline reference genome and / or map one or more reads to a microbial reference genome, and optionally classify the reads as microbial or feline; optionally, the instructions further configure the computer system to validate at least one unmapped sequence read in the metagenomic sequence data, and optionally classify the at least one unmapped read; or A comparison of the feline's oral microbial profile against a database of established microbial profiles excludes feline oral microbiome samples with fewer than 10,000 classified microbial reads or more than 500,000 classified microbial reads.

11. A computer system according to claim 9 or 10.

12. The instructions further configure the computer system to calculate the abundance of one or more microbial species present in the oral sample, and optionally: (a) the abundance of one or more specific microbial species present in the oral sample correlates with whether the one or more specific microbial species are predictive microbial species for a particular disease state; or (b) the instructions further configure the computer system to perform a pairwise log ratio comparison of the microbial abundance of the corresponding feline oral sample against information in the database; and optionally, (c) one or more particular microbial species become predicted microbial species when 50% or more of the maximum possible pairwise log ratio comparisons associated with that microorganism are significantly different when comparing the diseased cohort with the control cohort.

13. 1. A method for predicting the onset of a renal / urinary disease, an inflammatory disease and / or an endocrine disease in a cat, said method comprising: isolating microbial nucleic acids of one or more microbial species from an oral sample containing the one or more microbial species obtained from the cat; obtaining microbial nucleic acid sequence data corresponding to the microbial nucleic acid; analyzing the microbial nucleic acid sequence data to identify one or more microbial species present in the oral sample and, optionally, to quantify the one or more microbial species; generating an oral microbial profile for the cat based on the identified (optionally quantified) one or more microbial species, an oral microbial profile comprising the one or more microbial species, and optionally, the amount or relative abundance of the one or more microbial species in the oral sample; The oral microbial profile is (i) the incidence and / or prevalence of one or more renal / urinary, inflammatory and / or endocrine disorders in cats; and (ii) the presence and / or abundance of different microbial species within the oral microbiome in an animal of the feline classification, wherein the different microbial species comprise one or more microbial species within the oral sample; comparing the weighted correlation between the generating a risk score indicative of the likelihood of the cat developing one or more renal / urinary diseases, inflammatory diseases, and / or endocrine diseases based on one or more matches between the oral microbial profile and information in the database; If the risk score meets or exceeds a predetermined threshold, indicating that the cat will develop the one or more renal / urinary diseases, inflammatory diseases, and / or endocrine diseases; A method comprising:

14. The renal / urinary tract disease, inflammatory disease and / or endocrine disease is a renal / urinary tract disease. The method of claim 13.

15. The method of claim 13 or 14, wherein the feline oral microbial profile further comprises a certain proportion of gram-positive microorganisms, and wherein the various microbial species within the oral microbiome in animals of the feline classification further comprise a certain proportion of gram-positive microorganisms.

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