Biomarkers for paratuberculosis

Abstract

The present disclosure relates to miRNA markers for a Mycobacterium avium subspecies paratuberculosis infection in an animal.

Description

[0001] BIOMARKERS FOR PARATUBERCULOSIS

[0002] FIELD OF THE INVENTION

[0003] The present disclosure relates to miRNA markers for a Mycobacterium avium subspecies paratuberculosis infection in an animal.

[0004] BACKGROUND OF THE INVENTION

[0005] Paratuberculosis (also known as Johne’s disease (JD)) is a chronic wasting disease caused by Mycobacterium avium subspecies paratuberculosis (MAP). The disease impacts a wide range of ruminants, including cattle, sheep, lamas, deer and bison, and is commonly observed in dairy cattle. The disease is endemic in most parts of the world and is most prevalent in the United States and Europe. JD causes an estimated >AUD$4 billion in production losses each year. While the impact of JD on individual herds is modest when present at low prevalence, disease control measures, including vaccination, test and cull programs, or a combination of the two, are cost effective.

[0006] Detecting early stage MAP infection is challenging. The disease is transmitted from dams to calves either in utero or during the early stages of life through the faecal- oral route. Calves are considered particularly susceptible to infection due to their immature immune system. Older animals are refractory to MAP infection. During the early stages of infection, a cellular immune response results in the formation of clusters of white blood cells with engulfed MAP particles, known as granulomas, lodging in the animal’ s gut. Shedding of the bacteria is observed intermittently over the next few years, while seroconversion is observed 2-3 years post infection. These factors contribute to the latency period of JD in dairy cattle of being 2-5 years, during which time existing ante-mortem diagnostic tests (faecal culture, faecal PCR or ELISA (either blood- or milkbased) show a diagnostic sensitivity of 0.5 or less.

[0007] JD was first described over 120 years ago, and shortfalls of existing diagnostics are well established. Thus, there is a need for further methods of determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal.

[0008] SUMMARY OF THE INVENTION

[0009] The present inventors investigated microRNAs as biomarkers for a Mycobacterium avium subsp. paratuberculosis infection.

[0010] In an aspect, the present invention provides a method of determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal, the method comprising, in a biological sample from the animal, detecting a level of at least one miRNA selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR- 126-5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let-7f, let-7g, let-7i, miR-100, miR- 10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR- 12030, miR-1246, miR-125b, miR-1306, miR-138, miR-1388-5p, miR-139, miR-142- 5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151-5p, miR-15a, miR-15b, miR- 16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR-1839, miR-185, miR-186, miR- 18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR-195, miR-204, miR-205, miR- 20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR-2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR- 31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362- 5p, miR-374b, miR-378, miR-423-5p, miR-425-5p, miR-4286, miR-451, miR-484, miR- 486, miR-499, miR-532, miR-574, miR-6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR-99a-5p and miR-99b.

[0011] In an embodiment, the method determines the likelihood the animal has, or will develop, Johne’s disease.

[0012] Thus, the present invention further provides a method of determining the likelihood that an animal has Johne’s disease, the method comprising, in a biological sample from the animal, detecting a level of at least one miRNA selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let-7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR-12030, miR-1246, miR-125b, miR-1306, miR-138, miR-1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR- 150, miR-151-5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR- 181b, miR-1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR- 193a-5p, miR-195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR- 2284w, miR-2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR- 30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR- 335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR-425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR-6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR-99a-5p and miR-99b.

[0013] In a further aspect, the present invention provides a method of determining if an animal has Mycobacterium avium subsp. paratuberculosis infection, the method comprising, in a biological sample from the animal, detecting a level of at least one miRNA as defined herein.

[0014] In a further aspect, the present invention provides a method of determining if an animal has Johne’s disease, the method comprising, in a biological sample from the animal, detecting a level of at least one miRNA as defined herein.

[0015] In an embodiment, the method comprises comparing the level of the at least one miRNA to a reference value. In an embodiment, the reference value is a predetermined level of the at least one miRNA, a predetermined score, the level of the at least one miRNA in a control sample, or the level of the at least one miRNA in an animal who does not have a Mycobacterium avium subsp. paratuberculosis infection.

[0016] In an embodiment, the method comprises comparing the level of a panel of miRNAs to a reference value. In an embodiment, the reference value is a predetermined level of the panel, a predetermined score, the level of the panel in a control sample, or the level of the panel in an animal who does not have a Mycobacterium avium subsp. paratuberculosis infection.

[0017] In an embodiment, the method comprises assigning a score for the sample based on the level of the at least one miRNA. In an embodiment, the score is assigned using a miRNA analysis algorithm.

[0018] In an embodiment, the method further comprises normalizing the level of the at least one miRNA to obtain a normalized level of the at least one miRNA, and wherein the method comprises comparing the normalised level of the at least one miRNA to the reference value of the at least one miRNA.

[0019] In an embodiment, the method comprises correlating the level of the at least one miRNA in the sample to a likelihood of the animal being infected with Mycobacterium avium subsp. paratuberculosis by applying the level of the at least one miRNA to a supervised learning model.

[0020] In an embodiment, the supervised learning model was trained on a data set of miRNAs selected by applying recursive feature elimination.

[0021] In an embodiment hyperparameters of the supervised learning model were tuned using GridSearchCV.

[0022] In an embodiment the animal is determined to likely be infected with Mycobacterium avium subsp. paratuberculosis if the supervised learning model outputs a value exceeding a predefined threshold.

[0023] In an embodiment, the method has an accuracy of at least 75%, or at least 80%, or at least 85%, or at least 90%, or at least 95%, at least 97%, or at least 99%. In an embodiment, the method can identify a Mycobacterium avium subsp. paratuberculosis infection with at least 90% precision, at least 93% precision, at least 95% precision, or at least 98% precision.

[0024] In an embodiment, the method can identify a Mycobacterium avium subsp. paratuberculosis infection with at least 90% precision, at least 93% precision, at least 95% precision, or at least 98% precision compared to a non-Mycobaclerium avium Johne’s disease causing pathogen.

[0025] In an embodiment, the area under the curve (AUC) of the at least one miRNA is one or more of at least 0.65, at least 0.7, at least 0.75, at least 0.80, at least 0.85, at least 0.90, at least 0.95 or at least 0.98.

[0026] In an embodiment, when the method comprises detecting the level of three miRNAs, four miRNAs, five miRNAs, at least three miRNAs, at least four miRNAs, or at least five miRNAs, the method has one or more of i) an accuracy of at least about 95%, ii) a precision of at least about 90% and iii) an AUC of at least 0.98.

[0027] In an embodiment, the animal is a livestock animal. In an embodiment, the animal is an ungulate. In an embodiment, the ungulate is cattle, sheep, goat, deer, camel, horse, llama, alpaca, bison or buffalo. In an embodiment, the ungulate is cattle. In an embodiment, the cattle is a Bos laurus. Bos indicus or a cross thereof.

[0028] Any suitable biological sample can be used. Examples include, but are not limited to, or a blood fraction, plasma, serum, urine, whole blood, milk or a fraction thereof, whey or bulk milk. In an embodiment, the biological sample is serum.

[0029] In an embodiment, the method comprises detecting not more than 50 miRNA, or not more than 40 miRNA, or not more than 30 miRNA, or not more than 20 miRNA, or not more than 10 miRNA, or not more than 6 miRNA, or not more than 5 miRNA, or not more than 3 miRNA.

[0030] In an embodiment, the at least one miRNA is detected by next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, electrical interference, CRISPR-based method, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), strand-displacement amplification (SDA), enzyme-free amplification, microarray, multiplex miRNA profiling assay, RNA-ish, or northern blotting.

[0031] In an embodiment, the next generation sequencing is RNA-seq, small RNA-seq, miRNA-seq or targeted next generation sequencing.

[0032] In an embodiment, the at least one miRNA is detected by qRT-PCR, electrical interference or a CRISPR-based method. In an embodiment, detecting the level of the at least one miRNA comprises obtaining Ct values of the at least one miRNA in the sample by RT-qPCR. In an embodiment the RT-qPCR is one step RT-qPCR. In an embodiment, the RT-qPCR is two step RT-qPCR.

[0033] In an embodiment, obtaining Ct values of the at least one miRNA in the sample by RT-qPCR comprises: a) reverse transcription of RNA from the sample into DNA; b) amplification of the DNA by polymerase chain reaction (PCR); and c) quantification of the DNA during amplification to determine when the quantity of DNA surpasses a defined threshold.

[0034] In an embodiment, quantification of the DNA is based on detection of a fluorescence level emitted by a fluorescently labeled dye or probe.

[0035] In an embodiment the Ct value is normalized.

[0036] In an embodiment, a machine learning model is used to determine a probability of infection based on the Ct values.

[0037] In an embodiment, the machine learning model comprises a logistic regression model.

[0038] In an embodiment, the Mycobacterium avium subsp. paratuberculosis infection is a sub-clinical Mycobacterium avium subsp. paratuberculosis infection.

[0039] In a further aspect, the present invention provides a panel or kit for determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal, the panel or kit comprising one or more probes or primers for detecting at least one miRNA selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126- 5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let-7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR- 12030, miR- 1246, miR-125b, miR-1306, miR-138, miR-1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151-5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR-1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR-195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR-2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR- 32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR- 374b, miR-378, miR-423-5p, miR-425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR-6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR-99a-5p and miR-99b. In an embodiment, the panel or kit further comprising a control.

[0040] In an embodiment, the panel or kit further comprising a reference value.

[0041] In an embodiment, the panel or kit comprises a nucleotide array.

[0042] In an aspect, the present invention provides a method of managing a group of animals, the method comprising determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using the method of the invention or the panel or kit of the invention, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0043] In an aspect, the present invention provides a method of preventing spread of Mycobacterium avium subsp. paratuberculosis infections in a group of animals, the method comprising determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using the method of the invention or the panel or kit of the invention, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0044] In an aspect, the present invention provides a method of eliminating Mycobacterium avium subsp. paratuberculosis infections from a group of animals, the method comprising determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using the method of the invention or the panel or kit of the invention, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0045] In an embodiment, removing individuals from the group comprises culling the animal determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0046] In an aspect, the present invention provides a method of treating or preventing a Mycobacterium avium subsp. paratuberculosis infection in an animal, the method comprising i) determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal using the method of the invention or the panel or kit of the invention, ii) administering a treatment for a Mycobacterium avium subsp. paratuberculosis infection if it is determined the animal is likely to have a Mycobacterium avium subsp. paratuberculosis infection.

[0047] Also provided is the use of an &ni\Mycobaclerium avium subsp. paratuberculosis compound for the manufacture of a medicament for the treatment or prevention of a Mycobacterium avium subsp. paratuberculosis infection in an animal, wherein it has been determined that it is likely the animal has a Mycobacterium avium subsp. paratuberculosis infection using the method of the invention or the panel or kit of the invention.

[0048] Further provided is the use of an &ni\Mycobaclerium avium subsp. paratuberculosis compound for the treatment or prevention of a Mycobacterium avium subsp. paratuberculosis infection in an animal, wherein it has been determined that it is likely the animal has a Mycobacterium avium subsp. paratuberculosis infection using the method of the invention or the panel or kit of the invention.

[0049] In another aspect, the present invention provides a computer program product encoded on a computer-readable storage medium, wherein the computer program product comprises instructions for: a) detecting the presence, absence or quantity of at least one miRNA in a sample of an animal; and b) correlating the presence, absence, or quantity of the at least one miRNA in the sample to a likelihood that the animal being infected with Mycobacterium avium subsp. paratuberculosis, wherein the at least one miRNA is selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let- 7b, let-7c, let-7d, let-7f, let- 7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR- 11980, miR-11986b, miR-12030, miR-1246, miR-125b, miR-1306, miR-138, miR- 1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151- 5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR- 1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR- 195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR- 2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR- 425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR- 6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR- 99a-5p and miR-99b.

[0050] In an embodiment, step a) comprises detecting the quantity or quantities of the at least one miRNA in the sample and step b) comprises correlating the quantity or quantities of the at least one miRNA in the sample to a likelihood of the animal being infected with Mycobacterium avium subsp. paratuberculosis by applying the quantity or quantities of at least one miRNA to a supervised learning model. In an embodiment, detecting the quantity of the at least one miRNA comprises obtaining Ct values of the at least one miRNA in the sample by RT-qPCR. In an embodiment the RT-qPCR is one step RT-qPCR. In an embodiment, the RT-qPCR is two step RT-qPCR.

[0051] In an embodiment, obtaining Ct values of the at least one miRNA in the sample by RT-qPCR comprises: a) reverse transcription of RNA from the sample into DNA; b) amplification of the DNA by polymerase chain reaction (PCR); and c) quantification of the DNA during amplification to determine when the quantity of DNA surpasses a defined threshold.

[0052] In an embodiment, quantification of the DNA is based on detection of a fluorescence level emitted by a fluorescently labeled dye or probe.

[0053] In an embodiment the Ct value is normalized.

[0054] In an embodiment, the supervised learning model was trained on a data set of miRNAs selected by applying recursive feature elimination.

[0055] In an embodiment hyperparameters of the supervised learning model were tuned using GridSearchCV.

[0056] In an embodiment, the supervised learning model comprises a support vector machine.

[0057] In an embodiment the animal is determined to likely be infected with Mycobacterium avium subsp. paratuberculosis if the supervised learning model outputs a value exceeding a predefined threshold.

[0058] In another aspect, the present invention provides a system comprising: a) the computer program product of the invention; and b) a processor operable to execute programs; and / or a memory associated with the processor.

[0059] In another aspect, the present invention provides a system for detecting the presence or quantity of a Mycobacterium avium subsp. paratuberculosis in a sample of an animal comprising: a processor operable to execute programs; a memory associated with the processor; a database associated with said processor and said memory; and a program stored in the memory and executable by the processor, the program being operable for: a) detecting the presence, absence or quantity of at least one miRNA in a sample of an animal, wherein the at least one miRNA is selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let-7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR- 12030, miR-1246, miR-125b, miR-1306, miR-138, miR- 1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151- 5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR- 1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR- 195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR- 2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR- 425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR- 6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR- 99a-5p and miR-99b; and b) correlating the presence, absence, or quantity of the at least one miRNA in the sample to a likelihood that the animal being infected with Mycobacterium avium subsp. paratuberculosis.

[0060] In an aspect, the present invention provides a method of managing a group of animals, the method comprising determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using the computer program product, or system of the invention, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0061] In an aspect, the present invention provides a method of preventing spread of Mycobacterium avium subsp. paratuberculosis infections in a group of animals, the method comprising determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using the computer program product, or system of the invention, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0062] In an aspect, the present invention provides a method of eliminating Mycobacterium avium subsp. paratuberculosis infections from a group of animals, the method comprising determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using the computer program product, or system of the invention, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection. In an embodiment, removing individuals from the group comprises culling the animal determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0063] In an embodiment, removing individuals from the group comprises quarantining the animal determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0064] Any embodiment herein shall be taken to apply mutatis mutandis to any other embodiment unless specifically stated otherwise.

[0065] The present invention is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only. Functionally-equivalent products, compositions and methods are clearly within the scope of the invention, as described herein.

[0066] Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.

[0067] The invention is hereinafter described by way of the following non-limiting Examples and with reference to the accompanying figures.

[0068] BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

[0069] Figure 1. Workflow of the emiraldx.ai cloud-based diagnostic platform. CT values generated from RT-qPCR assays are exported as .csv files and uploaded into the emiraldx.ai platform. The cloud-based diagnostic platform processes the input data and applies the ML model, converting CT values into MAP probability of infection scores. The analysed results can be downloaded as. csv or .pdf reports for interpretation.

[0070] Figure 2. Multinomial BLCM for diagnostic test evaluation. The model illustrates how posterior estimates of diagnostic sensitivity (DSe) and specificity (DSp) are derived using prior information on prevalence and diagnostic test parameters from imperfect reference tests, together with observed data describing the latent class variables.

[0071] Figure 3. Relative expression of the most abundant host miRNAs in bovine sera samples.

[0072] Figure 4. MAP+ infection induces miRNA changes in sera. Volcano plot showing the increased (log2 fold change, log2FC, greater than 1) and decreased (log2 fold change below -1) differentially expressed (DE) miRNAs in MAP+ cases compared to controls. Horizontal dotted line marks the p-value cut-off (False Discovery Rate, FDR<0.05) and vertical lines are the log2fold change cut-off (>1 log2FC). miRNAs above the dotted line between a fold change of -1 and 1 are statistically significant but are not >1 log2FC.

[0073] Figure 5. A miRNA signature in sera classifies MAP+ infection with 95% accuracy. Left: Feature (miRNA) selection line plot showing the impact of increasing numbers of miRNAs on the performance of a Support Vector Machine model. MicroRNAs were selected using the recursive feature elimination approach to identify the most important miRNAs. Each combination of miRNAs was randomly assessed 100 times. Shaded areas are the 95% CI. Right: Bar plot showing the mean score of the miRNA signature in predicting MAP+ infection. Error bars are the 95% CI after 100 random iterative assessments, and the dotted line is a perfect (100%) score.

[0074] Figure 6. Decision boundary visualisation for a binary classification model using principal component analysis. The original dataset with three-miRNA biomarker was reduced to two dimensions using PCA to enable 2D visualisation. The plot illustrates the classifier’s decision boundary, where the background colour gradient represents the model’s prediction probability from low (blue) to high probabilities (red). Data points are overlaid as circles and diamonds, representing samples part of the control and MAP+ groups, respectively.

[0075] Figure 7. miRNA-based classification of MAP infection. (A) Confusion matrix summarising classification performance of the ML model for MAP -infected versus uninfected. (B) Decision boundary plot demonstrating separation between MAP-infected and uninfected groups for the predictive model using RT-qPCR data for miR-A, miR-B, and miR-C (miR-122 = miR-A, miR-125a = miR-B, and miR-194 = miR-C).

[0076] Figure 8. Analytical specificity of the JD biomarker RT-qPCR assays. A-C, miRNA RT-qPCR CT values and (D) three-dimensional PCA plot illustrating the clustering of MAP-infected (MAP+), MAP -uninfected (MAP-), OTHER (other infections), and bovine tuberculosis (bTB) samples based on the expression profiles of the selected miRNAs. miR-122 = miR-A, miR- 125a = miR-B, and miR-194 = miR-C.

[0077] Figure 9. Analytical sensitivity of JD biomarker RT-qPCR assays. (A-C) Standard curves for miR-A, miR-B, and miR-C (miR-122 = miR-A, miR-125a = miR-B, and miR- 194 = miR-C). Regression lines depict the relationship between logw copy number and CT values across 10-fold serial dilutions.

[0078] Figure 10. Operating range of JD biomarker RT-qPCR assays. (A-C) CT values plotted against serial dilutions of sera from MAP -infected (Pos) mixed with sera from uninfected animals (Neg). (D) Probability of infection scores across serial dilutions. miR-122 = miR-A, miR-125a = miR-B, and miR-194 = miR-C.

[0079] Figure 11. Repeatability of JD biomarker RT-qPCR assays. (A-C) CT values across 45 independent RT-qPCR reactions performed by multiple operators. Mean values are shown as solid lines, with ±1 SD and ±2 SD indicated by dotted lines. miR-122 = miR- A, miR-125a = miR-B, and miR-194 = miR-C.

[0080] Figure 12. Probability of infection scores for miRNA biomarkers across replicate tests. (A-C) Probability scores across repeats in three operators.

[0081] Figure 13. ROC curve analysis of miRNA biomarker assays. (A) ROC curve for the three-miRNA assay (miR-122, miR-125a and miR-194), showing strong discriminatory ability between MAP-infected and uninfected animal samples. (B) ROC curve for the four-miRNA assay (miR-122, miR-125a, miR-194 and miR-126-5p). (C) ROC curve for the five-miRNA assay (miR-122, miR-125a, miR-194, miR-126-5p and miR-339b). (D) Overlay of ROC curves from the three-, four-, and five-miRNA assays, demonstrating comparable diagnostic performance. Pairwise statistical comparisons indicated no significant differences between the ROC curves (P > 0.05), confirming that increasing the number of miRNAs beyond three did not improve assay performance.

[0082] Figure 14. Multi -lab oratory reproducibility of TaqMan assays. (A) CT values from 20 serum samples tested independently across four laboratories, shown on the x-axis as Lab 1 (Australian Rickettsial Reference Laboratory, Geelong), Lab 2 (Australian Animal Health Laboratory, Geelong), Lab 3 (Deakin University, Geelong), and Lab 4 (National JD Reference Laboratory, Bundoora). (B) Probability of MAP positivity for the same samples across the four laboratories, with the y-axis showing the probability of a positive result. The plots demonstrate assay consistency, inter-laboratory variation, and reproducible classification of positive (POS) and negative (NEG) cohorts. miR-122 = miR-A, miR-125a = miR-B, and miR-194 = miR-C. KEY TO THE SEQUENCE LISTING

[0083] SEQ ID NO: 1 - Bos taurus let-7a-5p

[0084] SEQ ID NO: 2 - Bos taurus let-7b

[0085] SEQ ID NO: 3 - Bos taurus let-7c

[0086] SEQ ID NO: 4 - Bos taurus let-7d

[0087] SEQ ID NO: 5 - Bos taurus let-7f

[0088] SEQ ID NO: 6 - Bos taurus let-7g

[0089] SEQ ID NO: 7 - Bos taurus let-7i

[0090] SEQ ID NO: 8 - Bos taurus miR-100

[0091] SEQ ID NO: 9 - Bos taurus miR-10225a

[0092] SEQ ID NO: 10 -Bo taurus miR-106b SEQ ID NO: 11 - os taurus miR-lOa SEQ ID NO: 12 -Bos taurus miR-lOb SEQ ID NO: 13 -Bos taurus miR-11971 SEQ ID NO: 14 -Bos taurus miR-11980 SEQ ID NO: 15 -Bos taurus miR-11986b SEQ ID NO: 16 -Bos taurus miR-12030 SEQ ID NO: 17 -Bos taurus miR-122 SEQ ID NO: 18 -Bos taurus miR-1246 SEQ ID NO: 19 -Bos taurus miR-125 a SEQ ID NO: 20 -Bos taurus miR-125b SEQ ID NO: 21 - Bos taurus miR-126-5p SEQ ID NO: 22 -Bos taurus miR-1306 SEQ ID NO: 23 -Bos taurus miR-138 SEQ ID NO: 24 -Bos taurus miR-1388-5p SEQ ID NO: 25 -Bos taurus miR-139 SEQ ID NO: 26 -Bos taurus miR-142-5p SEQ ID NO: 27 -Bos taurus miR-145 SEQ ID NO: 28 -Bos taurus miR-1468 SEQ ID NO: 29 -Bos taurus miR-146a SEQ ID NO: 30 -Bos taurus miR-146b SEQ ID NO: 31 - Bos taurus miR-150 SEQ ID NO: 32 -Bos taurus miR- 151 - 5p SEQ ID NO: 33 -Bos taurus miR-15a SEQ ID NO: 34 -Bos taurus miR- 15b SEQ ID NO: 35 -Bos taurus miR- 16a SEQIDNO: 36 - Bos taurus miR-16b

[0093] SEQIDNO: 37 - Bos taurus miR-17-5p

[0094] SEQIDNO: 38 - Bos taurus miR-181a

[0095] SEQIDNO: 39 - Bos taurus miR-181b SEQIDNO: 40 - Bos taurus miR-1839

[0096] SEQIDNO: 41 - Bos taurus miR-185

[0097] SEQIDNO: 42 - Bos taurus miR-186

[0098] SEQIDNO: 43 - Bos taurus miR-18a

[0099] SEQIDNO: 44 - Bos taurus miR-191 SEQIDNO: 45 - Bos taurus miR-191b

[0100] SEQIDNO: 46 - Bos taurus miR-192

[0101] SEQIDNO: 47 - Bos taurus miR-193a-5p

[0102] SEQIDNO: 48 - Bos taurus miR-194

[0103] SEQIDNO: 49 - Bos taurus miR-195 SEQIDNO: 50 - Bos taurus miR-204

[0104] SEQIDNO: 51 - Bos taurus miR-205

[0105] SEQIDNO: 52 - Bos taurus miR-20a

[0106] SEQIDNO: 53 - Bos taurus miR-215

[0107] SEQIDNO: 54 - Bos taurus miR-22-5p SEQIDNO: 55 - Bos taurus miR-224

[0108] SEQIDNO: 56 - Bos taurus miR-2284w

[0109] SEQIDNO: 57 - Bos taurus miR-2284x

[0110] SEQIDNO: 58 - Bos taurus miR-2284z

[0111] SEQIDNO: 59 - Bos taurus miR-2285bc SEQIDNO: 60 - Bos taurus miR-2285bf

[0112] SEQIDNO: 61 - Bos taurus miR-2285bn

[0113] SEQIDNO: 62 - Bos taurus miR-2285ce

[0114] SEQIDNO: 63 - Bos taurus miR-2285cj

[0115] SEQIDNO: 64 - Bos taurus miR-23b-3p SEQIDNO: 65 - Bos taurus miR-2419-5p

[0116] SEQIDNO: 66 - Bos taurus miR-26a

[0117] SEQIDNO: 67 - Bos taurus miR-26b

[0118] SEQIDNO: 68 - Bos taurus miR-28

[0119] SEQIDNO: 69 - Bos taurus miR-296-5p SEQIDNO: 70 - Bos taurus miR-30a-5p

[0120] SEQIDNO: 71 - Bos taurus miR-30b-5p SEQ ID NO: 72 - Bos taurus miR-30c SEQ ID NO: 73 - Bos taurus miR-30d SEQ ID NO: 74 - Bos taurus miR-30e-5p SEQ ID NO: 75 - Bos taurus miR-31 SEQ ID NO: 76 - Bos taurus miR-32 SEQ ID NO: 77 - Bos taurus miR-324 SEQ ID NO: 78 - Bos taurus miR-331 -5p SEQ ID NO: 79 - Bos taurus miR-335 SEQ ID NO: 80 - Bos taurus miR-339a SEQ ID NO: 81 - Bos taurus miR-339b SEQ ID NO: 82 - Bos taurus miR-361 SEQ ID NO: 83 - Bos taurus miR-362-5p SEQ ID NO: 84 - Bos taurus miR-374b SEQ ID NO: 85 - Bos taurus miR-378 SEQ ID NO: 86 - Bos taurus miR-423-5p SEQ ID NO: 87 - Bos taurus miR-425-5p SEQ ID NO: 88 - Bos taurus miR-4286 SEQ ID NO: 89 - Bos taurus miR-451 SEQ ID NO: 90 - Bos taurus miR-484 SEQ ID NO: 91 - Bos taurus miR-486 SEQ ID NO: 92 - Bos taurus miR-497 SEQ ID NO: 93 - Bos taurus miR-499 SEQ ID NO: 94 - Bos taurus miR-532 SEQ ID NO: 95 - Bos taurus miR-574 SEQ ID NO: 96 - Bos taurus miR-6119-5p SEQ ID NO: 97 - Bos taurus miR-6123 SEQ ID NO: 98 - Bos taurus miR-6529a SEQ ID NO: 99 - Bos taurus miR-660 SEQ ID NO: 100 - Bos taurus miR-744 SEQ ID NO: 101 - Bos taurus miR-877 SEQ ID NO: 102 - Bos taurus miR-93 SEQ ID NO: 103 - Bos taurus miR-98 SEQ ID NO: 104 - Bos taurus miR-99a-5p SEQ ID NO: 105 - Bos taurus miR-99b Due to limitations in the ST.26 software, each “T” in the sequence listing is actually a “U”.

[0121] DETAILED DESCRIPTION OF THE INVENTION

[0122] General Techniques and Definitions

[0123] Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in cell culture, molecular genetics, miRNA and detection thereof, and biochemistry).

[0124] It must be noted that as used herein and in the appended claims, the singular forms “a”, “an,” and “the” include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to “a nucleic acid sequence” includes a plurality of nucleotides that are formed, reference to “the nucleic acid sequence” is a reference to one or more nucleic acid sequences and equivalents thereof known to those skilled in the art, and so forth.

[0125] The term “and / or”, e.g., “X and / or Y” shall be understood to mean either “X and Y” or “X or Y” and shall be taken to provide explicit support for both meanings or for either meaning.

[0126] The term “about” is used herein to mean within the typical ranges of tolerances in the art. For example, “about” can be understood as about 2 standard deviations from the mean. According to certain embodiments, when referring to a measurable value such as an amount and the like, “about” is meant to encompass variations of ±10%, ±5%, ±1%, ±0.9%, ±0.8%, ±0.7%, ±0.6%, ±0.5%, ±0.4%, ±0.3%, ±0.2% or ±0.1% from the specified value as such variations are appropriate to perform the disclosed methods. When “about” is present before a series of numbers or a range, it is understood that “about” can modify each of the numbers in the series or range.

[0127] As used herein, “accuracy” refers to the ability of the method, kit or panel as described herein to discriminate between a target condition in a subject and health in a subject.

[0128] An “algorithm”, “formula” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an “index” or “index value.” Non-limiting examples of “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker (e.g., miRNAs disclosed herein) value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular use in combining markers are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of the biomarkers detected in a subject sample and the subject’s risk of disease (for example). In panel and combination construction, of particular interest are structural and syntactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as cross correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shruken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others. Many of these techniques are useful either combined with a biomarker selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, or they may themselves include biomarker selection methodologies in their own technique. These may be coupled with information criteria, such as Akaike’s Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the trade-off between additional biomarkers and model improvement, and to aid in minimizing overfit. The resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Leave-One-Out (LOO) and 10-Fold cross-validation (10-Fold-CV).

[0129] As used herein, an “amplification assay” is an assay that uses purified enzymes to exponentially replicate specific nucleic acids to levels where they can be detected. As the skilled person will appreciate, typically an amplification assay involves the use of oligonucleotide primers which hybridise regions flanking a target sequences, a polymerase, and numerous rounds of producing single stranded nucleic acids (usually by heat denaturation), primer annealing and primer extension using the polymerase.

[0130] The term “at least” prior to a number or series of numbers (e.g. “at least two”) is understood to include the number adjacent to the term “at least,” and all subsequent numbers or integers that could logically be included, as clear from context. When “at least” is present before a series of numbers or a range, it is understood that “at least” can modify each of the numbers in the series or range. The terms “complementary” or “complementarity” refer to polynucleotides (i.e., a sequence of nucleotides) related by base-pairing rules, for example, the sequence “5’- AGT-3’,” is complementary to the sequence “5’-ACT-3’ ” Complementarity may be “partial,” in which only some of the nucleic acids’ bases are matched according to the base pairing rules, or there may be “complete” or “total” complementarity between the nucleic acids.

[0131] As used herein, the terms “comprising” (and any form of comprising, such as “comprise,” “comprises,” and “comprised”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”), or “containing” (and any form of containing, such as “contains” and “contain”), are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[0132] The term “diagnosis” as used herein refers to the identification of the nature of an illness (e.g. identification of where a subject has a AT. avium subsp. paratuberculosis infection).

[0133] As used herein, “expression” refers to the process by which a polynucleotide is transcribed from a DNA template into a miRNA, mRNA or other RNA transcript and / or the process by which a transcribed mRNA is subsequently translated into peptides, polypeptides, or proteins.

[0134] The term “label” as used herein refers to any atom or molecule that can be used to provide a detectable (preferably quantifiable) effect, and that can be attached to a nucleic acid or protein.

[0135] The term “level” as used herein refers to qualitative or quantitative determination of the number of copies of a miRNA. In some embodiments, a miRNA transcript exhibits an “increased level” when the level of the miRNA transcript is higher than a reference value as described herein. In some embodiments, a miRNA transcript exhibits a “decreased level” when the level of the miRNA transcript is lower than a reference value as described herein.

[0136] The term “machine learning” as used herein encompasses all possible mathematical in silico techniques for creation of useful algorithms from large data sets. The term “algorithm” will be utilized in reference to the clinically useful mathematical equations or computer programs produced by the one or plurality of processes disclosed or executing the one or plurality of processes disclosed. In some embodiments, the performance of machine learning derived algorithms is independent of the specific in silico software routine used for its derivation. If the same training data set is used, techniques as different as supervised learning, unsupervised learning, association rule learning, hierarchical clustering, multiple linear and logistic regressions are likely to produce algorithms whose clinical performance is indistinguishable.

[0137] The term “measuring” or “measurement” means assessing the presence, absence, quantity or amount (which can be an effective amount) or determining a “score” as described herein of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances. Alternatively, the term “detecting” or “detection” may be used and is understood to cover measuring or measurement as described herein.

[0138] The term “normalizing” or “normalized” as used herein with regard to a miRNA, refers to the level of the miRNA, relative to the mean levels of a set or control set of reference miRNA. The reference miRNA are based on their minimal variation across patients, tissues, or treatments. Alternatively, the RNA transcript may be normalized to the totality of tested miRNAs, or a subset of such tested miRNAs.

[0139] The particular use of terms “nucleic acid,” “oligonucleotide,” and “polynucleotide” should in no way be considered limiting and may be used interchangeably herein. “Oligonucleotide” is used when the relevant nucleic acid molecules typically comprise less than about 100 bases. “Polynucleotide” is used when the relevant nucleic acid molecules typically comprise more than about 100 bases. Both terms are used to denote DNA, RNA, modified or synthetic DNA or RNA (including, but not limited to nucleic acids comprising synthetic and naturally-occurring base analogs, dideoxy or other sugars, thiols or other non-natural or natural polymer backbones), or other nucleobase containing polymers capable of hybridizing to DNA and / or RNA. Accordingly, the terms should not be construed to define or limit the length of the nucleic acids referred to and used herein, nor should the terms be used to limit the nature of the polymer backbone to which the nucleobases are attached.

[0140] The term “nucleic acid sequence” or “polynucleotide sequence” refers to a contiguous string of nucleotide bases and in particular contexts also refers to the particular placement of nucleotide bases in relation to each other as they appear in a polynucleotide.

[0141] As used herein “one or more of’ includes at least one of the recited components, or 2, 3, 4, 5, or 6 etc. of the recited components. In some embodiments, the phase includes all of the recited components.

[0142] Ranges provided herein are understood to include all individual integer values and all subranges within the ranges.

[0143] As used herein, the term “performance” relates to the quality and overall usefulness of, e.g., a model, algorithm, or prognostic test. Factors to be considered in model or test performance include, but are not limited to, the clinical and analytical accuracy of the test, use characteristics such as stability of reagents and various components, ease of use of the model or test, health or economic value, and relative costs of various reagents and components of the test. Performing can mean the act of carrying out a function.

[0144] As used herein, “precision” refers to the chance that a subject testing positive actually has the condition being tested for.

[0145] As used herein, the terms “prevent,” “preventing,” “prevention,” “prophylactic treatment,” and the like, are meant to refer to reducing the probability of developing a disease or condition in a subject, who does not have, but is at risk of or susceptible to developing a disease or condition.

[0146] As used herein, “sequence identity” is determined by using the stand-alone executable BLAST engine program for blasting two sequences (bl2seq), which can be retrieved from the National Center for Biotechnology Information (NCBI) ftp site, using the default parameters (Tatusova and Madden, 1999). Alternatively, “% sequence identity” can be determined using the EMBOSS Pairwise Alignment Algorithms tool available from The European Bioinformatics Institute (EMBL-EBI), which is part of the European Molecular Biology Laboratory (EMBL). This tool is accessible at the website ebi.ac.uk / Tools / emboss / align / . This tool utilizes the Needleman-Wunsch global alignment algorithm (Needleman and Wunsch, 1970; Kruskal, 1983). Default settings are utilized which include Gap Open: 10.0 and Gap Extend 0.5. The default matrix “Blosum62” is utilized for amino acid sequences and the default matrix “DNAfull” is utilized for nucleic acid sequences.

[0147] As used herein, the term “statistically significant” means an observed alteration is greater than what would be expected to occur by chance alone (e.g., a “false positive”). Statistical significance can be determined by any of various methods well-known in the art. An example of a commonly used measure of statistical significance is the p-value. The p-value represents the probability of obtaining a given result equivalent to a particular datapoint, where the datapoint is the result of random chance alone. A result is often considered highly significant (not random chance) at a p-value less than or equal to about 0.05.

[0148] The terms “treat,” “treated,” “treating,” “treatment,” and the like as used herein refer to reducing or ameliorating a disorder and / or symptoms associated therewith (e.g., an infection). “Treating” includes the concepts of “alleviating,” which refers to lessening the frequency of occurrence or recurrence, or the severity, of any symptoms or other ill effects related to an infection and / or the side effects associated with therapy. The term “treating” also encompasses the concept of “managing” which refers to reducing the severity of a particular disease or disorder in a patient or delaying its recurrence, e.g., lengthening the period of remission in a patient who had suffered from the disease. It is appreciated that, although not precluded, treating a disorder or condition does not require that the disorder, condition, or symptoms associated therewith be completely eliminated.

[0149] Johne’s Disease

[0150] Johne’s disease is a contagious, chronic, and usually fatal infection that affects primarily the small intestine of ruminants. Johne’s disease is caused by Mycobacterium avium subspecies paratuberculosis (M. avium subsp. paratuberculosis'). Johne’s disease is found worldwide.

[0151] Johne’s disease can have severe economic impacts on infected herds. Identifying and protecting noninfected herds and flocks provides a source of breeding stock and replacement animals for others and help to reduce the national prevalence of the disease.

[0152] In cattle, signs of Johne’s disease include weight loss and diarrhea with normal appetite. Several weeks after the onset of diarrhea, a soft swelling may occur under the jaw. This intermandibular edema, or “bottle jaw,” is due to protein loss from the bloodstream into the digestive tract. Animals at this stage of the disease will not live very long — perhaps a few weeks at most.

[0153] Signs are rarely evident until 2 or more years after the initial infection, which usually occurs shortly after birth. Animals exposed at an older age, or exposed to a very small dose of bacteria at a young age, are not likely to develop clinical disease until they are much older than 2 years.

[0154] In sheep and goats, the clinical signs are harder to spot. The intestines become thick and less efficient at absorbing nutrients. Affected sheep continue to eat but lose weight and “waste away.” Although the disease causes diarrhea in cattle, less than 20 percent of sheep show diarrhea. In up to 70 percent of sheep, the disease may remain at subclinical levels, where individual animals never show signs of the disease but shed the agent in their faeces and infect other sheep and contaminate the environment. In goats, weight loss, poor performance and occasionally clumpy faeces are all that is seen. Affected animals usually show sign before they are 1 year of age.

[0155] Johne’s Disease is generally described as having four stages;

[0156] 1) Stage I: Silent, subclinical, nondetectable infection

[0157] Typically, this stage occurs in calves, heifers, and young stock under 2 years of age or animals exposed at an older age. Current tests (including fecal culture and serological tests) cannot detect infection in animals that young. This stage progresses slowly over many months or years to Stage II. It is possible that some animals recover from this early phase of infection.

[0158] 2) Stage II: Subclinical

[0159] This stage usually occurs in heifers or older animals. Animals appear healthy but are shedding AT. avium subsp. paratuberculosis in their manure at levels high enough to be detected. These animals pose a major, but often hidden, threat of infection to other animals through contamination of the environment. Stage II animals may or may not progress over time to Stage III.

[0160] 3) Stage III: Clinical Johne’s disease

[0161] Animals in this stage have advanced infection, and clinical signs are often brought on by stress. Clinical signs at this stage include acute or intermittent diarrhea, weight loss despite a normal appetite, and decreased milk production. Some animals appear to recover but often relapse in the next stressful period. Most of these animals are shedding billions of Johne’s-causing organisms, and fecal organism detection tests give positive results. Many animals are positive on serologic tests as well. Clinical signs may last days to weeks before the animals progress to Stage IV.

[0162] 4) Stage IV: Emaciated animals with fluid diarrhea

[0163] This is the terminal stage of the disease in which animals become extremely thin and develop bottle jaw. Animals culled to slaughter in this stage may not pass inspection for human consumption due to disseminated infection. In a typical herd, for every animal in Stage IV, many other cattle are infected. For every obvious case of Johne’s disease (Stage IV) among dairy cattle on the farm, 15 to 25 other animals are likely infected. The clinical case represents only the “tip of the iceberg” of Johne’s infection. miRNA

[0164] As used herein “microRNA” or “miRNA” refers to miRNAs (typically 19-25 nucleotides in length) or a precursor thereof that regulate endogenous gene expression at the post-transcriptional level. MicroRNA play a role in gene regulation by binding to complementary target messenger RNAs (mRNAs) resulting in target mRNA degradation or translational blockade. In most instances, miRNAs function by interacting with the 3 ’ untranslated region (3’ UTR) of target mRNAs to induce mRNA degradation and translational repression. The sequences of miRNAs are often conserved across species. The present disclosure provides a method of determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal, the method comprising, in a biological sample from the animal, detecting a level of at least one miRNA selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126- 5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let-7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR- 12030, miR- 1246, miR-125b, miR-1306, miR-138, miR-1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151-5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR-1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR-195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR-2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR- 32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR- 374b, miR-378, miR-423-5p, miR-425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR-6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR-99a-5p and miR-99b.

[0165] The present disclosure further provides a method of determining the likelihood an animal has Johne’s disease, the method comprising, in a biological sample from the animal, detecting a level of at least one miRNA selected from: miR-122, miR-125a, miR-

[0166] 194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let- 7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR- 12030, miR- 1246, miR- 125b, miR-1306, miR-138, miR- 1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151- 5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR- 1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR-

[0167] 195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR- 2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR- 425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR- 6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR- 99a-5p and miR-99b.

[0168] In an embodiment, a method of the invention comprises detecting a level of at least miR-122, miR-125a and miR-194. In an embodiment, a method of the invention comprises detecting a level of at least miR-122, miR-125a, miR-194 and miR-126-5p. In an embodiment, a method of the invention comprises detecting a level of at least miR- 122, miR-125a, miR-194, miR-126-5p and miR-339b. In an embodiment, each of the above combinations is detected by PCR such as RT-qPCR.

[0169] In an embodiment, a method of the invention comprises detecting a level of at least miR-497, miR-23b-3p and miR-126-5p. In an embodiment, a method of the invention comprises detecting a level of at least miR-497, miR-23b-3p, miR-126-5p and miR-145. In an embodiment, a method of the invention comprises detecting a level of at least miR-497, miR-23b-3p, miR-126-5p, miR-145 and miR-194. In an embodiment, each of the above combinations is detected by sequencing.

[0170] In an embodiment, a method of the invention comprises detecting a level of at least miR-122.

[0171] In an embodiment, a method of the invention comprises detecting a level of at least miR-125a.

[0172] In an embodiment, a method of the invention comprises detecting a level of at least miR-194.

[0173] In an embodiment, a method of the invention comprises detecting a level of at least miR-126-5p.

[0174] In an embodiment, a method of the invention comprises detecting a level of at least miR-339b.

[0175] In an embodiment, a method of the invention comprises detecting a level of at least miR-497.

[0176] In an embodiment, a method of the invention comprises detecting a level of at least miR-23b-3p.

[0177] In an embodiment, a method of the invention comprises detecting a level of at least miR-145.

[0178] In some embodiments, the miRNA is from a livestock animal. In some embodiments, the miRNA is from a ruminant. In some embodiment, the miRNA is an ungulate miRNA. In some embodiment, the miRNA is a Bovidae miRNA. In some embodiments, the miRNA is Bovinae miRNA. In some embodiments, the miRNA is a Bos miRNA. In some embodiments, the miRNA is a Bos taurus miRNA. Bos taurus miRNAs are designated with the prefix “bta”. In some embodiments, the miRNA is a Bos indicus miRNA.

[0179] In an embodiment, a miRNA detected using a method of the invention comprises, or consists of, an RNA sequence provided as any one of SEQ ID NO’s 1 to 105 (where each “T” in the sequence is a “U”). In an embodiment, a miRNA detected using a method of the invention comprises, or consists of, an RNA sequence provided as any one of SEQ ID NO’s 1 to 105 (where each “T” in the sequence is a “U”) which has one or two nucleotide differences. Such miRNAs can be orthologs of the cattle miRNAs provided as SEQ ID NO’s 1 to 105, in for example sheep or goats, or may be naturally occurring allelic variants in cattle.

[0180] In some embodiments, the miRNAs are circulating miRNAs (circulating in the blood stream).

[0181] In some embodiments, the method comprises detecting not more than 50 miRNA. In some embodiments, the method comprises detecting not more than 40 miRNA. In some embodiments, the method comprises detecting not more than 30 miRNA. In some embodiments, the method comprises detecting not more than 20 miRNA. In some embodiments, the method comprises detecting not more than 10 miRNA. In some embodiments, the method comprises detecting not more than 6 miRNA. In some embodiments, the method comprises detecting not more than 5 miRNA. In some embodiments, the method comprises detecting not more than 4 miRNA. In some embodiments, the method comprises detecting not more than 3 miRNA. n some embodiments, the method comprises detecting not more than 2 miRNA.

[0182] Subjects and Biological Samples

[0183] An “animal” or "subject" contemplated in the present disclosure includes mammals including livestock, companion animals or laboratory or accepted test or vehicle animals. In some embodiments, the animal is a mammal. In some embodiment, the animal is a livestock animal.

[0184] As used herein “livestock” refers to domesticated animals raised in an agricultural setting to produce labour and commodities such as meat, milk, fur, leather, and wool. In some embodiments, the livestock is selected from: Bos Iannis. Bos indicus. Bubalus bubalis (Asian water buffalo), Capra hircus (goats), Ovis aries (sheep), Syncerus caffer (African buffalo), Kobus leche kafuensis (Kafue lechwe antelopes), bison, camel elk, deer, and pig. In an embodiment, the animal is a camelid. In an embodiment, the animal is a horse.

[0185] In an embodiment, the animal is an Ungulate which primarily consists of large mammals with hooves. In an embodiment, the animal is a ruminant.

[0186] In some embodiments, the livestock is cattle. In some embodiments, the cattle is Bovidae. In some embodiments, cattle is Bovinae. In some embodiments, the cattle is Bos Iannis. Bos indicus or a cross thereof. In some embodiments, the cattle is Bos taurus. In some embodiments, the cattle is Bos indicus. In some embodiments, the cattle is selected from dairy cattle, meat cattle, dairy-cross cattle or rearing cattle. In some embodiments, the cattle is dairy cattle. In some embodiments, the cattle is meat cattle. In some embodiments, the cattle is beef cattle.

[0187] In some embodiments, the animal has a sub-clinical Mycobacterium avium subsp. paratuberculosis infection. In some embodiments, the animal is shedding Mycobacterium avium subsp. paratuberculosis DNA.

[0188] As described herein, a biological sample refers to any sample from an animal comprising miRNA e.g. bodily fluids, biopsy, tissue, and / or waste from an animal. In some embodiments, the biological sample is selected from: serum, blood or a fraction thereof, plasma, urine, milk or a fraction thereof, whey, lymph fluid, biopsy or tissue sample, respiratory mucosal sample, nasopharangeal sample, saliva, bulk milk. In some embodiments, the sample is serum. In some embodiments, the sample is blood or a fraction thereof. In some embodiments, the sample is plasma. In some embodiments, the sample is urine. In some embodiments, the sample is selected from milk, whey and bulk milk. In some embodiments, the sample is milk. In some embodiments, the sample is whey. In some embodiments, the sample is bulk milk. As used herein “bulk milk” refers to pooled milk collected from a dairy herd. In embodiment, the bulk milk is from a daily collection from a dairy herd. In an embodiment, the bulk milk is unprocessed.

[0189] In an embodiment, the sample is stimulated blood or a fraction thereof. The stimulant can be any compound, composition, molecule, particle, or agent that, when contacted with a blood sample or blood-derived cells, induces, enhances, or modulates the secretion, release, or expression of one or more miRNAs, cytokines, chemokines, or other immune-derived biomarkers. Such stimulants may include, but are not limited to, natural or synthetic mitogens, immunostimulants, receptor agonists (e.g., Toll-like receptor agonists, pathogen-associated molecular pattern mimetics), antigens, peptides, nucleic acids, aptamers, nanoparticles, or combinations thereof. For example, the blood sample can be stimulated with an allergen such as an Mycobacterium avium subsp. paratuberculosis antigen. In one example, the blood is stimulated with bovine tuberculin.

[0190] The sample may be in a form taken directly from the animal, or may be at least partially processed (purified) to remove at least some, for example, non-nucleic acid material. Such purification procedures are well known in the art.

[0191] A person skilled in the art would appreciate that the serum may be isolated from whole blood by any method known to a person skilled in the art. A person skilled in the art would appreciate that the plasma may be isolated from whole blood by any method known to a person skilled in the art. For example, the method may comprise collection of whole blood in an ethylenedi aminetetraacetic acid (EDTA) treated, citrate treated, potassium oxalate / sodium fluoride treated or heparinized container and centrifugation to isolate the plasma fraction. Thus, in some embodiments, the plasma is ethylenediaminetetraacetic acid (EDTA), citrate, potassium oxalate, sodium fluoride or heparin treated plasma. In some embodiments, the biological sample is collected in a PAXgene Blood RNA tube.

[0192] In some embodiments, biological samples may be collected from an animal at more than one time point to e.g. monitor progression of a M. avium subsp. paratuberculosis infection, or to monitor, assess or optimize the efficacy of a treatment protocol. In some embodiments, the biological sample may be collected from an animal before, during and / or after treatment for a M. avium subsp. paratuberculosis infection. Samples may be collected, daily, weekly, fortnightly or monthly to monitor progression of a M. avium subsp. paratuberculosis infection or to assess the efficacy of a treatment regimen. Biological samples may be frozen for processing or analysis at a later date. In some embodiments, of the methods as described herein the biological samples are processed to extract RNA, small RNA and / or miRNA. For example, biological samples may be processed within 1 hour, or within 2 hours, or within 3 hours, or within 4 hours of collection for detection of miRNAs.

[0193] In some embodiments, any of the methods disclosed herein comprise using a small volume of sample. In some embodiments, the methods disclosed comprise isolating total RNA and / or amplifying miRNA in a sample of no more than about 20 microliters of sample, about 40 microliters of sample, about 80 microliters of sample, about 100 microliters of sample, about 200 microliters of sample, about 300 microliters of sample, about 400 microliters of sample, about 500 microliters of sample, about 600 microliters of sample, about 700 microliters of sample, about 800 microliters of sample, about 900 microliters of sample, about 1 milliliter of sample, about 1.1 milliliter of sample, about 1.2 milliliter of sample, about 1.3 milliliter of sample, about 1.4 milliliter of sample, about 1.5 milliliter of sample, about 1.6 milliliter of sample, about 1.7 milliliter of sample, about 1.8 milliliter of sample, about 1.9 milliliter of sample, about 2.0 milliliter of sample. In some embodiments, the sample size is from about 25 microliters to about 2 milliliter of liquid. miRNA / RNA Extraction

[0194] A person skilled in the art will appreciate that the RNA, small RNA (cut-off approximately 200 nt) and / or miRNA fraction of the biological samples as described herein may be extracted by any method known to a person skilled in the art including for example, phenol-based techniques, combined phenol and column-based techniques or a column-based technique as described in El-Khoury et al. (2016). A commercial kit may be used for RNA and / or miRNA extraction including for example, isolation with the miRNeasy Serum / Plasma kit (Qiagen, #217184), PAXgene Blood RNA Kit (Qiagen, #762174), MagnaZol cfRNA Isolation Reagent (Bioo Scientific. #NOVA-3830-01), mirPrimer (Sigmaaldrich #SNC10), miRCURY RNA Isolation Kit - Cell and Plant (Exiqon, #300110) or miRCURY RNA Isolation Kit - Biofluids (Exiqon, #300112). In some embodiments, for miRNeasy extraction glycogen (10 pg, Sigma Aldrich, G1767) may be added as a carrier to each sample after lysis with Qiazol.

[0195] In some embodiments, the quality and / or quantity of the extracted RNA, small RNA and / or miRNA may also be determined by any method known to a person skilled in the art e.g. spectrophotometrically at 260, 280 and 230 nm, agarose gel electrophoreses, fluorometrically (for example, using the Qubit Fluorometer (Invitrogen)), or Bioanalyzer analysis (Agilent). In some embodiments, RNA, small RNA and / or miRNA is not extracted or concentrated from the biological sample. For example, a multiplex miRNA profiling assay may be performed directly on a biological sample without prior processing to extract or concentrate the miRNA component of the sample (Tackett et al., 2017).

[0196] Library Preparation

[0197] For detection methods comprising next generation sequencing a person skilled in the art would appreciate that an RNA sample may be subjected to a library preparation process. In some embodiments, the library preparation process is selected from CleanTag Small RNA Library Prep kit (TRiLink), NEXTflex Small RNA Sequence Kit v3 (Bioo Scientific) and QIAseq miRNA Library kit (Qiagen) as described for example in Wong et al. (2019).

[0198] Detection Methods

[0199] A person skilled in the art will appreciate that the miRNA can be detected with any method known to a person skilled in the art including, for example, the methods described in or adapted from Git et al. (2010), Hunt et al. (2015), Tian et al. (2015), Blondal et al. (2017), Tackett et al. (2017), Hu et al. (2017), D’AGata et al. (2019), Aquino-Jarquin et al. (2021) and Collins et al (2021). Generally, small RNA, such as miRNA, can be detected and quantified from a sample (including fractions thereof), such as samples of isolated RNA by various methods known for mRNA, including, for example, amplification assay (e.g., Polymerase Chain Reaction (PCR), Real-Time Polymerase Chain Reaction (RT-PCR), Quantitative Polymerase Chain Reaction (qPCR), rolling circle amplification, etc.), hybridization-based methods (e.g., hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched DNA (bDNA) signal amplification, in situ hybridization, etc.), and sequencingbased methods (e.g., next-generation sequencing methods, for example, using the Illumina or lonTorrent platforms). Other exemplary techniques include ribonuclease protection assay (RPA), mass spectroscopy, electrical interference, nanomaterials, or a CRISPR-based method.

[0200] In some embodiments, the RNA is converted to DNA (cDNA) prior to analysis. cDNA can be generated by reverse transcription of isolated miRNA using conventional techniques. In some embodiments, miRNA is amplified prior to measurement. In other embodiments, the level of miRNA is measured during the amplification process. In still other embodiments, the level of miRNA is not amplified prior to measurement. Some exemplary methods suitable for determining the level of miRNA in a sample are described in greater detail below. These methods are provided by way of illustration only, and it will be apparent to a skilled person that other suitable methods may likewise be used.

[0201] In an embodiment, the method is a point of care method.

[0202] In an aspect of the invention, a miRNA as defined herein, or combination thereof, is detected in vivo.

[0203] Amplification-Based Methods

[0204] Many amplification assays exist for detecting the level of miRNA nucleic acid sequences, including, but not limited to, PCR, RT-PCR, qPCR, and rolling circle amplification. Other amplification-based techniques include, for example, ligase chain reaction, multiplex ligatable probe amplification, in vitro transcription (IVT), strand displacement amplification, transcription-mediated amplification, RNA (Eberwine) amplification, and other methods that are known to persons skilled in the art.

[0205] A typical PCR reaction includes multiple steps, or cycles, that selectively amplify target nucleic acid species: a denaturing step, in which a target nucleic acid is denatured; an annealing step, in which a set of PCR primers (i.e., forward and reverse primers) anneal to complementary DNA strands, and an elongation step, in which a thermostable DNA polymerase elongates the primers. By repeating these steps multiple times, a DNA fragment is amplified to produce an amplicon, corresponding to the target sequence. Typical PCR reactions include 20 or more cycles of denaturation, annealing, and elongation. In many cases, the annealing and elongation steps can be performed concurrently, in which case the cycle contains only two steps. A reverse transcription reaction (which produces a cDNA sequence having complementarity to a miRNA) may be performed prior to PCR amplification. Reverse transcription reactions include the use of, e.g., a RNA-based DNA polymerase (reverse transcriptase) and a primer.

[0206] Kits for quantitative real time PCR of miRNA are known, and are commercially available. Examples of suitable kits include, but are not limited to, the TaqMan miRNA Assay (ThermoFisher Scientific) and the mirVana qRT-PCR miRNA detection kit (ThermoFisher Scientific). The miRNA can be ligated to a single stranded oligonucleotide containing universal primer sequences, a polyadenylated sequence, or adaptor sequence prior to reverse transcriptase and amplified using a primer complementary to the universal primer sequence, poly(T) primer, or primer comprising a sequence that is complementary to the adaptor sequence.

[0207] In some instances, custom qRT-PCR assays can be developed for determination of miRNA levels. Custom qRT-PCR assays to measure miRNAs in a biological sample, e.g., a body fluid, can be developed using, for example, methods that involve an extended reverse transcription primer and locked nucleic acid modified PCR. Custom miRNA assays can be tested by running the assay on a dilution series of chemically synthesized miRNA corresponding to the target sequence. This permits determination of the limit of detection and linear range of quantitation of each assay. Furthermore, when used as a standard curve, these data permit an estimate of the absolute abundance of miRNAs measured in biological samples.

[0208] Amplification curves may optionally be checked to verify that Ct values are assessed in the linear range of each amplification plot. Typically, the linear range spans several orders of magnitude. For each candidate miRNA assayed, a chemically synthesized version of the miRNA can be obtained and analyzed in a dilution series to determine the limit of sensitivity of the assay, and the linear range of quantitation. Relative expression levels may be determined, for example, as described by Livak et al. (2001).

[0209] In some embodiments, two or more miRNAs are amplified in a single reaction volume. For example, multiplex q-PCR, such as qRT-PCR, enables simultaneous amplification and quantification of at least two miRNAs of interest in one reaction volume by using more than one pair of primers and / or more than one probe. The primer pairs comprise at least one amplification primer that specifically binds each miRNA, and the probes are labeled such that they are distinguishable from one another, thus allowing simultaneous quantification of multiple miRNAs. Rolling circle amplification is a DNA-polymerase driven reaction that can replicate circularized oligonucleotide probes with either linear or geometric kinetics under isothermal conditions (see, for example, Lizardi et al., 1998; Gusev et al., 2001; Nallur et al., 2001). In the presence of two primers, one hybridizing to the (+) strand of DNA, and the other hybridizing to the (-) strand, a complex pattern of strand displacement results in the generation of over 10A9 copies of each DNA molecule in 90 minutes or less. Tandemly linked copies of a closed circle DNA molecule may be formed by using a single primer. The process can also be performed using a matrix-associated DNA. The template used for rolling circle amplification may be reverse transcribed. This method can be used as a highly sensitive indicator of miRNA sequence and expression level at very low miRNA concentrations (see, for example, Cheng et al., 2009; Neubacher et al., 2009).

[0210] Hybridization-Based Methods

[0211] MicroRNA may be detected using hybridization-based methods, including but not limited to hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched DNA (bDNA) signal amplification, and in situ hybridization.

[0212] Microarrays can be used to measure the expression levels of large numbers of miRNAs simultaneously. Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, photolithography using premade masks, photolithography using dynamic micromirror devices, ink-jet printing, or electrochemistry on microelectrode arrays. Also useful are microfluidic TaqMan Low- Density Arrays, which are based on an array of microfluidic qRT-PCR reactions, as well as related microfluidic qRT-PCR based methods.

[0213] Axon B-4000 scanner and Gene-Pix Pro 4.0 software or other suitable software can be used to scan images. Non-positive spots after background subtraction, and outliers detected by the ESD procedure, are removed. The resulting signal intensity values are normalized to per-chip median values and then used to obtain geometric means and standard errors for each miRNA. Each signal can be transformed to log base 2, and a one-sample t test can be conducted. Independent hybridizations for each sample can be performed on chips with each miRNA spotted multiple times to increase the robustness of the data.

[0214] Microarrays can be used for the expression profiling of miRNAs in diseases. For example, RNA can be extracted from a sample and, optionally, the miRNAs are size- selected from total RNA. Oligonucleotide linkers can be attached to the 5' and 3' ends of the miRNAs and the resulting ligation products are used as templates for an RT-PCR reaction. The sense strand PCR primer can have a fluorophore attached to its 5' end, thereby labeling the sense strand of the PCR product. The PCR product is denatured and then hybridized to the microarray. A PCR product, referred to as the target nucleic acid that is complementary to the corresponding miRNA capture probe sequence on the array will hybridize, via base pairing, to the spot at which the capture probes are affixed. The spot will then fluoresce when excited using a microarray laser scanner.

[0215] The fluorescence intensity of each spot is then evaluated in terms of the number of copies of a particular miRNA, using a number of positive and negative controls and array data normalization methods, which will result in assessment of the level of expression of a particular miRNA.

[0216] Total RNA containing the miRNA extracted from a body fluid sample can also be used directly without size-selection of the miRNAs. For example, the RNA can be 3' end labeled using T4 RNA ligase and a fluorophore-labeled short RNA linker. Fluorophore-labeled miRNAs complementary to the corresponding miRNA capture probe sequences on the array hybridize, via base pairing, to the spot at which the capture probes are affixed. The fluorescence intensity of each spot is then evaluated in terms of the number of copies of a particular miRNA, using a number of positive and negative controls and array data normalization methods, which will result in assessment of the level of expression of a particular miRNA.

[0217] Several types of microarrays can be employed including, but not limited to, spotted oligonucleotide microarrays, pre-fabricated oligonucleotide microarrays or spotted long oligonucleotide arrays.

[0218] MicroRNAs can also be detected without amplification using the nCounter Analysis System (NanoString Technologies, Seattle, Wash.). This technology employs two nucleic acid-based probes that hybridize in solution (e.g., a reporter probe and a capture probe). After hybridization, excess probes are removed, and probe / target complexes are analyzed in accordance with the manufacturer's protocol. nCounter miRNA assay kits are available from NanoString Technologies, which are capable of distinguishing between highly similar miRNAs with great specificity.

[0219] MicroiRNAs can also be detected using branched DNA (bDNA) signal amplification (see, for example, Urdea, 1994). miRNA assays based on bDNA signal amplification are commercially available. One such assay is the QuantiGene.RTM. 2.0 miRNA Assay (Affymetrix, Santa Clara, Calif.).

[0220] Northern Blot and in situ hybridization may also be used to detect miRNAs. Suitable methods for performing Northern Blot and in situ hybridization are known in the art. Sequencing-Based Methods

[0221] Advanced sequencing methods can likewise be used as available. For example, miRNAs can be detected using Illumina. Next Generation Sequencing (e.g., Sequencing- By-Synthesis or TruSeq methods, using, for example, the HiSeq, HiScan, GenomeAnalyzer, or MiSeq systems (Illumina, Inc., San Diego, Calif.)). miRNAs can also be detected using Ion Torrent Sequencing (Ion Torrent Systems, Inc., Gulliford, Conn.), or other suitable methods of semiconductor sequencing.

[0222] CRISPR-Based Methods

[0223] CRISPR (clustered regularly interspaced short palindromic repeats) based methods can also be used for detection of the miRNAs of the invention. Such methods are described in or can be adapted from the methods therein e.g. in Aquino-Jarquin et al. (2021), Urban et al. (2019), Collins et al. (2021), and Makhawi et al. (2021). In an embodiment, the CRISPR-based method uses a Cast 3 nuclease. In an embodiment, the CRISPR based method uses a Cast 3a nuclease. In an embodiment, the CRISPR-based method uses a Cast 2 nuclease. In an embodiment, the CRISPR-based method uses a Casl2a nuclease. In an embodiment, the CRISPR-based method uses a Csm6 nuclease. Examples of CRISPR-based methods for miRNA detection include: a single step Cas 13a-Triggered signal amplification assay (Single-Step assay); cascade CRISPR-casl3 (casCRISPR) assay; ddDasl3a assay; DNA endonuclease-targeted CRISPR trans reporter assay (DETECTR), specific high-sensitivity enzymatic reporter unlocking assay (SHERLOCK), naked-eye-CRISPR assay; Casl3a-based visual detection (vCas) assay; electrochemical CRISPR / CHDC assay (EM-CRISPR); CRISPR-Biosensor X assay; CRISPR-Casl3a powered portable ECL chip (PECL-CRISPR) assay; and Cas-CHDC- Powered Electrochemical RNA Sensing Technology (COMET) assay. In an embodiment, the readout from the CRISPR-based assay is selected from: colometry, electrochemical, fluorescence, lateral flow or electrochemiluminescence. In an embodiment, the CRISPR-based method is an amplification free method (e.g. a electrochemical microfluidic biosensor method). In an embodiment, the method uses electrical interference to detect the miRNA, for example as described in Urban et al (2019). In an embodiment, the CRISPR-based method determines the presence or absence of an miRNA. In an embodiment, the CRISPR-based method the level of an miRNA. In an embodiment, the CRISPR-based method determines the relative level of an miRNA. Additional miRNA Detection Tools

[0224] Mass spectroscopy can be used to quantify miRNA using RNase mapping. Isolated RNAs can be enzymatically digested with RNA endonucleases (RNases) having high specificity (e.g., RNase Tl, which cleaves at the 3 '-side of all unmodified guanosine residues) prior to their analysis by MS or tandem MS (MS / MS) approaches. The first approach developed utilized the on-line chromatographic separation of endonuclease digests by reversed phase HPLC coupled directly to ESI-MS. The presence of post- transcriptional modifications can be revealed by mass shifts from those expected based upon the RNA sequence. Ions of anomalous mass / charge values can then be isolated for tandem MS sequencing to locate the sequence placement of the post-transcriptionally modified nucleoside.

[0225] Matrix-assisted laser desorption / ionization mass spectrometry (MALDI-MS) has also been used as an analytical approach for obtaining information about post- transcriptionally modified nucleosides. MALDI-based approaches can be differentiated from ESI-based approaches by the separation step. In MALDI-MS, the mass spectrometer is used to separate the miRNA.

[0226] To analyze a limited quantity of intact miRNAs, a system of capillary LC coupled with nanoESLMS can be employed, by using a linear ion trap-orbitrap hybrid mass spectrometer (LTQ Orbitrap XL, Thermo Fisher Scientific) or a tandem-quadrupole time-of-flight mass spectrometer (QSTAR XL, Applied Biosystems) equipped with a custom-made nanospray ion source, a Nanovolume Valve (Valeo Instruments), and a splitless nano HPLC system (DiNa, KYA Technologies). Analyte / TEAA is loaded onto a nano-LC trap column, desalted, and then concentrated. Intact miRNAs are eluted from the trap column and directly injected into a Cl 8 capillary column, and chromatographed by RP-HPLC using a gradient of solvents of increasing polarity. The chromatographic eluent is sprayed from a sprayer tip attached to the capillary column, using an ionization voltage that allows ions to be scanned in the negative polarity mode.

[0227] Additional methods for miRNA detection and measurement include, for example, electrical interference (Ye et al., 2019), strand invasion assay (Third Wave Technologies, Inc.), surface plasmon resonance (SPR), cDNA, MTDNA (metallic DNA; Advance Technologies, Saskatoon, SK), and single-molecule methods such as the one developed by US Genomics. Multiple miRNAs can be detected in a microarray format using a novel approach that combines a surface enzyme reaction with nanoparticle-amplified SPR imaging (SPRI). The surface reaction of poly(A) polymerase creates poly(A) tails on miRNAs hybridized onto locked nucleic acid (LNA) microarrays. DNA-modified nanoparticles are then adsorbed onto the poly(A) tails and detected with SPRI. This ultrasensitive nanoparticle-amplified SPRI methodology can be used for miRNA profiling at attomole levels. In some embodiments, the method is a CRISPR-based electrical interference method.

[0228] Other methods include nanomaterials e.g. gold nanoparticles (AuNPs), magnetic nanoparticles, silver nanoclusters (AgNCs), and quantum dots (QDs) (Ye et al., 2019).

[0229] Detection of Amplified or Non-Amplified miRNAs

[0230] In certain embodiments, labels, dyes, or labeled probes and / or primers are used to detect amplified or unamplified miRNAs. The skilled artisan will recognize which detection methods are appropriate based on the sensitivity of the detection method and the abundance of the target. Depending on the sensitivity of the detection method and the abundance of the target, amplification may or may not be required prior to detection. One skilled in the art will recognize the detection methods where miRNA amplification is preferred.

[0231] A probe or primer may include standard (A, T or U, G and C) bases, or modified bases. Modified bases include, but are not limited to, the AEGIS bases (from Eragen Biosciences), which have been described, e.g., in U.S. Pat. Nos. 5,432,272, 5,965,364, and 6,001,983. In certain aspects, bases are joined by a natural phosphodiester bond or a different chemical linkage. Different chemical linkages include, but are not limited to, a peptide bond or a Locked Nucleic Acid (LNA) linkage, which is described, e.g., in U.S. Pat. No. 7,060,809.

[0232] In a further aspect, oligonucleotide probes or primers present in an amplification reaction are suitable for monitoring the amount of amplification product produced as a function of time. In certain aspects, probes having different single stranded versus double stranded character are used to detect the nucleic acid. Probes include, but are not limited to, the 5'-exonuclease assay (e.g., TAQMAN) probes (see U.S. Pat. No. 5,538,848), stemloop molecular beacons (see, e.g., U.S. Pat. Nos. 6,103,476 and 5,925,517), stemless or linear beacons (see, e.g., WO 9921881, U.S. Pat. Nos. 6,485,901 and 6,649,349), peptide nucleic acid (PNA) Molecular Beacons (see, e.g., U.S. Pat. Nos. 6,355,421 and 6,593,091), linear PNA beacons (see, e.g. U.S. Pat. No. 6,329,144), non-FRET probes (see, e.g., U.S. Pat. No. 6,150,097), Sunrise. TM. / AmplifluorB.TM. probes (see, e.g., U.S. Pat. No. 6,548,250), stem-loop and duplex SCORPION probes (see, e.g., U.S. Pat. No. 6,589,743), bulge loop probes (see, e.g., U.S. Pat. No. 6,590,091), pseudo knot probes (see, e.g., U.S. Pat. No. 6,548,250), cyclicons (see, e.g., U.S. Pat. No. 6,383,752), MGB Eclipse. TM. probe (Epoch Biosciences), hairpin probes (see, e.g., U.S. Pat. No. 6,596,490), PNA light-up probes, antiprimer quench probes (Li et al., 2006), self- assembled nanoparticle probes, and ferrocene-modified probes described, for example, in U.S. Pat. No. 6,485,901.

[0233] In certain embodiments, one or more of the primers in an amplification reaction can include a label. In yet further embodiments, different probes or primers comprise detectable labels that are distinguishable from one another. In some embodiments a nucleic acid, such as the probe or primer, may be labeled with two or more distinguishable labels.

[0234] In some aspects, a label is attached to one or more probes and has one or more of the following properties: (i) provides a detectable signal; (ii) interacts with a second label to modify the detectable signal provided by the second label, e.g., FRET (Fluorescent Resonance Energy Transfer); (iii) stabilizes hybridization, e.g., duplex formation; and (iv) provides a member of a binding complex or affinity set, e.g., affinity, antibodyantigen, ionic complexes, hapten-ligand (e.g., biotin-avidin). In still other aspects, use of labels can be accomplished using any one of a large number of known techniques employing known labels, linkages, linking groups, reagents, reaction conditions, and analysis and purification methods.

[0235] MicroRNAs can be detected by direct or indirect methods. In a direct detection method, one or more miRNAs are detected by a detectable label that is linked to a nucleic acid molecule. In such methods, the miRNAs may be labeled prior to binding to the probe. Therefore, binding is detected by screening for the labeled miRNA that is bound to the probe. The probe is optionally linked to a bead in the reaction volume.

[0236] In certain embodiments, nucleic acids are detected by direct binding with a labeled probe, and the probe is subsequently detected. In some embodiments, the nucleic acids, such as amplified miRNAs, are detected using FlexMAP Microspheres (Luminex) conjugated with probes to capture the desired nucleic acids. Some methods may involve detection with polynucleotide probes modified with fluorescent labels or branched DNA (bDNA) detection, for example.

[0237] In some embodiments, biomarker expression is determined using a PCR-based assay comprising specific primers and / or probes for each biomarker. As used herein, the term “probe” refers to any molecule that is capable of selectively binding a specifically intended target biomolecule. In some embodiments, herein, the term “probe” refers to any molecule that may bind or associate, indirectly or directly, covalently or non- covalently, to any of the substrates and / or reaction products and / or proteases disclosed herein and whose association or binding is detectable using the methods disclosed herein. In some embodiments, the probe is a fluorogenic probe, antibody or absorbance-based probes. If an absorbance-based probe, the chromophore pNA (para-nitroanaline) may be used as a probe for detection and / or quantification of a target nucleic acid sequence disclosed herein. In some embodiments, the probe may be a nucleic acid sequence comprising a fluorogenic molecule or a substrate that when exposed to an enzyme becomes fluorogenic and the nucleic acid sequence is complementary or substantially complementary to the nucleic acid sequence comprising at least about 70%, 80%, 81%, 82%, 83%, 84, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity to any of the miRNAs provided herein.

[0238] Probes can be synthesized by one of skill in the art using known techniques, or derived from biological preparations. Probes may include but are not limited to, RNA, DNA, proteins, peptides, aptamers, antibodies, and organic molecules. The term “primer” or “probe” encompasses oligonucleotides that have a specific sequence that is complimentary or substantially complimentary to any one or combination of nucleic acid sequences identified herein.

[0239] In other embodiments, nucleic acids are detected by indirect detection methods. For example, a biotinylated probe may be combined with a streptavidin-conjugated dye to detect the bound nucleic acid. The streptavidin molecule binds a biotin label on amplified miRNA, and the bound miRNA is detected by detecting the dye molecule attached to the streptavidin molecule. In one embodiment, the streptavidin-conjugated dye molecule comprises PHYCOLINK. Streptavidin R-Phycoerythrin (PROzyme). Other conjugated dye molecules are known to persons skilled in the art.

[0240] Labels include, but are not limited to, light-emitting, light-scattering, and lightabsorbing compounds which generate or quench a detectable fluorescent, chemiluminescent, or bioluminescent signal (see, e.g., Kricka, 1992; Garman, 1997). A dual labeled fluorescent probe that includes a reporter fluorophore and a quencher fluorophore is used in some embodiments. It will be appreciated that pairs of fluorophores are chosen that have distinct emission spectra so that they can be easily distinguished.

[0241] In certain embodiments, labels are hybridization-stabilizing moieties which serve to enhance, stabilize, or influence hybridization of duplexes, e.g., intercalators and intercalating dyes (including, but not limited to, ethidium bromide and SYBR-Green), minor-groove binders, and cross-linking functional groups (see, e.g., Blackbum et al., 1996).

[0242] In other embodiments, methods relying on hybridization and / or ligation to quantify miRNAs may be used, including oligonucleotide ligation (OLA) methods and methods that allow a distinguishable probe that hybridizes to the target nucleic acid sequence to be separated from an unbound probe. As an example, HARP-like probes, as disclosed in U.S. 2006 / 0078894 may be used to measure the quantity of miRNAs. In such methods, after hybridization between a probe and the targeted nucleic acid, the probe is modified to distinguish the hybridized probe from the unhybridized probe. Thereafter, the probe may be amplified and / or detected. In general, a probe inactivation region comprises a subset of nucleotides within the target hybridization region of the probe. To reduce or prevent amplification or detection of a HARP probe that is not hybridized to its target nucleic acid, and thus allow detection of the target nucleic acid, a post-hybridization probe inactivation step is carried out using an agent which is able to distinguish between a HARP probe that is hybridized to its targeted nucleic acid sequence and the corresponding unhybridized HARP probe. The agent is able to inactivate or modify the unhybridized HARP probe such that it cannot be amplified. A probe ligation reaction may also be used to quantify miRNAs. In a Multiplex Ligation-dependent Probe Amplification (MLPA) technique (Schouten et al., 2002), pairs of probes which hybridize immediately adjacent to each other on the target nucleic acid are ligated to each other driven by the presence of the target nucleic acid. In some aspects, MLPA probes have flanking PCR primer binding sites. MLPA probes are specifically amplified when ligated, thus allowing for detection and quantification of miRNA biomarkers.

[0243] In other embodiments, the miRNA can be detected using an isothermal exponential amplification method, a rolling cycle amplification based method, a cleavage based method, a gold particle (AuNPs)-based method, a duplex specific nuclease (DSN) and AuNPs-based system quantum dot-based method or capillary-electrophoreses-based assay method, an AuNPs-based method, an DSN and AuNPs-based system quantum dotbased method or capillary-electrophoreses-based as described in Tian et al. (2015).

[0244] In some embodiments, the method comprises performing an assay on a sample from an animal to determine the miRNA expression profile of the animal.

[0245] In some embodiments, the miRNA can be detected using real-time reverse transcription-PCR (qRT-PCR), microarray hybridization, a multiplex miRNA profiling assay, massively parallel / next generation sequencing also referred to as “NGS sequencing,” RNA-ish, northern blotting or colorimetric sensor-based analysis.

[0246] In some embodiments, the next generation sequencing is selected from: RNA-seq, small RNA-seq, and miRNA-seq.

[0247] Detection includes methods comprising direct labelling of a miRNA (e.g. with a modified nucleotide, labelled nucleotide or tag incorporated into the miRNA) or binding of the miRNA with a binding molecule which binds a miRNA or a truncated version thereof forming a miRNA-binding molecule complex. In some embodiments, the binding molecule is selected from: i) a polynucleotide, ii) an aptamer, iii) an antibody. In some embodiments, the polynucleotide is complementary to the miRNA or a truncated version thereof or detects a tag attached to the miRNA. In some embodiments, the polynucleotide is a primer.

[0248] In some embodiments, the binding molecule is detectably labelled or capable of binding a detectable label. In some embodiments, the binding molecule is linked to an enzyme, enzyme substrate, a fluorescent or fluorescent substrate, chemiluminescent molecule, chemiluminescent substrate, purification tag and / or a solid support. In some embodiments the miRNA-binding complex is directly or indirectly detected.

[0249] In some embodiments, the detection method is a reverse transcription and quantitative PCR (RT-qPCR or qRT-PCR) assay. In some embodiments, the primer for reverse transcription can be a stem-loop specific RT primer or a universal primer if the miRNAs have undergone prior 3’ poly- A tailing and 5’ adaptor ligation, for example the TaqMan Advanced miRNA cDNA Synthesis kit from Applied Biosystems. In some embodiments, the detection method is fluorescence.

[0250] Aptamer-Based Electrochemical Biosensor

[0251] In an embodiment, the miRNA(s) are detected using an aptamer-based electrochemical biosensor.

[0252] Electrochemical aptamer-based (E-AB) biosensors generate an electrochemical signal in response to specific target binding. The signal is measured by a change in Faradaic current passed through an electrode. E-AB sensors are advantageous over previously reported aptamer-based sensors, such as fluorescence generating aptamers, due to their ability to detect target binding in vivo with real-time measurements. An E- AB sensor is composed of a three-electrode cell: an interrogating (or working) electrode, a reference electrode, and a counter electrode. A signal is generated within the electrochemical cell then measured and analyzed by a potentiostat. Several biochemical and electrochemical parameters optimize signal gain for E-AB biosensors. The density packing of DNA or RNA aptamers, the ACV frequency administered by the potentiostat, and the chemistry of the self assembling monolayer (SAM) are all factors that determine signal gain as well as the signal to noise ratio of target binding. See, for example, Bao et al. (2025).

[0253] MicroRNA Analysis Algorithms

[0254] In some embodiments, data that are generated using samples such as “known samples” can then be used to “train” a classification model. A “known sample” is a sample that has been pre-classified, e.g., classified as being derived from a normal subject, from a subject known to have AY. avium subsp. paratuberculosis infection. The data that are derived from a range of sources and are used to form the classification model can be referred to as a “training data set.” Once trained, the classification model can recognize patterns in data derived from spectra generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased).

[0255] In some embodiments, data for the training data set that is used to form the classification model can be obtained from any established method for nucleic acid quantitation. In some embodiments, the data can come directly from quantitative PCR (for example, Ct values obtained using the double delta Ct method), or from high- throughput expression profiling, such as microarray analysis (for example, total counts or normalized counts from a miRNA RNA expression assay).

[0256] Classification models can be formed using any suitable statistical classification (or “learning”) method that attempts to segregate bodies of data into classes based on objective parameters present in the data. Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000.

[0257] In supervised classification, training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships. Examples of supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART— classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).

[0258] Machine learning is a subfield of artificial intelligence in which algorithms are developed to allow systems to “learn”, a process of finding statistical regularities or other patterns of data based on a given dataset, and executing activities that are not specified in the code. There are now numerous machine learning algorithms developed, updated and improved for classifying biological information, for recent examples, see, e.g. Azari et al. (2023) and Jindal et al. (2023). For an overview of the application of machine learning applications in miRNA methodologies, see, e.g. Parveen et al. (2019).

[0259] Machine learning can be further categorised into supervised or unsupervised learning based on whether or not the given data are labelled. In supervised learning such as support vector machines, decision trees, and neural networks, systems use the features of the given data to predict their labels, and uses labelled datasets to train algorithms to predict outcomes and recognise patterns. Many supervised learning techniques have found application in their processing and analysing a variety of biological data. One of the main characteristics is that the supervised learning has the ability of annotated training data.

[0260] In supervised algorithms, the classes are predetermined. These classes are created in a manner of finite set, defined by the human, which in practice means that a certain segment of data will be labelled with these classifications. The task of the machine learning algorithm is to find patterns and construct mathematical models. These models are then evaluated based on the predictive capacity in relation to measures of variance in the data itself. There are a variety of algorithms that are used in the supervised learning methods. For a summary of the fundamental aspects of some supervised methods, see, e.g., Nasteski (2017).

[0261] In unsupervised learning such as clustering, the system is trained entirely on the unlabelled input values, two main supervised models being classification models (classifiers) and regression models. Regression models map the input space into a real- value domain. The classifiers map the input space into pre-defined classes. There are many alternatives for representing classifiers, for example but not limited to, support vector machines, decision trees, probabilistic summaries, and algebraic function.

[0262] Unsupervised learning algorithms automatically develop classification labels, using any identified similarities between data to determine if they can be categorised to create a group. These groups are clusters, and they represent the whole family of clustering machine learning techniques. In this unsupervised classification (cluster analysis) the machine doesn’t know how the clusters are grouped, which can be useful for identifying unknown, novel, and / or unpredicted clusters and for exploring novel relationships between different biological markers.

[0263] As well as supervised and unsupervised machine learning methods, other methods suitable for classifying biological information include:

[0264] • Semi-supervised learning (combines both labelled and unlabelled examples to generate an appropriate function or classifier); • Reinforcement learning (the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm);

[0265] • Transduction (similar to supervised learning, but does not explicitly construct a function, instead trying to predict new outputs based on training inputs, training outputs, and new inputs); and

[0266] • Learning to learn (where the algorithm learns its own inductive bias based on previous experience).

[0267] The training data set(s) and the classification models can be embodied by computer code that is executed or used by a digital computer. The computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including C, C++, visual basic, etc.

[0268] The learning algorithms described above are examples which can be used for developing classification algorithms for miRNAs specific for M. avium subsp. paratuberculosis infection. The classification algorithms can, in turn, be used in diagnostic tests by providing diagnostic values (e.g., cut-off points) for miRNAs used singly or in combination.

[0269] Reference Value

[0270] In an embodiment, a method of the invention comprises comparing the level of the at least one miRNA to a reference value.

[0271] The reference value can be determined, or predetermined, using a wide variety of procedures known in the art including, but not limited to, the reference value is a predetermined level of the at least one miRNA, a predetermined score, the level of the at least one miRNA in a control sample, or the level of the at least one miRNA in a subject who does not have a AL avium subsp. paratuberculosis.

[0272] In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of the at least one miRNA in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least one miRNA. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least two miRNAs. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of at least three miRNAs.

[0273] In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-122 in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-125a in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-194 in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-126-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-339b in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-122, miR-125a and miR-194, in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-122, miR-125a, miR-194 and miR-126-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-122, miR-125a, miR-194, miR-126-5p and miR-339b in the sample.

[0274] In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-497 in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-23b-3p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-126-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-145 in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-194 in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-497, miR-23b-3p and miR-126-5p in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-497, miR-23b-3p, miR-126-5p and miR-145 in the sample. In an embodiment, the miRNA analysis algorithm can assign the sample a score using the level of miR-497, miR-23b-3p, miR-126-5p, miR-145 and miR-194 in the sample.

[0275] As is known in the art, a predetermined score may also be considered a threshold value. More specifically, based on the analysis of a sufficient number of animals with and without a M. avium subsp. paratuberculosis infection, a value (threshold) can be determined such that if the animal has a score based on the level of the at least one (typically two or more) miRNA at or above the threshold it is determined they have a M. avium subsp. paratuberculosis infection. Processes for determining suitable scores and thresholds for a given diagnostic test are well known in the art. In an embodiment, the reference value is a threshold determined by the computer-based miRNA analysis algorithm on training and / or validation data. In some embodiments, the algorithm is based on the level of 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or more miRNA in the sample.

[0276] In an embodiment, the reference value is based upon a machine learning classification algorithm (such as logistic regression) that has been trained using the expression of, for example: one or more or all of : miR-122, miR-125a, miR-194, miR- 497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let- 7b, let-7c, let-7d, let-7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR-12030, miR-1246, miR-125b, miR-1306, miR-138, miR-1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151-5p, miR- 15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR-1839, miR- 185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR-195, miR- 204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR-2284x, miR- 2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419- 5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR- 30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR- 339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR-425-5p, miR- 4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR-6119-5p, miR- 6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR-99a-5p and miR- 99b. Training provides the parameters, or coefficients, to the algorithm to allow it to make a prediction. When a new sample is tested, the resulting data for the, for example, three-miR signature is fed into the algorithm and the probability of that sample coming from someone infected with M. avium subsp. paratuberculosis is produced. If the probability is greater than 0.5 or 50%, then the sample is classified as being M. avium subsp. paratuberculosis positive.

[0277] In an embodiment, the level is an absolute level. In an embodiment, the level is a relative level between two or more miRNAs which may be all associated with a M. avium subsp. paratuberculosis infection in an animal such as those described herein, or the levels of a suitable control miRNA(s) could be used to determine the relative level, where expression of the control miRNA is not associated with a M. avium subsp. paratuberculosis infection.

[0278] In an embodiment, the score factors in patient characteristics such as age, gender, other health conditions.

[0279] In an embodiment, the reference value is the level of the at least one miRNA in a control sample. The reference value may be a standard level of an RNA or miRNA synthetically produced or from a normal control biological sample from one or more animals. In an embodiment, the normal control biological sample is age, gender and / or ethnicity matched to the animal being evaluated by the methods as described herein. In an embodiment, the reference value is the level of the at least one miRNA in a normal control biological sample. In an embodiment, the reference value is the level of the at least one miRNA in animal not having one or both of aA- / . avium subsp. paratuberculosis infection and a non- Mycobacterium avium Johne’s disease causing pathogen. In an embodiment, the reference value is the level of the at least one miRNA in animal not having a M. avium subsp. paratuberculosis infection.

[0280] In an embodiment, an altered level of the at least one miRNA in the biological sample compared to a predetermined reference value indicates the presence of aA- / . avium subsp. paratuberculosis infection.

[0281] In an embodiment, the level of the at least one miRNA is a higher level compared to the reference value, and a higher level of the at least one miRNA is indicative oiM. avium subsp. paratuberculosis infection in the animal.

[0282] In an embodiment, the level of the at least one miRNA is a lower level compared to the reference value, and the lower level of the at least one miRNA is indicative oiM. avium subsp. paratuberculosis infection in the animal.

[0283] In an embodiment, the method has an accuracy of one or more of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, or at least 99%. In an embodiment, the method has an accuracy of at least 90%. In an embodiment, the method has an accuracy of at least 95%. In an embodiment, the method has an accuracy of at least 97%. In an embodiment, the method has an accuracy of at least 99%.

[0284] In an embodiment, the method can distinguish a M. avium subsp. paratuberculosis infection from a non-Mycobaclerium avium Johne’s disease causing pathogen.

[0285] In an embodiment, the method can identify a M. avium subsp. paratuberculosis infection with at least 90% precision. In an embodiment, the method can identify a M. avium subsp. paratuberculosis infection with at least 93% precision. In an embodiment, the method can identify a M. avium subsp. paratuberculosis infection with at least 95% precision. In an embodiment, the method can identify aA- / . avium subsp. paratuberculosis infection with at least 97% precision. In an embodiment, the method can identify a M. avium subsp. paratuberculosis infection with at least 98% precision.

[0286] In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.65. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.7. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.75. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.80. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.85. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.90. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.95. In an embodiment, the area under the curve (AUC) of the at least one miRNA is at least 0.98.

[0287] In an embodiment, the method comprises detecting the level of at least three miRNAs the method has one or more of i) an accuracy of at least about 95%, ii) a precision of at least about 90% and iii) an AUC of at least 0.98.

[0288] In an embodiment, of the methods described herein the level of the at least one miRNA may be normalized. In an embodiment, the level of the at least one miRNA is normalised against a control.

[0289] In an embodiment, the method comprises normalizing the level of the at least one miRNA to obtain a normalized level of the at least one miRNA, and wherein the method comprises comparing the normalised level of the at least one miRNA to the reference value of the at least one miRNA.

[0290] In an embodiment, the control is an endogenous control. In an embodiment, the endogenous control is a small RNA, for example, a miRNA, small non-coding RNA (ncRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), or small nucleolar RNA (snoRNA). In an embodiment, the endogenous control is a miRNA.

[0291] In an embodiment, the control is an exogenous control, for example an exogenous RNA added to the biological sample before miRNA extraction (a spike-in control). Spike-in controls may be added to a sample before RNA, small RNA and / or miRNA is recovered, the amount of the spike-in control recovered after RNA, small RNA and / or miRNA extraction is directly correlated with the amount of total RNA recovered. In an embodiment, the exogenous RNA is isolated from a host source or is synthetic. Synthetic spike-in controls are available from a number of commercial manufactures including for example, Qiagen and Norgen Biotek Corporation and Life Technologies.

[0292] Diagnostic Tests

[0293] The miRNAs described herein can be used individually or in combination in diagnostic tests to assess the presence or the absence of a AL avium subsp. paratuberculosis infection in an animal. In some embodiments, the M. avium subsp. paratuberculosis infection is a sub-clinical infection. The M. avium subsp. paratuberculosis infection status includes the presence or absence of a AL avium subsp. paratuberculosis in the animal. The AL avium subsp. paratuberculosis infection status may also include monitoring the course of the infection, for example, monitoring disease progression. Based on the AL avium subsp. paratuberculosis infection status of an animal, additional procedures may be indicated, including, for example, additional diagnostic tests or therapeutic procedures. The power of a diagnostic test to correctly predict disease status is commonly measured in terms of the accuracy of the assay, the sensitivity of the assay, the specificity of the assay, or the “Area Under a Curve” (AUC), for example, the area under a Receiver Operating Characteristic (ROC) curve. As used herein, accuracy is a measure of the fraction of misclassified samples. Accuracy may be calculated as the total number of correctly classified samples divided by the total number of samples, e.g., in a test population. Sensitivity is a measure of the “true positives” that are predicted by a test to be positive, and may be calculated as the number of correctly identified breast cancer samples divided by the total number of breast cancer samples. Specificity is a measure of the "true negatives" that are predicted by a test to be negative, and may be calculated as the number of correctly identified normal samples divided by the total number of normal samples. AUC is a measure of the area under a Receiver Operating Characteristic curve, which is a plot of sensitivity vs. the false positive rate (1 -specificity). The greater the AUC, the more powerful the predictive value of the test. Other useful measures of the utility of a test include the “positive predictive value,” which is the percentage of actual positives who test as positives, and the “negative predictive value,” which is the percentage of actual negatives who test as negatives. In some embodiments, the level of one or more miRNAs disclosed herein in samples derived from animals having different M. avium subsp. paratuberculosis infection statuses show a statistically significant difference of at least p = 0.05, e.g., p = 0.05, p = 0.01, p = 0.005, p = 0.001, etc. relative to normal animals, as determined relative to a suitable control. In other preferred embodiments, diagnostic tests that use miRNAs described herein individually or in combination show an accuracy of at least about 75%, e.g., an accuracy of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%. In other embodiments, diagnostic tests that use miRNAs described herein individually or in combination show a specificity of at least about 75%, e.g., a specificity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%. In other embodiments, diagnostic tests that use miRNA described herein individually or in combination show a sensitivity of at least about 75%, e.g., a sensitivity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100%. In other embodiments, diagnostic tests that use miRNAs described herein individually or in combination show a specificity and sensitivity of at least about 75% each, e.g., a specificity and sensitivity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100% (for example, a specificity of at least about 80% and sensitivity of at least about 80%, or for example, a specificity of at least about 80% and sensitivity of at least about 95%). Determining the level of the miRNA in a sample may include measuring, detecting, or assaying the level of the miRNA in the sample using any suitable method, for example, the methods described herein elsewhere. Determining the level of the miRNA in a sample may also include examining the results of an assay that measured, detected, or assayed the level of the miRNA in the sample. In some embodiments, the method may also involve comparing the level of the miRNA in a sample with a reference value.

[0294] A change in the level of the miRNA relative to that in a normal animal as assessed using a suitable reference value is indicative of the M. avium subsp. paratuberculosis infection status of the animal. A diagnostic amount of a miRNA that represents an amount of the miRNA above or below which an animal is classified as having a particular M. avium subsp. paratuberculosis infection status can be used. For example, if the miRNA is upregulated in samples derived from an individual having M. avium subsp. paratuberculosis infection as compared to a normal individual (or a reference value), a measured amount above the diagnostic cutoff provides a diagnosis of M. avium subsp. paratuberculosis infection. As is well-understood in the art, adjusting the particular diagnostic cut-off used in an assay allows one to adjust the sensitivity and / or specificity of the diagnostic assay as desired. The particular diagnostic cut-off can be determined, for example, by measuring the amount of the miRNA in a statistically significant number of samples from animals with different M. avium subsp. paratuberculosis infection statuses, and drawing the cut-off at the desired level of accuracy, sensitivity, and / or specificity. In certain embodiments, the diagnostic cut-off can be determined with the assistance of a classification algorithm, as described herein.

[0295] Accordingly, methods are provided for diagnosing a M. avium subsp. paratuberculosis infection or identifying the status thereof in an animal, by determining the level of at least one miRNA in a sample containing small RNAs from the animal, wherein a difference in the level of the at least one disclosed miRNA versus that in a reference value is indicative of avium subsp. paratuberculosis infection in the animal. For example, the present disclosure provides a method of determining the level of at least one miRNA in a sample containing small RNAs derived from the animal, wherein an increase in the level of the at least one miRNA relative to a reference value is indicative of M. avium subsp. paratuberculosis infection in the animal.

[0296] Optionally, the method may further comprise providing a diagnosis that the animal has or does not M. avium subsp. paratuberculosis infection based on the level of at least one miRNA in the sample. In addition, or alternatively, the method may further comprise correlating a difference in the level or levels of at least one miRNA relative to a reference value with a diagnosis of avium subsp. paratuberculosis infection in the animal.

[0297] While individual miRNAs are useful in diagnostic applications for M. avium subsp. paratuberculosis infection, a combination of miRNAs may provide greater predictive value of M. avium subsp. paratuberculosis infection status than a single miRNA when used alone. Specifically, the detection of a plurality of miRNAs can increase the accuracy, sensitivity, and / or specificity of a diagnostic test. The disclosure includes the individual miRNA alone and miRNA combinations as set forth herein, and their use in methods and kits described herein.

[0298] Accordingly, methods are provided for diagnosing M. avium subsp. paratuberculosis infection in an animal, by determining the level of two or more miRNAs as described herein in a sample containing small RNA from the animal.

[0299] Comparison of the sample from the subject with the set of data may be assisted by a classification algorithm, which computes whether or not a statistically significant difference exists between the collective levels of the two or more miRNAs in the sample.

[0300] In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least two miRNAs described herein. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least three miRNAs described herein. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least four miRNAs described herein. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least five miRNAs described herein. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least six miRNAs described herein. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least seven miRNAs described herein. In some embodiments, the methods of the disclosure detect the presence, absence or quantity of at least eight miRNAs described herein.

[0301] In some embodiments wherein more than one miRNA is used in the methods of the disclosure, a combined score that integrates the level of the multiple miRNA biomarkers within a signature serves as the basis of the prediction of infection.

[0302] In some embodiments, the combined score is calculated based on the miRNA level using Equation I below:

[0303] (miR-X level *miR-X weight value) + / - (miR-Y level * miR-Y weight value) + / - (miR-Z level * miR-Z weight value).

[0304] In some embodiment, the score is compared to a reference value as described herein. In some embodiments wherein more than one miRNA is used in the methods of the disclosure, a combined score that integrates the RT-PCR Ct values of the multiple miRNA biomarkers within a signature serves as the basis of the prediction of infection. Such combined score may be a combining function that can be as simple as sum of the Ct values of specific set of miRNAs. Alternatively, the combined score may be determined by logistic regression, other regression techniques, support vector machines, random forests, neural networks, genetic algorithms, annealing algorithms, weighted sums, additive models, linear models, nearest neighbors or probabilistic models.

[0305] In some embodiments, the combined score is the combination of the Ct values of the miRNAs calculated by Equation II below:

[0306] (miR-X CT *miR-X weight value) + / - (miR-Y CT * miR-Y weight value) + / - (miR-Z CT * miR-Z weight value).

[0307] In Equation I and II, X, Y and Z refer to different miRNAs. In some embodiments, the X, Y and Z miRNAs contribute equally to the score (in such embodiments they are all weighted 1). In some embodiments, one or more of the X, Y and Z miRNAs do not contribute equally to the score (in such embodiments one or more of the miRNAs are assigned a different weight value from one or more of the other miRNA). In some embodiment, the score is compared to a reference value as described herein.

[0308] In some embodiments, the combined score is the linear combination of the Ct values of the miRNAs calculated by Equation III below:

[0309] Ct(miRNAl) + Ct(miRNA2) - Ct(miRNA3)

[0310] In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 10 to about 50. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 15 to about 45. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 20 to about 40. In some embodiments, the Ct value of each of the miRNAs used in the prediction ranges from about 25 to about 35. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 15. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 20. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 25. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 30. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 35. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 40. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 45. In some embodiments, the Ct value of each of the miRNAs used in the prediction is about 50. In an embodiment, the score is a prediction score of the probability of an infection, and is between 0 and 1.

[0311] In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 10 to about 60. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 12 to about 55. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 14 to about 50. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 15 to about 45. In some embodiments, the maximum value of the combining function or combined score for the prediction is from about 20 to about 40. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 10. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 15. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 20. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 25. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 30. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 35. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 40. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 45. In some embodiments, the maximum value of the combining function or combined score for the prediction is about 50.

[0312] In some embodiments, to establish a set of interpretive ranges and a diagnostic threshold, the combined score can be calculated for a set of about 60 patient samples and the scores bucketed into different groups based on the combined score. In some embodiments, the combined score of from about 10 to about 21 is used as the diagnostic threshold. In some embodiments, the combined score of from about 15 to about 21 is used as the diagnostic threshold. In some embodiments, the combined score of from about 21 to about 27 is used as the diagnostic threshold. In some embodiments, the combined score of from about 27 to about 39 is used as the diagnostic threshold. In some embodiments, the combined score of from about 39 to about 45 is used as the diagnostic threshold. Systems

[0313] The above described methods can be implemented in any of numerous ways. For example, the embodiments may be implemented using a computer program product (i.e. software), hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.

[0314] Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, a tablet computer, or cloud computing. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

[0315] Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.

[0316] Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

[0317] A computer employed to implement at least a portion of the functionality described herein may include a memory, coupled to one or more processing units (also referred to herein simply as “processors”), one or more communication interfaces, one or more display units, and one or more user input devices. The memory may include any computer-readable media, and may store computer instructions (also referred to herein as “processor-executable instructions”) for implementing the various functionalities described herein. The processing unit(s) may be used to execute the instructions. The communication interface(s) may be coupled to a wired or wireless network, bus, or other communication means and may therefore allow the computer to transmit communications to and / or receive communications from other devices. The display unit(s) may be provided, for example, to allow a user to view various information in connection with execution of the instructions. The user input device(s) may be provided, for example, to allow the user to make manual adjustments, make selections, enter data or various other information, and / or interact in any of a variety of manners with the processor during execution of the instructions.

[0318] The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and / or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

[0319] In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention disclosed herein. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above. In some embodiments, the system comprises cloud-based software that executes one or all of the steps of each disclosed method instruction.

[0320] The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.

[0321] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments. Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

[0322] Also, the disclosure relates to various embodiments in which one or more disclosed methods are used. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

[0323] In some embodiments, the disclosure relates to a system that comprises at least one processor, a program storage, such as memory, for storing program code executable on the processor, and one or more input / output devices and / or interfaces, such as data communication and / or peripheral devices and / or interfaces. In some embodiments, the user device and computer system or systems are communicably connected by a data communication network, such as a Local Area Network (LAN), the Internet, or the like, which may also be connected to a number of other client and / or server computer systems. The user device and client and / or server computer systems may further include appropriate operating system software.

[0324] In some embodiments, components and / or units of the devices described herein may be able to interact through one or more communication channels or mediums or links, for example, a shared access medium, a global communication network, the Internet, the World Wide Web, a wired network, a wireless network, a combination of one or more wired networks and / or one or more wireless networks, one or more communication networks, an a-synchronic or asynchronous wireless network, a synchronic wireless network, a managed wireless network, a non-managed wireless network, a burstable wireless network, a non-burstable wireless network, a scheduled wireless network, a non-scheduled wireless network, or the like.

[0325] Discussions herein utilizing terms such as, for example, “processing,” “computing,” “calculating,” “determining,” or the like, may refer to operation(s) and / or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulate and / or transform data represented as physical (e.g., electronic) quantities within the computer's registers and / or memories into other data similarly represented as physical quantities within the computer’s registers and / or memories or other information storage medium that may store instructions to perform operations and / or processes.

[0326] Some embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment including both hardware and software elements. Some embodiments may be implemented in software, which includes but is not limited to firmware, resident software, microcode, or the like.

[0327] Furthermore, some embodiments may take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For example, a computer-usable or computer-readable medium may be or may include any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0328] In some embodiments, the medium may be or may include an electronic, magnetic, optical, electromagnetic, InfraRed (IR), or semiconductor system (or apparatus or device) or a propagation medium. Some demonstrative examples of a computer- readable medium may include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a Random Access Memory (RAM), a Read-Only Memory (ROM), a rigid magnetic disk, an optical disk, or the like. Some demonstrative examples of optical disks include Compact Disk-Read-Only Memory (CD-ROM), Compact Disk- Read / Write (CD-R / W), DVD, or the like.

[0329] In some embodiments, a data processing system suitable for storing and / or executing program code may include at least one processor coupled directly or indirectly to memory elements, for example, through a system bus. The memory elements may include, for example, local memory employed during actual execution of the program code, bulk storage, and cache memories which may provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.

[0330] In some embodiments, input / output or I / O devices (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system either directly or through intervening I / O controllers. In some embodiments, network adapters may be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices, for example, through intervening private or public networks. In some embodiments, modems, cable modems and Ethernet cards are demonstrative examples of types of network adapters. Other suitable components may be used.

[0331] Some embodiments may be implemented by software, by hardware, or by any combination of software and / or hardware as may be suitable for specific applications or in accordance with specific design requirements. Some embodiments may include units and / or sub-units, which may be separate of each other or combined together, in whole or in part, and may be implemented using specific, multi-purpose or general processors or controllers. Some embodiments may include buffers, registers, stacks, storage units and / or memory units, for temporary or long-term storage of data or in order to facilitate the operation of particular implementations.

[0332] Some embodiments may be implemented, for example, using a machine-readable medium or article which may store an instruction or a set of instructions that, if executed by a machine, cause the machine to perform a method steps and / or operations described herein. Such machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, electronic device, electronic system, computing system, processing system, computer, processor, or the like, and may be implemented using any suitable combination of hardware and / or software. The machine-readable medium or article may include, for example, any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and / or storage unit; for example, memory, removable or nonremovable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk drive, floppy disk, Compact Disk Read Only Memory (CD-ROM), Compact Disk Recordable (CD-R), Compact Disk Re-Writeable (CD-RW), optical disk, magnetic media, various types of Digital Versatile Disks (DVDs), a tape, a cassette, or the like. The instructions may include any suitable type of code, for example, source code, compiled code, interpreted code, executable code, static code, dynamic code, or the like, and may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled and / or interpreted programming language, e.g., C, C++, Java™, BASIC, Pascal, Fortran, Cobol, assembly language, machine code, or the like.

[0333] In some embodiments, the methods described herein comprise a circuit. For example, a circuit may be implemented as a hardware circuit comprising custom very- large-scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A circuit may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like. In some embodiment, the circuits may also be implemented in machine-readable medium for execution by various types of processors. An identified circuit of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified circuit need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the circuit and achieve the stated purpose for the circuit. Indeed, a circuit of computer readable program code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within circuits, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.

[0334] The computer readable medium (also referred to herein as machine-readable media or machine-readable content) may be a tangible computer readable storage medium storing the computer readable program code. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. As alluded to above, examples of the computer readable storage medium may include but are not limited to a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, a holographic storage medium, a micromechanical storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, and / or store computer readable program code for use by and / or in connection with an instruction execution system, apparatus, or device.

[0335] The computer readable medium may also be a computer readable signal medium. A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electrical, electro-magnetic, magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport computer readable program code for use by or in connection with an instruction execution system, apparatus, or device. As also alluded to above, computer readable program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), or the like, or any suitable combination of the foregoing. In one embodiment, the computer readable medium may comprise a combination of one or more computer readable storage mediums and one or more computer readable signal mediums. For example, computer readable program code may be both propagated as an electro-magnetic signal through a fiber optic cable for execution by a processor and stored on RAM storage device for execution by the processor.

[0336] Computer readable program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program code may execute entirely on a user's computer, partly on the user’s computer, as a stand-alone computer-readable package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0337] The program code may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the schematic flowchart diagrams and / or schematic block diagrams block or blocks.

[0338] Functions, operations, components and / or features described herein with reference to one or more embodiments, may be combined with, or may be utilized in combination with, one or more other functions, operations, components and / or features described herein with reference to one or more other embodiments, or vice versa. Panels and Kits

[0339] The present disclosure provides panels or kits for determining the likelihood of a M. avium subsp. paratuberculosis infection in an animal. The present disclosure also provides panels or kits for determining the likelihood an animal has Johne’s disease. The present disclosure further provides panels or kits for determining if an animal has Mycobacterium avium subsp. paratuberculosis infection. The present disclosure further provides panels or kits for determining if an animal has Johne’s disease.

[0340] In some embodiments, the M. avium subsp. paratuberculosis infection is a sub- clinical infection.

[0341] The kits of the disclosure will preferably comprise a nucleotide array comprising miRNA-specific probes and / or oligonucleotides for amplifying at least one miRNA described herein.

[0342] In an embodiment, the probe is a nucleic acid aptamer.

[0343] In an embodiment, the probe is attached to an electrode surface.

[0344] In some embodiments, the one or more reagents for detecting at least one at least one miRNA comprises a binding molecule which binds a miRNA or a truncated version thereof. In some embodiments, the binding molecule is a polynucleotide, aptamer or antibody. In some embodiments, the binding molecule is detectably labelled.

[0345] In some embodiments, the panel or kit further comprises a reference value as described herein. In some embodiments, the reference value comprises a standard curve of at least one miRNA as described herein. In some embodiments, the panel or kit further comprises a control as described herein. In some embodiments, the panel or kit further comprises a standard curve of the control as described herein.

[0346] In some embodiments, the panel or kit further comprises one or more reagents for detecting the level of a control. In some embodiments, the one or more reagents in a binding molecule which binds an exogenous control as described herein. In some embodiments, the binding molecule is detectably labelled.

[0347] In some embodiments, the reference value comprises a predetermined threshold of a miRNA described herein.

[0348] In an aspect, the disclosure provides a nucleotide array for determining the likelihood of a M. avium subsp. paratuberculosis infection in an animal, the nucleotide array comprising miRNA-specific probes for at least one miRNA as described herein.

[0349] In some embodiments, the panel or kit as described herein is for next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, a microarray, a multiplex miRNA profiling assay, RNA-ish, or northern blotting. In some embodiments, the panel or kit comprises not more than about 50 miRNA.

[0350] In some embodiments, the panel or kit comprises not more than about 40 miRNA. In some embodiments, the panel or kit comprises not more than about 30 miRNA. In some embodiments, the panel or kit comprises not more than about 20 miRNA. In some embodiments, the panel or kit comprises not more than about 10 miRNA. In some embodiments, the panel or kit comprises not more than about 5 miRNA. In some embodiments, the panel or kit comprises not more than about 3 miRNA.

[0351] In some embodiments, the panel or kit as described herein is for ex vivo analysis. In some embodiments, the kit is suitable for use with blood samples or a fraction there of e.g. blood or serum.

[0352] In some embodiment, the kit uses electrical interference to detect the miRNA. In some embodiment, the kit comprises a nanomaterial to detect the miRNA.

[0353] In some embodiments, the kit uses a CRISPR-based method to detect the miRNA. In some embodiment, the kit comprises a Cast 3 nuclease. In some embodiments, the kit comprises a Casl3a nuclease.

[0354] In some embodiments, the panel or kit as described herein is suitable for high- throughput screening. The term “high-throughput screening” refers to screening methods that can be used to test or assess more than one sample at a time and that can reduce the time for testing multiple samples. In some embodiments, the methods are suitable for testing or assessing at least about 5 samples, at least about 10, at least about 20, at least about 30, at least about 50, at least about 70, at least about 90, at least about 150, at least about 200, at least about 300 samples at a time. Such high-throughput screening methods can analyse more than one sample rapidly e.g. in at least about 30 minutes, in at least about 1 hour, in at least about 2 hours, in at least about 3 hours, in at least about 4 hours, in at least about 5 hours, in at least about 6 hours, in at least about 7 hours, in at least about 8 hours, in at least about 9 hours or in at least about 10 hours. High-throughput screening may also involve the use of liquid handling devices. In some embodiments, high-throughput analysis may be automated.

[0355] Methods of Management

[0356] In one aspect, the present invention provides a method of managing a group of animals, the method comprising determining the likelihood of Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using a method of the invention or a panel or kit of the invention, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

[0357] In an embodiment, the animals removed are destroyed. In another aspect, the present invention provides a method of treating or preventing a Mycobacterium avium subsp. paratuberculosis infection in an animal, the method comprising i) determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal using a method of the invention or a panel or kit of the invention, ii) administering a treatment for a Mycobacterium avium subsp. paratuberculosis infection if it is determined the animal is likely to have a Mycobacterium avium subsp. paratuberculosis infection.

[0358] Also provided is the use of an &ni\Mycobaclerium avium subsp. paratuberculosis compound for the manufacture of a medicament for the treatment or prevention of a Mycobacterium avium subsp. paratuberculosis infection in an animal, wherein it has been determined that it is likely the animal has a Mycobacterium avium subsp. paratuberculosis infection using the method of the invention or the panel or kit of the invention.

[0359] In a further aspect, the present invention provides for the use of an anti- Mycobacterium avium subsp. paratuberculosis compound for the treatment or prevention of a Mycobacterium avium subsp. paratuberculosis infection in an animal, wherein it has been determined that it is likely the animal has a Mycobacterium avium subsp. paratuberculosis infection using a method of the invention or a panel or kit of the invention.

[0360] In some embodiments, the present disclosure provides a method of monitoring a M. avium subsp. paratuberculosis infection in an animal or evaluating the efficacy of a M. avium subsp. paratuberculosis treatment in an animal, the method comprising detecting a level of at least one miRNA as described herein in a biological sample from the animal at a first time point and at least one subsequent time point. In some embodiments, the method comprises determining whether an animal has recovered from a A- / . avium subsp. paratuberculosis infection.

[0361] In an embodiment, the animal is administered with a vaccine comprising an M. avium subsp. paratuberculosis antigen. In an embodiment, the vaccine comprises an inactivated (killed) M. avium subsp. paratuberculosis, such as inactivated (killed) M. avium subsp. paratuberculosis strain 316F. EXAMPLES

[0362] Example 1 - Materials and Methods

[0363] Samples

[0364] Samples for JD biomarker discovery and validation were obtained from the following sources:

[0365] MAP-infected cattle

[0366] Samples were sourced both in Australia and New Zealand. In Australia, sampling of two commercial dairy herds in Southwest Victoria were organised by the National JD Reference Laboratory (NJDRL). Blood and faecal samples were collected by veterinary staff from the Victorian Government Department of Energy, Environment and Climate Action (DEECA). Samples from New Zealand were provided by a commercial JD testing provider (DRL Ltd.), with sampling performed by private veterinarians across multiple commercial dairy farms in the South Island.

[0367] MAP -uninfected cattle

[0368] Samples from MAP -uninfected cattle were collected from a single beef farm in Southwest Victoria. Farm visits were coordinated by NJDRL with sampling performed by DEECA veterinary staff. This farm has participated in JD testing programs without a single positive result for over 20 years.

[0369] Bovine tuberculosis

[0370] Sampling and testing of cattle infected with bovine tuberculosis (TB) was performed by AsureQuality Ltd, New Zealand.

[0371] Other infections

[0372] Samples collected for biomarker analytical specificity testing were collected by a private veterinary practice from multiple dairy properties in Southwest Victoria. These were categorised as “OTHER” infections. Diagnostic test reports (both culture and molecular) on matching sera and milk samples were provided by Gribbles Veterinary Pathology (Melbourne).

[0373] JD reference testing and other microbiological testing

[0374] For samples collected in Australia, all JD reference testing was performed by NJDRL. FC assays were performed as described (Whittington et al., 2000). Humoral immune responses were measured in serum using a commercial ELISA test (Paracheck™, Applied Biosystems) as described (Hope et al., 2000).

[0375] For samples collected in New Zealand, JD reference testing was performed by DRL Ltd. using ELISA and faecal PCR tests as described (Bates et al., 2020). Bovine tuberculosis testing (using both the intradermal tuberculin skin test and post-mortem examination of slaughtered animals, was performed by OSPRI (New Zealand).

[0376] RNA Isolation and RNA-seq

[0377] Total RNA was isolated from 200 pL of serum using the miRNA micro kit (Qiagen) as per the manufacturer’s instructions with a slight modification. Following lysis with Qiazol, glycogen (10 pg, Sigma / Merck) was added as a carrier to each sample. DNA libraries were generated from eluted RNA using the QIAseq miRNA Library Kit and QIAseq miRNA GNS 48 Index IL (QIAGEN), as per the manufacturer’s instructions. Libraries were analysed on the Agilent Bioanalyzer 2100 using the High Sensitivity DNA chip (Aligent). Libraries were sequenced on the NovaSeq 6000 (Illumina) using 100 bp single end chemistry by the Australian Genome Research Facility (AGRF). Small RNA sequencing resulting in 20-50 million raw reads per sample. Reads were trimmed to 18-26 nucleotides using CutAdapt (Martin, 2011). Quality was assessed using FastQC

[0378] (www.bioinformatics.babraham.ac.uk / pH)jects / fastqc / ). Filtering on length and quality resulted in a loss of 30-80% of raw reads, leaving 2-10 million reads per sample. MicroRNAs with at least 1 count per million in three or more samples were analysed further.

[0379] MicroRNA Expression Analysis

[0380] MiRDeep2 (Friedlander et al., 2012) was used to identify miRNA transcripts, with total counts including all reads that mapped to a locus (as opposed to reads matching the canonical / consensus sequence only) against the miRBase bovine miRNA database. Read normalisation was performed using DESeq2 (Love et al., 2014) in R.

[0381] Machine Learning

[0382] All machine learning analyses were conducted using the scikit-learn module in Python. Initially, normalized reads were examined for highly correlated miRNAs; pairs with a Pearson R value greater than 0.8 or less than -0.8 had one member removed to prevent multicollinearity. The remaining normalized miRNA counts were then scaled using either a standard z-score transformation or a robust scaler. Feature selection was performed using recursive feature elimination (RFE) to identify the most significant miRNAs for the classification model. Supervised learning algorithms, such as logistic regression and support vector machine, were employed for binary classification. Following the selection of the optimal number of features, hyperparameter tuning was conducted with GridSearchCV. To evaluate model performance, the dataset was split into 70% labeled training data and 30% unlabeled test data. Predicted classes of the test samples were compared to the true classes, and this process was repeated 1,000 times for reliability. The models were assessed based on accuracy, precision, recall, and the receiver operating characteristic area under the curve (ROC AUC) for the logistic regression model.

[0383] RT-qPCR

[0384] Complementary DNA (cDNA) was synthesised from 2 pL of input RNA using the TaqMan Advanced miRNA cDNA Synthesis Kit (Applied Biosystems), following the manufacturer’s instructions. RT-qPCR was performed with TaqMan miRNA Assays (F AM-labelled, one per miRNA target) in combination with TaqMan Fast Universal PCR Master Mix (no AmpErase UNG; Applied Biosystems) and 2 pL of cDNA. Standard cycling conditions were applied: 95 °C for 20 seconds, followed by 40 cycles of 95 °C for 1 second and 60 °C for 30 seconds. The fluorescence threshold for all assays was set at 0.1. Cycle threshold (CT) values (also known as quantification cycle (Cq)) were exported as .csv files from the qPCR instrument and uploaded into emiraldx.ai, a cloudbased platform developed by CSIRO to integrate with this molecular assay (Figure 1). The software automatically extracted CT values and converted them to a probability of infection score using a machine learning model. The machine learning model may comprise a logistic regression model, in some embodiments. bta-miR-126-5p was detected by RT-qPCR using the hsa-miR-126* assay from Thermo Fisher Scientific (Catalogue number 4427975 (000451)). bta-miR-122 was detected by RT-qPCR using the ccr-miR-122 assay from Thermo Fisher Scientific (Catalogue number 4440886 (470937_M AT)). bta-miR-125a was detected by RT-qPCR using the cfa-miR-125a assay from Thermo Fisher Scientific (Catalogue number 4440886 (007065_MAT)). bta-miR-339b was detected by RT-qPCR using an assay from Thermo Fisher Scientific (Catalogue number 4398987 (CT47WR3)). bta-miR-134 was detected by RT-qPCR using the dre-miR-194a assay from Thermo Fisher Scientific (Catalogue number 4440886 (005656_MAT)). JD Biomarker Analytical Specificity

[0385] Analytical specificity was evaluated by testing the JD biomarker RT-qPCR assays on RNA extracted from serum samples of cattle testing positive for other infections.

[0386] JD Biomarker Analytical Sensitivity

[0387] Synthetic RNA templates (Integrated DNA Technologies) corresponding to the target miRNAs were used to generate for RT-qPCR assay validation. Standard curves were established using serial dilutions of each synthetic RNA to determine CT ranges corresponding to relevant miRNA concentrations in bovine sera. To mimic positive and negative samples, diluted synthetic RNA oligonucleotides for the miRNAs were combined, with each adjusted to predefined CT ranges indicative of either a positive or negative result. A sample was classified as positive or negative only when targets fell within their respective Cr / concentration ranges.

[0388] JD Biomarker Limit of Detection

[0389] Limit of detection (LOD) was measured by using synthetic RNA oligonucleotides for the target miRNAs which were serially diluted 10-fold in nuclease-free water (Promega), and 5 pL of each dilution was used per RT-qPCR reaction. This generated a final copy number range from 1012to 104copies, with each dilution tested in triplicate.

[0390] JD Biomarker Repeatability

[0391] Inter- and intra-assay variation of the JD biomarker RT-qPCR assay was investigated by measuring how the assay operator and day influenced assay performance. Because this experiment required more serum samples than were available, synthetic RNA oligonucleotide templates were used. Four negative and four positive samples were tested blindly in 45 independent RT-qPCR reactions by three operators on three separate days.

[0392] JD Biomarker Diagnostic Sensitivity and Diagnostic Specificity

[0393] Diagnostic sensitivity (DSe) and specificity (DSp) were determined by Bayesian latent class modelling (BLCM). Sera were classified as positives and negatives based on the combined results of three imperfect reference tests (FC, faecal PCR and ELISA). For estimating DSp with 95% confidence and absolute precision of 2%, assuming DSe of the JD biomarker RT-qPCR assay could be >99% and DSp to be 98%, the minimum required sample size was estimated a priori to be 95 samples for estimating DSe and 188 samples for estimating DSp, using the R package ‘epiR’ (Stevenson et al., 2013). BLCM, as recommended by WOAH (Cheung et al., 2021) is particularly suited for situations where insufficient reference samples are available, as it does not rely on assumptions of the ‘true’ disease status of animals. A schematic representation of this modelling approach is shown in Figure 2.

[0394] JD Biomarker ROC Curve

[0395] Receiver Operating Characteristics curve (ROC curve) analysis was performed to identify threshold values for the JD biomarker RT-qPCR assay using MedCalc® (MedCalc, Statistical Software version 19.2.6, Ostend Belgium, 2020). The results were compared with the outcomes of the reference assays and the predictions of the ML discovery platform. A BLCM based on a publication of Branscum et al. (2005) was fitted with the assumption that the JD biomarker was conditionally dependent and the results of the reference assays were conditionally independent of the JD biomarker assays (Cheung et al., 2021). Neither test was perfect (Enoe et al., 2000) and the true status of the samples was unknown. The data consisted of the joint results from imperfect reference assays and the JD biomarker RT-qPCR assays obtained from cattle herds in Australia and New Zealand. The BLCM was constructed based on prior information regarding the joint DSe and DSp of the reference assays and the population prevalence of JD in endemic regions. The Beta (a,b) distributions for the priors were estimated using Betabuster 1.0 (https: / / betabuster.software.informer.com) in the ‘epiR’ library of the R statistical program (Stevenson et al., 2013) with assumptions that for the combined reference assays, the DSe was 95% sure to be >0.70 with mode = 0.75. The DSp estimate was 97.5% certain to be greater than 0.90, with a mode of 0.95 (Elsohaby et al., 2025). The assumption for prior herd prevalence was 95% sure to be <0.30 with mode = 0.20. Flat Beta (1,1) priors were assumed for the DSe and DSp of the JD biomarker RT-qPCR assay. Covariances were defined for DSe and DSp due to the conditional dependence of the miRNA biomarker assays.

[0396] Scripts were run in the R statistical program using OpenBUGS v3.2.3 (Fanslow and Schultz, 1991), with convergence estimates derived using 100,000 iterations of simulation with sampling done every 1,000thiteration until the Monte Carlo error value was <5% of the standard deviation of the node estimate using three assumed chains as initials or three generated initials, and discarding 500 iterations as burn-in. Convergence was assessed by evaluating the history, trace plots, and calculation of the Gelman-Rubin statistic diagnostic, which compares the within- and between-chain variability of the three initial values. Posterior medians with 95% probability intervals (PI) corresponding to the 2.5thand 97.5thpercentiles of the Markov Chain Monte Carlo sample were used to summarise parameter estimates of DSe, DSp and apparent prevalence.

[0397] JD Biomarker Reproducibility

[0398] JD biomarker RT-qPCR assay reproducibility testing was carried out in four laboratories. Lab 1 was the Australian Rickettsial Reference Laboratory in Geelong, Victoria. Lab 2 was the Australian Animal Health Laboratory in Geelong. Lab 3 was Deakin University in Geelong, and Lab 4 was the National Johne’s Disease Reference Laboratory in Bundoora, Victoria. Laboratories independently performed RT-qPCR analysis on 20 samples using protocols and reagents provided by CSIRO. Samples were blinded, randomised and transported to laboratories on dry ice. The run files from each laboratory were exported and analysed through emerald. ai to generate predicted classifications, with participants reporting individual and mean CT values alongside qualitative results. Cochran’s Q statistic was used to evaluate the proportion of positive and negative results between laboratories (Cochran et al., 1950). Cochran’s Q statistic is an extension of the McNemar test for related samples, providing a method for testing differences between three or more matched sets of frequencies or proportions. The positive and negative results for each sample are assigned values of 1 and 0, respectively. Indeterminate results are also assigned a value of 1.

[0399] Example 2 - Identification of Markers of a Mycobacterium avium subsp. paratuberculosis infection

[0400] The most abundant miRNAs identified in sera samples are shown in Figure 3, with the relative size of the area shown indicating the relative abundance. The most abundant miRNAs were bta-miR-16b, bta-miR-486 and bta-miR-23b-3p with over 50 million reads. DESeq2 was used to identify miRNAs with significant altered expression levels between Mycobacterium avium subsp. paratuberculosis (MAP) negative (n=81) and positive (n=50) samples via count-based differential expression (DE) testing. miRNAs that were up- or down-regulated were identified and highlighted in Figure 4.

[0401] Using a False Discovery Rate (FDR) adjusted p-value < 0.05, log2 fold change (FC) > 1 and baseMean > 231 (75th percentile), the dataset consisted of 19 miRNAs, of which 10 were up-regulated (m Mycobacterium avium subsp. paratuberculosis positive samples) and 9 were down-regulated. An additional 59 miRNAs were significantly DE in MAP positive samples with log2FC values < 1. A further 27 miRNAs were identified as useful for identifying MAP but are not significantly differentially expressed in the samples. A supervised machine learning method was implemented for the identification of the most predictive miRNAs and refined to identify the minimum targets necessary for accurate prediction and classification between MAP negative and positive samples. A Support Vector Machine model was implemented that randomly split the data into discovery and validation sets, for training and testing the model, respectively. This process was repeated 100 times to determine reproducibility. The most predictive miRNAs were selected using recursive feature elimination (Figure 5A). miRNA markers for Mycobacterium avium subsp. paratuberculosis infection and / or Johne’s disease identified using this approach weremiR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let- 7b, let-7c, let-7d, let-7f, let- 7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR- 11980, miR-11986b, miR-12030, miR-1246, miR-125b, miR-1306, miR-138, miR- 1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151- 5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR- 1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR- 195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR- 2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR- 425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR- 6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR- 99a-5p and miR-99b.

[0402] Measuring 3 miRNAs (bta-miR-497, bta-miR-23b-3p, and bta-miR-126-5p) identified MAP infection with 95% accuracy, 93% precision and recall, and a receiver operating characteristic area under the curve (ROC AUC) of 0.98 (Figure 5B and Figure 6).

[0403] Example 3 - Three Marker Panel for RT-qPCR Identification of a Mycobacterium avium subsp. paratuberculosis infection

[0404] Several differentially expressed (DE) miRNAs identified through RNA-seq were validated by RT-qPCR. Of miRNAs tested, five showed significant differences in CT values between uninfected and strict MAP-infected cohorts, in agreement with RNA-seq findings (data not shown). To further evaluate classification, the three-miRNA signature was transformed into two-dimensions via PCA, and visualised with the predictive model’s decision boundary, showing a clear separation between uninfected and strict MAP-infected groups (Figure 7A). The best-performing RT-qPCR model comprised a three-miRNA signature (miR-122 (miR-A), miR-125a (miR-B), and miR-194 (miR-C)) in a logistic regression model, achieving an accuracy of 0.98, recall of 0.882, Fl score of 0.938, precision of 0.981, and AUC of 0.970. This assay correctly classified all 85 uninfected animals (100%) and 15 of 17 MAP-infected animals (88%) (Figure 7B).

[0405] To visualise model performance, the three-miRNA RT-qPCR dataset was reduced to two dimensions by PCA and the model’s decision boundary was generated (Figure 7B). The resulting plot illustrates the classifier’s demarcation line, where the colour gradient (blue to red) reflects increasing predicted probability of being positive as MAP- infected. Circles represent control animals and diamond shapes represent MAP-infected animals, highlighting the clear separation achieved by the model.

[0406] Analytical Specificity

[0407] To determine the analytical specificity of the JD biomarker, the inventors investigated miRNA expression in sera from livestock infected with other mastitiscausing pathogens. Matching sera and milk samples were collected from 34 animals, with milk samples used to generate culture and molecular diagnostic test reports. Samples were categorised as disease other than JD (OTHER) and compared against uninfected and MAP-positive samples by RT-qPCR (Figure 8A-C). A resulting PCA plot (Figure 8D) shows that cattle testing positive for bovine TB (bTB) cluster separately from MAP -infected cattle and aligned more with uninfected cattle. OTHER infections clustered separately. These results demonstrate that MAP infection induces a miRNA profile that is not recapitulated by bovine TB or other bacterial infections tested to date.

[0408] JD Biomarker Assay Analytical Sensitivity and Limit of Detection

[0409] The performance of the RT-qPCR assays was benchmarked against serial dilutions of miRNA template to determine the LOD and to assess the operating range. To establish the LOD, synthetic RNA oligonucleotides for miR-122 (miR-A), miR-125a (miR-B), and miR-194 (miR-C) were serially diluted and amplified. Reliable amplification was observed in all replicates up to 1012copies, with strong linearity between template copy number and CT value across the dilution series (Figure 9A-C). Linear responses were maintained down to 104copies, confirming assay reliability within this dynamic range. Importantly, the target assay matrix sera showed CT values that fell within this validated linear range, supporting the practical applicability of the assays in cattle sera. To further assess JD biomarker assay performance in biological matrices, sera from a MAP-infected animal was serially diluted with sera from an uninfected animal. Dilution-dependent increases in CT values were observed for all three miRNAs, accompanied by corresponding decreases in the probability of infection scores (Figure 10A-C). These results demonstrate that while the assay is robust in individual serum samples, pooling or excessive dilution reduces sensitivity and limits applicability for pooled herd-level testing.

[0410] JD Biomarker RT-qPCR Repeatability

[0411] Repeatability was evaluated across 45 independent RT-qPCR runs performed by three operators on different days using synthetic RNA oligonucleotides. All samples were correctly classified as MAP -infected (MAP+) or uninfected (MAP-). Across runs, intra- and inter-assay variation remained within ±2 standard deviations of the mean CT values (Figure 11A-D and Figure 12).

[0412] ROC Curve and Diagnostic Accuracy Analysis

[0413] To assess diagnostic performance, ROC curve analyses were performed for three- , four-, and five-miRNA assay combinations (Figure 13, Table 1). The AUC values were high across all assay designs, with the three-miRNA assay achieving an AUC of 0.970 at a threshold of 0.27 (miR-122, miR-125a, and miR-194), the four-miRNA assay an AUC of 0.965 at a threshold of 0.50 (miR-122, miR-125a, miR-194, and miR-126-5p), and the five-miRNA assay (miR-122, miR-125a, miR-194, miR-126-5p and miR-339b) an AUC of 0.961 at a threshold of 0.65. Pairwise comparisons of the ROC curves revealed no statistically significant differences between the assays (p>0.05). BLCM confirmed that the three-miRNA assay achieved the highest DSe while maintaining DSp comparable to assays with four or five miRNAs. The DSe and DSp for the three-miRNA assay exceeded 0.90 and 0.95, respectively, outperforming existing reference tests. ML- based classification results aligned with the BLCM findings, further confirming the utility of the three-miRNA design as the most reliable biomarker panel. Other statistical parameters related to DSe, DPE and ROC curve analysis are outlined in Tables 2 to 4. Table 1. Four-way cross-classification of results using ROC curve-defined thresholds.

[0414] Table 2. Diagnostic sensitivity and specificity of an RT-qPCR miRNA biomarker assay and JD reference tests developed using BLCM. The diagnostic thresholds were derived using a ROC curve and used to cross-classify the results with the reference test.

[0415] Table 3. Four-way cross-classification of results from AI / ML predictions. Table 4. Diagnostic sensitivity and specificity of an RT-qPCR miRNA biomarker assay and JD reference tests developed using BLCM. The diagnostic thresholds were derived using ML predictions and used to cross-classify the results with the reference test. JD Biomarker Assay Reproducibility

[0416] JD biomarker assay reproducibility was evaluated for 20 samples by four separate laboratories running different qPCR platforms using same PCR reaction and running condition (Tables 5 and 6). Data were visualised as dot plots and box plots to compare Crvalues for miR-122 (miR-A), miR-125a (miR-B), and miR-194 (miR-C) (Figure 14A) and corresponding probability of infection scores for MAP-infected and uninfected samples (Figure 14B). Cochran’s Q test was applied to assess inter-laboratory agreement, with the null hypothesis being that no differences exist between laboratories. The test returned a P value of 0.07, indicating no statistically significant differences. Minor discrepancies were observed, including Lab 1 classifying two positive samples as negative, Lab 3 classifying two negative samples as positive, and Lab 4 misclassifying one positive sample as negative. These disagreements occurred with different samples, suggesting that variability was laboratory-centric rather than assay-centric.

[0417] Table 5. Composition of the reaction mix used in the RT-qPCR assay. Table 6. Details of RT-qPCR reagents and volumes per tube.

[0418] The present application claims priority from AU 2024903098 filed 25 September 2024, the entire contents of which are incorporated herein by reference.

[0419] It will be appreciated by persons skilled in the art that numerous variations and / or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

[0420] All publications discussed and / or referenced herein are incorporated herein in their entirety.

[0421] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.

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Claims

CLAIMS1. A method of determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal, the method comprising, in a biological sample from the animal, detecting a level of at least one miRNA selected from: miR-122, miR- 125a, miR-194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let-7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR- 11971, miR-11980, miR-11986b, miR-12030, miR-1246, miR-125b, miR-1306, miR- 138, miR-1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151-5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR-1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR-195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR-2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR- 2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b- 5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR- 425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR- 6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR- 99a-5p and miR-99b.

2. The method of claim 1 which comprises detecting the levels of miR-122, miR- 125a and miR-194.

3. The method of claim 1 or claim 2 which determines the likelihood the animal has, or will develop, Johne’s disease.

4. The method of any one of claims 1 to 3, wherein the method comprises comparing the level of the at least one miRNA to a reference value.

5. The method of claim 4, wherein the reference value is a predetermined level of the at least one miRNA, a predetermined score, the level of the at least one miRNA in a control sample, or the level of the at least one miRNA in an animal who does not have a Mycobacterium avium subsp. paratuberculosis infection.

6. The method of any one of claims 1 to 5, wherein the method comprises assigning a score for the sample based on the level of the at least one miRNA.

7. The method of claim 6. wherein the score is assigned using an miRNA analysis algorithm.

8. The method of any one of claims 1 to 7 which further comprises normalizing the level of the at least one miRNA to obtain a normalized level of the at least one miRNA, and wherein the method comprises comparing the normalised level of the at least one miRNA to the reference value of the at least one miRNA.

9. The method of any one of claims 1 to 8 which has an accuracy of least 75%, or at least 80%, or at least 85%, or at least 90%, or at least 95%, at least 97%, or at least 99%.

10. The method of any one of claims 1 to 9, wherein the method can identify a Mycobacterium avium subsp. paratuberculosis infection with at least 90% precision, at least 93% precision, at least 95% precision, or at least 98% precision.

11. The method of any one of claims 1 to 10, wherein the method can identify a Mycobacterium avium subsp. paratuberculosis infection with at least 90% precision, at least 93% precision, at least 95% precision, or at least 98% precision compared to a non- Mycobacterium avium Johne’s disease causing pathogen.

12. The method of any one of claims 1 to 11, wherein the area under the curve (AUC) of the at least one miRNA is one or more of at least 0.65, at least 0.7, at least 0.75, at least 0.80, at least 0.85, at least 0.90, at least 0.95 or at least 0.98.

13. The method of any one of claims 1 to 12, wherein, when the method comprises detecting the level of at least three miRNAs, the method has one or more of i) an accuracy of at least about 95%, ii) a precision of at least about 90% and iii) an AUC of at least 0.98.

14. The method of any one of claims 1 to 13, wherein the animal is a livestock animal.

15. The method of any one of claims 1 to 14, wherein the animal is an ungulate.

16. The method of claim 15, wherein the ungulate is cattle, sheep, goat, deer, camel, horse, llama, alpaca, bison or buffalo.

17. The method of claim 16, wherein the ungulate is cattle.

18. The method of claim 17, wherein the cattle is a Bos taurus. Bos indicus or a cross thereof.

19. The method of any one of claims I to 18, wherein the biological sample is selected from: blood or a blood fraction, plasma, serum, urine, whole blood, milk or a fraction thereof, whey and bulk milk.

20. The method of claim 19, wherein the biological sample is serum.

21. The method of any one of claims 1 to 20, wherein the method comprises detecting not more than 50 miRNA, or not more than 40 miRNA, or not more than 30 miRNA, or not more than 20 miRNA, or not more than 10 miRNA, or not more than 6 miRNA, or not more than 5 miRNA, or not more than 3 miRNA.

22. The method of any one of claims 1 to 21, wherein the at least one miRNA is detected by next generation sequencing, quantitative real-time reverse transcription-PCR (qRT-PCR), isothermal amplification, electrical interference, CRISPR-based method, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), stranddisplacement amplification (SDA), enzyme-free amplification, microarray, multiplex miRNA profiling assay, RNA-ish, or northern blotting.

23. The method of claim 22, wherein the next generation sequencing is RNA-seq, small RNA-seq, miRNA-seq or targeted next generation sequencing.

24. The method of claim 23, wherein the at least one miRNA is detected by qRT- PCR, electrical interference or a CRISPR-based method.

25. The method of any one of claims 1 to 24, wherein the Mycobacterium avium subsp. paratuberculosis infection is a sub-clinical Mycobacterium avium subsp. paratuberculosis infection.

26. A panel or kit for determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal, the panel or kit comprising one or more probes or primers for detecting at least one miRNA selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let- 7b, let-7c, let-7d, let- 7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR-12030, miR-1246, miR-125b, miR-1306, miR-138, miR- 1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151- 5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR- 1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR-195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR- 2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR- 425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR- 6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR- 99a-5p and miR-99b.

27. The panel or kit of claim 26, further comprising a control.

28. The panel or kit of claim 26 or claim 27, further comprising a reference value.

29. The panel or kit of any one of claims 26 to 28 which comprises a nucleotide array.

30. A method of managing a group of animals, preventing spread of Mycobacterium avium subsp. paratuberculosis infections in a group of animals, or eliminating Mycobacterium avium subsp. paratuberculosis infections from a group of animals, the method comprising determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using the method of any one of claims 1 to 25 or the panel or kit of any one of claims 26 to 29, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

31. A method of treating or preventing a Mycobacterium avium subsp. paratuberculosis infection in an animal, the method comprisingi) determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in an animal using the method of any one of claims 1 to 25 or the panel or kit of any one of claims 26 to 29, ii) administering a treatment for a. Mycobacterium avium subsp. paratuberculosis infection if it is determined the animal is likely to have a Mycobacterium avium subsp. paratuberculosis infection.

32. Use of an aAi-Mycobacterium avium subsp. paratuberculosis compound for the manufacture of a medicament for the treatment or prevention of a Mycobacterium avium subsp. paratuberculosis infection in an animal, wherein it has been determined that it is likely the animal has a Mycobacterium avium subsp. paratuberculosis infection using the method of any one of claims 1 to 25 or the panel or kit of any one of claims 26 to 29.

33. Use of an aAi-Mycobacterium avium subsp. paratuberculosis compound for the treatment or prevention of a Mycobacterium avium subsp. paratuberculosis infection in an animal, wherein it has been determined that it is likely the animal has a Mycobacterium avium subsp. paratuberculosis infection using the method of any one of claims 1 to 25 or the panel or kit of any one of claims 26 to 29.

34. A computer program product encoded on a computer-readable storage medium, wherein the computer program product comprises instructions for: a) detecting the presence, absence or quantity of at least one miRNA in a sample of an animal; and b) correlating the presence, absence, or quantity of the at least one miRNA in the sample to a likelihood that the animal being infected with Mycobacterium avium subsp. paratuberculosis, wherein the at least one miRNA is selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let-7f, let- 7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR- 11980, miR-11986b, miR-12030, miR-1246, miR-125b, miR-1306, miR-138, miR- 1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151- 5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR- 1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR- 195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR- 2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p,miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR- 425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR- 6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR- 99a-5p and miR-99b.

35. A system comprising: a) the computer program product of claim 34; and b) a processor operable to execute programs; and / or a memory associated with the processor.

36. A system for detecting the presence or quantity of a Mycobacterium avium subsp. paratuberculosis in a sample of an animal comprising: a processor operable to execute programs; a memory associated with the processor; a database associated with said processor and said memory; and a program stored in the memory and executable by the processor, the program being operable for: a) detecting the presence, absence or quantity of at least one miRNA in a sample of an animal, wherein the at least one miRNA is selected from: miR-122, miR-125a, miR-194, miR-497, miR-23b-3p, miR-126-5p, miR-145, let-7a-5p, let-7b, let-7c, let-7d, let-7f, let-7g, let-7i, miR-100, miR-10225a, miR-106b, miR-lOa, miR-lOb, miR-11971, miR-11980, miR-11986b, miR-12030, miR-1246, miR-125b, miR-1306, miR-138, miR- 1388-5p, miR-139, miR-142-5p, miR-1468, miR-146a, miR-146b, miR-150, miR-151- 5p, miR-15a, miR-15b, miR-16a, miR-16b, miR-17-5p, miR-181a, miR-181b, miR- 1839, miR-185, miR-186, miR-18a, miR-191, miR-191b, miR-192, miR-193a-5p, miR- 195, miR-204, miR-205, miR-20a, miR-215, miR-22-5p, miR-224, miR-2284w, miR- 2284x, miR-2284z, miR-2285bc, miR-2285bf, miR-2285bn, miR-2285ce, miR-2285cj, miR-2419-5p, miR-26a, miR-26b, miR-28, miR-296-5p, miR-30a-5p, miR-30b-5p, miR-30c, miR-30d, miR-30e-5p, miR-31, miR-32, miR-324, miR-331-5p, miR-335, miR-339a, miR-339b, miR-361, miR-362-5p, miR-374b, miR-378, miR-423-5p, miR- 425-5p, miR-4286, miR-451, miR-484, miR-486, miR-499, miR-532, miR-574, miR- 6119-5p, miR-6123, miR-6529a, miR-660, miR-744, miR-877, miR-93, miR-98, miR- 99a-5p and miR-99b; andb) correlating the presence, absence, or quantity of the at least one miRNA in the sample to a likelihood that the animal being infected with Mycobacterium avium subsp. paratuberculosis .

37. A method of managing a group of animals, preventing spread of Mycobacterium avium subsp. paratuberculosis infections in a group of animals, or eliminating Mycobacterium avium subsp. paratuberculosis infections from a group of animals, the method comprising determining the likelihood of a Mycobacterium avium subsp. paratuberculosis infection in individual animals of the group using the computer program product, or system according to any one of claims 34 to 36, and removing individuals from the group determined to likely have a Mycobacterium avium subsp. paratuberculosis infection.

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