Assays, kits and methods for determining antimicrobial susceptibility in mannheimia haemolytic

Abstract

Rapid diagnostic assays and related kits and methods to determine antimicrobial susceptibility are described in which genetic determinants of resistance that are predictive of phenotypic resistance are targeted in Mannheimia haemolytica.

Description

ASSAYS, KITS AND METHODS FOR DETERMINING ANTIMICROBIAL SUSCEPTIBILITY IN MANNHEIMIA HAEMOLYTICACROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U. S. provisional patent application no.63 / 764,240, which was filed January 27, 2025, which is incorporated herein by reference in its entirety.STATEMENT OF GOVERNMENT SUPPORT

[0002] This invention was made with government support under 2020-68014-31302 awarded by the US Department of Agriculture and under ICASATWG-0000000022 awarded by the Foundation for Food and Agriculture Research. The government has certain rights in the invention.TECHNICAL FIELD

[0003] The present disclosure relates to the development of genetic field and lab tests to determine susceptibility of Mannheimia haemolytica to antimicrobial treatment.SEQUENCE LISTING

[0004] The instant application contains a Sequence Listing which has been submitted herewith and is hereby incorporated by reference in its entirety. Said.xml copy, created on January 23, 2026, is named PRF_70998-02 PCT, and is 70,018 bytes in size.BACKGROUND

[0005] Mannheimia haemolytica (Mh) is one of the most common pathogens implicated in bovine respiratory disease (BRD). This bacterium is Gram-negative, non-motile, non-spore forming, facultative anaerobic bacilli sometimes found in coccobacilli formations. Mh is a bacterial species commonly found in ruminant animals and was originally classified as Pasteurella haemolytica due to its relation to that genus, as well as the complete lysis of heme when grown on blood agar. Mh serotype Al, A2 and A6 are the most predominant in cattle with the “A” describing the strain biotype, and the subsequent number signifying the capsular serotype. All three serotypes can be found naturally within the respiratory tract. Serotype A2 hasbeen identified as a commensal serotype. Its abundance decreases in response to various stressors to the animal, while Al and A6 are found in low levels in healthy animals and they increase in abundance with stress and respiratory disease. As an opportunistic pathogen of the BRD complex, Mh Al infections often occur after viral infections or environmental stress to the animal. During these scenarios, the bacteria will migrate to the lower respiratory tract, infecting and damaging mucosal epithelial cells.

[0006] Given that BRD is an extremely costly disease for both the animal and producer, numerous management practices have been implemented to improve animal outcomes by reducing risk factors. These practices include training calves on feed and water during weaning; weaning at least 45 days prior to transport from cow-calf operation to a feedlot; castrating and deworming calves prior to transport from cow-calf operation to feedlot; and administering vaccinations prior to and following transport. Each of these practices focuses on a different type of risk factor: predisposing (e.g., transport stress and animal age), environmental (e.g., stocking density), or epidemiological (i.e., exposure to pathogenic microorganisms). By reducing risk factors, animals ultimately face reduced risk of morbidity and mortality from BRD.

[0007] The treatment of BRD is highly error-prone due to the necessary use of empiric antibiotic treatment - treatment administered prior to determining the etiology, source of infection, or resistance profile of the disease-causing pathogen. Empiric treatments allow for an immediate attempt at treating the animal rather than waiting days for laboratory confirmed information. However, empiric treatment can result in failed treatments (antibiotic re-treatment or animal fatality) in 10-20% of cattle, with increased risk of mortality for each consecutive treatment.

[0008] Failed treatments may be the result of resistant bacteria. A study on the relationship between serotype (Mh Al, A2, or A6) and phenotypic resistance to antibiotics used in BRD treatment, such as macrolides and tetracyclines, observed phenotypic resistance in 56% of isolates classified as pathogenic serotype Al, with 38% of all tested Al isolates having resistance to two or more drugs. A different study on phenotypic antibiotic resistance of Mh isolates noted a stark increase in the number of pan-resistant isolates (i.e., isolates with phenotypic resistance to five or more antibiotics) during the 2009-2011 time period. Acquisition of antibiotic resistance can be accelerated by horizontal transfer of antibiotic resistance genes. Mobile genetic elements (MGE) such as transposons and integrative-conjugative elements (ICE)allow the horizontal transfer of genetic materials between bacteria and have been identified in Mh isolates from cattle with BRD. Several ICEs have been found to contain multidrug resistance islands, conferring resistance to beta-lactam, aminoglycoside, tetracycline, sulfonamide, and macrolide-lincosamide-streptogramin (MLS) antibiotics.

[0009] Antibiotic resistance not only compromises the effectiveness of treatment but also poses a significant public health risk, as resistant pathogens may be transmitted from animals to humans. Therefore, monitoring and managing antibiotic resistance in BRD is significant for both animal health and human safety. The ability to detect and monitor antibiotic resistance in BRD pathogens directly at the point of care is a game-changer for the livestock industry. Early and accurate detection methods are critical for implementing appropriate treatment strategies, which can help to reduce the overuse of antibiotics and slow the spread of resistance. By identifying resistant infections quickly, veterinarians and farmers can make informed decisions about alternative treatments or management practices, potentially saving the lives of affected animals, improving overall herd health, and improving economic outcomes.

[0010] There is a need to development of a rapid diagnostic test to determine antimicrobial susceptibility. While much information on the phenotypic or genotypic antibiotic resistance of Mh isolates from BRD cases has been examined, there is a lack of comparison of the two types of data. This is important because the presence of antibiotic resistance genes does not guarantee phenotypic resistance. Previous analysis on phenotype to genotype comparisons in BRD pathogens have focused on analysis of Pasteurella multocida, Histophilus somni, and Mh where genotype-phenotype concordance rates ranged from 17-100%. For a rapid antibiotic susceptibility test to be reliable, the concordance rates would need to be improved considerably.SUMMARY

[0011] Presently described are rapid diagnostic assays and related kits and methods to determine antimicrobial susceptibility targeting genetic determinants of resistance that are predictive of phenotypic resistance Mannheimia haemolytica.

[0012] To that end, in one aspect, real-time molecular diagnostic assays are provided to detect genetic determinants of phenotypic resistance in order to determine susceptibility to antimicrobial treatment. By way of example, loop-mediated isothermal amplification (LAMP) assays are provided. In at least one embodiment, such assays can comprise one or more LAMPprimer sets that targets deoxyribonucleic acid (DNA) fragments of a suite of genetic determinants of phenotypic resistance to one or more antimicrobial therapeutics in a sample (e.g., a bovine nasal sample or a bovine water sample taken from a trough from which the cattle drink, for example), wherein the assay allows for single-step identification of one or more of the genetic determinants in a pathogen associated with phenotypic resistance to one or more antimicrobial therapeutics. The pathogen associated with BRD can be, for example, a bacterium (e.g., bacterium or a bacterial mycoplasma), e.g., Mannheimia haemolytica (Mh). In another such example, multiplex qPCR assays are provided.

[0013] In certain embodiments, the DNA fragments are of Mh genes of mutant-type (MT) gyrA of SEQ. ID. NO. 1, wild-type (WT) gyrA of SEQ ID. NO. 2, MT parC of SEQ ID. NO. 3, WT parC of SEQ ID. NO. 4, estT of SEQ ID. NO. 5, mphE of SEQ ID. NO. 6, msrE of SEQ ID. NO. 7, erm(42) of SEQ ID. NO. 8, erm(47) of SEQ ID. NO. 9, floR of SEQ ID. NO. 10, tet(H) of SEQ ID. NO. 11, tet(A) of SEQ ID. NO. 12, and tet(T) of SEQ ID. NO. 13. In LAMP examples, the at least one LAMP primer set is one or more of primer sets of one or more fragments of each of SEQ. ID. NOS. 1-13. Each assay can also include various primer sets drawn to any combination of the foregoing. In certain embodiments, each LAMP primer set is at least about 98% specific to the targeted DNA fragment. By way of example, Primer sets may span <200 bp of a target gene sequence. The LAMP primer set can also comprise loop primers, e.g., loop forward and / or loop backward. Embodiments of the assay can process and provide a visual result in 60 minutes or less. For example, the visual result can be indicative of the presence or absence of genetic determinants of antibiotic resistance in the sample.

[0014] Embodiments of such assays can process results to indicate susceptibility to an antibiotic treatment based in indications of the presence or absence of genetic determinants of antibiotic resistance. By way of example, such assays may process results to indicate susceptibility to multiple classes of antibiotics, including, e.g., quinolones (e.g., fluoroquinolone), macrolides (e.g., tilmicosin, tulathromycin) phenicols (e.g., florfenicol, thiamfenicol), and tetracycline (e.g., oxytetracycline, chlortetracycline).

[0015] The visual result can, for example, identify the type of pathogen present in the sample. In certain embodiments, the visual result is a color-coded or colorimetric result.

[0016] In certain embodiments, the assay can be used in a method comprises identifying an antibiotic to which Mh is susceptible. Susceptibility / resistance may be based on the detection of the presence and / or absence of one or more genetic determinants, wherein(a) the detected absence of both MT gyrA (SEQ. ID. NO. 1) (and, optionally, the presence of WT gyrA (SEQ. ID. NO. 2) as a control) and MT parC (and, optionally, the presence of WT parC (SEQ. ID. NO. 4) is indicative of susceptibility to treatment with a quinolone, e.g., fluoroquinolone;(b) the detected absence of each of estT (SEQ ID. NO. 5), mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), erm(47) (SEQ. ID. NO. 9) is indicative of susceptibility to treatment with an antibiotic on the macrolide class;(c) the detected presence estT (SEQ ID. NO. 5) and one of mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), or erm(47) (SEQ. ID. NO. 9) is indicative of resistance to treatment with an antibiotic on the macrolide class;(d) the detected presence estT (SEQ ID. NO. 5) and one the absence of each mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), and erm(47) (SEQ. ID. NO. 9) is indicative of susceptibility to treatment with the tulathromycin, tilmicosin, and / or tildipirosin; (e) the detected presence of flor (SEQ. ID. NO. 10) is indicative of resistance to treatment with phenicol;(f) the detected absence of flor (SEQ. ID. NO. 10) is indicative of susceptibility to treatment with phenicol;(g) the detected presence of one of tet(H) (SEQ ID. NO. 11), tet(A) (SEQ ID. NO. 12), and tet(T) (SEQ ID. NO. 13) is indicative of resistance to treatment with tetracycline (e.g., oxytetracycline, chlortetracycline); and(h) the detected absence of each of tet(H) (SEQ ID. NO. 11), tet(A) (SEQ ID. NO. 12), and tet(T) (SEQ ID. NO. 13) is indicative of susceptibility to treatment with tetracycline (e.g., oxytetracycline, chlortetracycline);

[0017] In certain embodiments, the assay comprises one or more indicators. Such indicators, for example, can comprise a pH- or magnesium-sensitive indicator. In some embodiments, the indicator comprises a magnesium-based indicator. In some embodiments, the indicator is a fluorescent indicator.

[0018] In certain embodiments, each LAMP primer set is associated with a colorimetric reagent. The colorimetric reagent can be pH sensitive or magnesium sensitive, for example. In certain embodiments, the colorimetric reagent is phenol red.

[0019] Methods are also provided for identifying deoxyribonucleic acid (DNA) fragments of a suite of genetic determinants of resistance to one or more antimicrobial therapeutics in a sample. In at least one embodiment, the method comprises: providing a LAMP primer panel that targets DNA fragments of a suite of respective genetic determinants of resistance to one or more antimicrobial therapeutics in a sample; obtaining a sample from a subject; combining the sample and the LAMP primer panel into a mixture; heating the combination to initiate amplification of the targeted DNA fragment; and detecting a visual result in the heated combination indicative of the presence or absence genetic determinants of resistance. There, the at least one LAMP primer set can be, for example, one or more of primer sets of one or more fragments of each of SEQ. ID. NOS. 1-13.

[0020] In some cases, the methods can provide a visual result in 60 minutes or less of initiating the heating step and the sample is a bovine nasal sample (or a bovine water sample).

[0021] The step of detecting a visual result can also further comprise measuring a relative clarity of the heated combination using a turbidimeter; and analyzing colorimetric data in the visual result using one or more of a fluorescent reader, an ultraviolet light reader, or camera. The visual result can be indicative of the presence or absence of the targeted DNA determinant. In certain embodiments, if the visual result is indicative of the presence of a targeted DNA determinant, the method further comprises treating the subject (or cohort (e.g., herd)) for the targeted pathogen.

[0022] In certain embodiments of the method, the at least one LAMP primer set is coupled with a colorimetric reagent that is pH sensitive or magnesium sensitive. For example, the colorimetric agent can be phenol red.

[0023] Kits are also provided for performing methods identifying DNA fragments of a suite of genetic determinants of resistance to one or more antimicrobial therapeutics in a sample. In at least one embodiment, the kit can comprise a fluorescent indicator, and a fluorescent reader, an ultraviolet light reader, or a camera to provide color metric result data indicative of the presence or absence of a targeted pathogen in the sample. In at least one embodiment, the at least one primer set is coupled with a colorimetric reagent that is pH sensitive or magnesium sensitive.In certain embodiments, the colorimetric agent is phenol red. In examples, the kit provides a solid support medium on which LAMP assay can be performed. In the alternative, the kit can provide a liquid medium in which LAMP assay can be performed.

[0024] In certain embodiments, the at least one swab comprises a nasal swab and the kit further comprises a sealable container with a transport media therein. In certain embodiments, the heating element is a water bath.

[0025] The kits hereof can be portable and capable of use in a non-laboratory setting (e.g., the field).BRIEF DESCRIPTION OF THE FIGURES

[0026] FIG. 1 is a flow diagram of an example LAMP method disclosed herein.

[0027] FIGS. 2A-D present example panel results of genetic determinant detection and antibiotic susceptibility in a sample.SEQUENCE LISTINGS SEQUENCE ID. NO. 1: Mutant-type (MT) gyrA (fluoroquinolone resistant (MH001)) (bold / underline denotes SNIP)

[0028] TTATTCTTCAGTTGATGTTTCTGCTGTTTCGGCATTTTCTAATGCGTCTA GGCTTTCATTTTCATCGTCTTCAGGCTCGCAGACTCGTTCAAGGCTAACAACTTGTTC GTCTTCTGCGGTGCGAATAATACGTACGCCTTGGGTGTTTCGTCCAACGAGGCTGAC TTCTGCCACTCGGGTGCGAACCAGCGTGCCGGCATCGGTAATCAACATAATTTGGTC GGTTTCCTCTACTTGAACTGCCGCAACCACTTTGCCGTTTCGTTCATTCACTTTAATC GAAACGACCCCTTTGGTTGCTCGGGATTTCACCGGATATTCGCTAATCACCGTACGT TTACCGTAGCCGTTTTGGGTGACGGTTAAAATATCGCCTTCGGTGCGAGGAATCACT AAAGAAACCACACGGTCTTTATCAAGGCTGACGCTGGAGGAATCTTCTGCCTCTTCA TTGTCAGTTTCAATAATTTCAGCTTCTTCAATTTCTTCGCTTGAAATTTCGGTGGTAG AAAGTTTAATACCTCGTACGCCGGTGGCGGTACGTCCCATCGCACGAACTGCTGTTT CGGCAAAGCGAACTACTCGCCCTTGTGCCGAGAACAACATAATTTCGCTGTTACCAT CAGTAATATCTACCCCGATCAGCTCATCGCCTTCACGAAGTTTTAACGCAATCAGAC CGCTTGCACGCACGTTGCTAAAGGCATCAAGGGAAACTTTTTTCACTACACCGCTAG CTGTTGCCATAAAGATAAACTTATCGGCACTAAATTCACCGTTTGGAATCGGTAGAATCGCGGTAATACGTTCGTTTTCTTCTTTCACTAACGGCAGAATATTCACAATCGGCGT GCCTCTTGCCCCACGGCTTGCTTGCGGTAATTGATACACTTTTAATTGATATAAACGC CCACGGCTTGAGAAGCAAAGAATGGTGTCGTGGGTGTTTGCCACCAAGAGTTTTTCG ATGAAATCATCTTCTTTCATCTTGGTTGCCGACTTACCTTTACCGCCACGGCGTTGTG CTTCGTAGTCGGATAACGGCTGATATTTCACATAGCCGGCGTGAGAAAGCGTAACCA CCACATCTTCTTGTGCGATTAAGTCTTCAATGTTGATGTCGCCAGAGGCTGCGGTAA TTTCAGTACGGCGTTCATCATTAAAATCGGTTTTTACACGTTCTAACTCTTCACGGAT CACTTCCATTAAGCGTTCTGGGCTGGTTAAAATGTGGATTAATTCGCCAATTTCAGTT AAGAGTTCTTTGTATTCTTCAACGATTTTCTCGTGTTCTAAACCGGTTAAGCGGTGTA AGCGTAGCTCTAAAATCGCTCTGGCTTGAGCTTCAGATAAATAGTATTCGCCATCAC GCACTCCTAAGTGTTCAGGTAAATCTTCCGGTCGGGAAGCACCAACACCAGCAGCCT CTAACATTGGTGCAACATTGCCTAATGCCCAAGGGCGAGCCAATAAGCCTTCACGG GCTTCTTCTGCGGTTTTTGAGGCTCGAATTAATTCAATTACCGGGTCGATATTCGCTA ACGCAATCGCCAAACCTTCTAAAATATGGGCACGTTCACGGGCTTTACGCAATTCAA AAATCGTACGGCGTGTGACCACTTCACGACGGTGTTTAACAAAGGCTTCGATGATCT GGCGTAAGTTCAGCACTTTTGGCTGCCCATTATCTAACGCCACCATATTGATACCAA ACGTGACCTGCATTTGGGTTAAAGAATAAAGGTGGTTTAACACCACTTCACCCACTG CATCACGCTTGACTTCAATAACAATCGAAATCCCTTTTTTATCGGAATAGTCATCAAT TTTGCTGATGCCTTCGATTTTCTTCTCTTTAACCAGCTCGGCAATTTTTTCAATTAATT TTGCCTTATTCACTTGGTATGGCAGTTCGGTTACAACAATTTGCTCACGCCCTTTATC AGTTGTTTCCACGCTGGCTTTCGCGCGCACATACACCTTGCCACGACCGGTGCGGTA AGCCTCTTCAATCCCTTTACGACCGTTGATAATCGCCCCGGTCGGGAAATCCGGCCC CGGGATATAACGCATTAGCTCTTCAACACTAATCTGTTCATTTTCAATATACGCCAA GCAACCATCTAATACTTCGCCTAAATTATGAGGTGGAATATTGGTTGCCATACCGAC CGCAATCCCTGAAGAACCATTGACTAACAGAGCCGGAATTTTGGTTGGCAATACATC AGGAATCATCTCTTTGCCGTCATAGTTCGGTGAGAAATCCACCGTTTCTTTATCGAG ATCGGTTAATAATTCCTGCGTAATTTTCTGCATACGCACTTCGGTATAACGCATTGCT GCCGGTGCATCGCCATCAATAGAACCAAAGTTACCTTGCCCGTCCACCAACATATAA CGTAGTGAGAACGGTTGAGCCATACGCACAATAGTGTTATACACCGCAAAGTCACC GTGTGGGTGATATTTACCGATCACATCACCGACCACACGGGCAGATTTAACGTGCGG CTTATTATAGGTGTTGCTGTTTTGATCCATTGAAAAAAGCACTCGGCGGTGTACCGGCTTTAAACCGTCTCGTACATCAGGCAAGGCACGCCCAACAATCACCGACATCGCATA GTCAAGGTAAGAGGTTTTTAATTCGTCCTCAATACTTACGGGAATAATATCTTTGGC TAATTCGCTCAT SEQUENCE ID. NO. 2: Wild-type (WT) gyrA (fluoroquinolone susceptible (MH002))

[0029] TTATTCTTCAGTTGATGTTTCTGCTGTTTCGGCATTTTCTAATGCGTCTA GGCTTTCATTTTCATCGTCTTCAGGCTCGCAGACTCGTTCAAGGCTAACAACTTGTTC GTCTTCTGCGGTGCGAATAATACGTACGCCTTGGGTGTTTCGTCCAACGAGGCTGAC TTCTGCCACTCGGGTGCGAACCAGCGTGCCGGCATCGGTAATCAACATAATTTGGTC GGTTTCCTCTACTTGAACTGCCGCAACCACTTTGCCGTTTCGTTCATTCACTTTAATC GAAACGACCCCTTTGGTTGCTCGGGATTTCACCGGATATTCGCTAATCACCGTACGT TTACCGTAGCCGTTTTGGGTGACGGTTAAAATATCGCCTTCGGTGCGAGGAATCACT AAAGAAACCACACGGTCTTTATCAAGGCTGACGCTGGAGGAATCTTCTGCCTCTTCA TTGTCAGTTTCAATAATTTCAGCTTCTTCAATTTCTTCGCTTGAAATTTCGGTGGTAG AAAGTTTAATACCTCGTACGCCGGTGGCGGTACGTCCCATCGCACGAACTGCTGTTT CGGCAAAGCGAACTACTCGCCCTTGTGCCGAGAACAACATAATTTCGCTGTTACCAT CAGTAATATCTACCCCGATCAGCTCATCGCCTTCACGAAGTTTTAACGCAATCAGAC CGCTTGCACGCACGTTGCTAAAGGCATCAAGGGAAACTTTTTTCACTACACCGCTAG CTGTTGCCATAAAGATAAACTTATCGGCACTAAATTCACCGTTTGGAATCGGTAGAA TCGCGGTAATACGTTCGTTTTCTTCTTTCACTAACGGCAGAATATTCACAATCGGCGT GCCTCTTGCCCCACGGCTTGCTTGCGGTAATTGATACACTTTTAATTGATATAAACGC CCACGGCTTGAGAAGCAAAGAATGGTGTCGTGGGTGTTTGCCACCAAGAGTTTTTCG ATGAAATCATCTTCTTTCATCTTGGTTGCCGACTTACCTTTACCGCCACGGCGTTGTG CTTCGTAGTCGGATAACGGCTGATATTTCACATAGCCGGCGTGAGAAAGCGTAACCA CCACATCTTCTTGTGCGATTAAGTCTTCAATGTTGATGTCGCCAGAGGCTGCGGTAA TTTCAGTACGGCGTTCATCATTAAAATCGGTTTTTACACGTTCTAACTCTTCACGGAT CACTTCCATTAAGCGTTCTGGGCTGGTTAAAATGTGGATTAATTCGCCAATTTCAGTT AAGAGTTCTTTGTATTCTTCAACGATTTTCTCGTGTTCTAAACCGGTTAAGCGGTGTA AGCGTAGCTCTAAAATCGCTCTGGCTTGAGCTTCAGATAAATAGTATTCGCCATCAC GCACTCCTAAGTGTTCAGGTAAATCTTCCGGTCGGGAAGCACCAACACCAGCAGCCT CTAACATTGGTGCAACATTGCCTAATGCCCAAGGGCGAGCCAATAAGCCTTCACGG GCTTCTTCTGCGGTTTTTGAGGCTCGAATTAATTCAATTACCGGGTCGATATTCGCTAACGCAATCGCCAAACCTTCTAAAATATGGGCACGTTCACGGGCTTTACGCAATTCAA AAATCGTACGGCGTGTGACCACTTCACGACGGTGTTTAACAAAGGCTTCGATGATCT GGCGTAAGTTCAGCACTTTTGGCTGCCCATTATCTAACGCCACCATATTGATACCAA ACGTGACCTGCATTTGGGTTAAAGAATAAAGGTGGTTTAACACCACTTCACCCACTG CATCACGCTTGACTTCAATAACAATCGAAATCCCTTTTTTATCGGAATAGTCATCAAT TTTGCTGATGCCTTCGATTTTCTTCTCTTTAACCAGCTCGGCAATTTTTTCAATTAATT TTGCCTTATTCACTTGGTATGGCAGTTCGGTTACAACAATTTGCTCACGCCCTTTATC AGTTGTTTCCACGCTGGCTTTCGCGCGCACATACACCTTGCCACGACCGGTGCGGTA AGCCTCTTCAATCCCTTTACGACCGTTGATAATCGCCCCGGTCGGGAAATCCGGCCC CGGGATATAACGCATTAGCTCTTCAACACTAATCTGTTCATTTTCAATATACGCCAA GCAACCATCTAATACTTCGCCTAAATTATGAGGTGGAATATTGGTTGCCATACCGAC CGCAATCCCTGAAGAACCATTGACTAACAGAGCCGGAATTTTGGTTGGCAATACATC AGGAATCATCTCTTTGCCGTCATAGTTCGGTGAGAAATCCACCGTTTCTTTATCGAG ATCGGTTAATAATTCCTGCGTAATTTTCTGCATACGCACTTCGGTATAACGCATTGCT GCCGGTGCATCGCCATCAATAGAACCAAAGTTACCTTGCCCGTCCACCAACATATAA CGTAGTGAGAACGGTTGAGCCATACGCACAATAGTGTCATACACCGCAGAGTCACC GTGTGGGTGATATTTACCGATCACATCACCGACCACACGGGCAGATTTAACGTGCGG CTTATTATAGGTGTTGCTGTTTTGATCCATTGAAAAAAGCACTCGGCGGTGTACCGG CTTTAAACCGTCTCGTACATCAGGCAAGGCACGCCCAACAATCACCGACATCGCATA GTCAAGGTAAGAGGTTTTTAATTCGTCCTCAATACTTACGGGAATAATATCTTTGGC TAATTCGCTCAT SEQUENCE ID. NO.3: (MT) parC (fluoroquinolone resistant (MH001)) (bold / underline denotes SNIP)

[0030] ATGACCACAGAAATCAATTATGAAGGCATTGAACAGATGCCGATTAA GCGTTTTACCGAAGATGCTTACCTCAATTATTCGATGTATGTAATTATGGACCGGGC ATTGCCGTTTATTGGCGATGGCTTGAAACCGGTGCAACGCCGTATTATTTATGCGAT GTCGGAACTTGGGCTAAACGCCTCGGCAAAGTATAAAAAATCAGCCCGTACCGTGG GGGATGTATTAGGTAAATTCCATCCGCACGGTGATAGTGCCTGCTATAAAGCAATGG TGCTAATGGCTCAACCGTTCTCTTACCGCTATCCGTTAGTGGACGGACAAGGCAACT GGGGGGCTCCGGACGATCCGAAATCCTTCGCTGCAATGCGTTATACCGAATCGAAA CTGTCTAAAATTGCAGAAATTTTATTGGGCGAGTTAGGGCAAGGAACGGTGGATTATCAGCCAAATTTTGATGGCTCGTTGGAAGAACCGAAATATCTGCCGGCTCGTTTACCG CATATTTTGTTGAATGGCACGATGGGGATTGCGGTGGGAATGGCAACCGATATTCCA CCACACAATATTAATGAATTGGCTGATGCCAGTGTGATGTTGCTGGATAACCCGAAA GCAACGCTTGATGATGTACTAAGCGTGGTACAAGGGCCGGATTACCCGACAGAGGC TGAAATCATCACACCAAAAGCGGAAATTGCCAGAATGTATGAGCAGGGGCGTGGTT CTATTAAAATGCGTGCGGTGTGGAAAAAAGAAGAGGGTGAAATTGTGATTTCAGCC TTGCCGCACCAAGCCTCTCCTTCAAAAATTATTGAGCAAGTAGCAACCCAAATGCGG AATAAAAAACTACCGATGGTGGACGATATTCGTGATGAATCTGACCACGAGAACCC GATTCGCATTGTGATTGTGCCACGCTCTAATCGCATTGATTTTGATGCGTTAATGGAT CACCTGTTTGCGACGACCGATTTGGAGAAAAGCTACCGAGTCAATATGAATATGATT GGCTTAGACGGCAAGCCGGCAGTGAAGAATTTGCTGACTATTCTCAATGAGTGGCTG AGTTTTAGACGTACCACTGTCACTCGCCGCCTGAATTACCGATTAGATAAAATTTTA AACCGCTTGCATATTTTAGACGGTTTGATGATTGCATTTCTCAATATTGATGAAGTGA TTGAGATTATTCGCAATGAAGATGAGCCGAAAGCCGAGTTAATGGCTCGTTTTAATC TGACCGATGTGCAAGCAGAGGCGATCCTGAACTTACGTTTACGCCACTTAGCCAAAT TGGAAGAGCATCAATTACAGGCGGAAAAATCCGAACTGGAAAAAGAACGTGATGA ATTGCAACTGATTTTAGGCTCGGAACGCCGTTTGAACAGCCTGATCAAGAAAGAGAT TCAAGCCGATGCCAAAGCATTTGCCAGCCCACGCCGCTCACCATTGGTTGAACGAGC GGAGTCTAAAGCGATTGCGGAAAGTGACTTGACTCCAACCGAAGATGTGACTGTGA TTTTGTCTGAAAAAGGCTGGGTACGTTGTGCGAAAGGACACGATATTGATGTTCAAG CCCTAAGCCATCGAGCCGGAGATGGTTATCTTGCCCACGCAAGGGGTAGAAGTAAT CAGCCTGTGGTATTTTTAGACAGTACAGGGCGAGCTTATGCTCTCGATCCAACTTCA CTGCCTTCGGCACGCTCGCAAGGCGAACCGCTTACAGGCAAAATCACCCTACCGGA AGGTGCAGTAGTGCAGCAAGTGCTAATGGCAAGTCCAGAAACTAAAGTGCTAATGG CATCCGATTCAGGTTACGGCTTTATTTGCACCTTTGCAGATTTAGTTTCTCGTAATAA AGCCGGCAAAGCGGTTATTTCTTTAACCGAAAATGCAAAAGTACTGCCGCCGCAATT ATTGGAAAATGATGAAAATTTATCGCTTGTAGCGATGAGTAATGTTGGCAGAATGTT GGTTTTCCCGGTGAGTGAATTACCTCAACTTTCCAAAGGTAAAGGCAATAAAATCAT CAATATTTCCGCCGCAGCGGCAAAATCCGGTGATGAATATTTAGCCCGTTTATTAGT CATCAAACCAACCAACTCGCTGGTCTTTGTGTCCGGTAAACGCAAAATCACATTAAAACCGAGCGATGTTGATAACTACCGTGGCGAACGAGCAAGAAAAGGCTCACAGCTAG TGAGAGGGCTTAGTACCAATTCAACGGTAGA SEQUENCE ID. NO. 4: WT parC (fluoroquinolone susceptible (MH002))

[0031] ATGACCACAGAAATCAATTATGAAGGCATTGAACAGATGCCGATTAA GCGTTTTACCGAAGATGCTTACCTCAATTATTCGATGTATGTAATTATGGACCGGGC ATTGCCGTTTATTGGCGATGGCTTGAAACCGGTGCAACGCCGTATTATTTATGCGAT GTCGGAACTTGGGCTAAACGCCTCGGCAAAGTATAAAAAATCAGCCCGTACCGTGG GGGATGTATTAGGTAAATTCCATCCGCACGGTGATAGTGCCTGCTATGAAGCAATGG TGCTAATGGCTCAACCGTTCTCTTACCGCTATCCGTTAGTGGACGGACAAGGCAACT GGGGGGCTCCGGACGATCCGAAATCCTTCGCTGCAATGCGTTATACCGAATCGAAA CTGTCTAAAATTGCAGAAATTTTATTGGGCGAGTTAGGGCAAGGAACGGTGGATTAT CAGCCAAATTTTGATGGCTCGTTGGAAGAACCGAAATATCTGCCGGCTCGTTTACCG CATATTTTGTTGAATGGCACGATGGGGATTGCGGTGGGAATGGCAACCGATATTCCA CCACACAATATTAATGAATTGGCTGATGCCAGTGTGATGTTGCTGGATAACCCGAAA GCAACGCTTGATGATGTACTAAGCGTGGTACAAGGGCCGGATTACCCGACAGAGGC TGAAATCATCACACCAAAAGCGGAAATTGCCAGAATGTATGAGCAGGGGCGTGGTT CTATTAAAATGTGTGCGGTGTGGAAAAAAGAAGAGGGTGAAATTGTGATTTCAGCC TTGCCGCACCAAGCCTCTCCTTCAAAAATTATTGAGCAAGTAGCAACCCAAATGCGG AATAAAAAACTACCGATGGTGGACGATATTCGTGATGAATCTGACCACGAGAACCC GATTCGCATTGTGATTGTGCCACGCTCTAATCGCATTGATTTTGATGCGTTAATGGAT CACCTGTTTGCGACGACCGATTTGGAGAAAAGCTACCGAGTCAATATGAATATGATT GGCTTAGACGGCAAGCCGGCAGTGAAGAATTTGCTGACTATTCTCAATGAGTGGCTG AGTTTTAGACGTACCACTGTCACTCGCCGCCTGAATTACCGATTAGATAAAATTTTA AACCGCTTGCATATTTTAGACGGTTTGATGATTGCATTTCTCAATATTGATGAAGTGA TTGAGATTATTCGCAATGAAGATGAGCCGAAAGCCGAGTTAATGGCTCGTTTTAATC TGACCGATGTGCAAGCAGAGGCGATCCTGAACTTACGTTTACGCCACTTAGCCAAAT TGGAAGAGCATCAATTACAGGCGGAAAAATCCGAACTGGAAAAAGAACGTGATGA ATTGCAACTGATTTTAGGCTCGGAACGCCGTTTGAACAGCCTGATCAAGAAAGAGAT TCAAGCCGATGCCAAAGCATTTGCCAGCCCACGCCGCTCACCATTGGTTGAACGAGC GGAGTCTAAAGCGATTGCGGAAAGTGACTTGACTCCAACCGAAGATGTGACTGTGA TTTTGTCTGAAAAAGGCTGGGTACGTTGTGCGAAAGGACACGATATTGATGTTCAAGCCCTAAGCCATCGAGCCGGAGATGGTTATCTTGCCCACGCAAGGGGTAGAAGTAAT CAGCCTGTGGTATTTTTAGACAGTACAGGGCGAGCTTATGCTCTCGATCCAACTTCA CTGCCTTCGGCACGCTCGCAAGGCGAACCGCTTACAGGCAAAATCACCCTACCGGA AGGTGCAGTAGTGCAGCAAGTGCTAATGGCAAGTCCAGAAACTAAAGTGCTAATGG CATCCGATTCAGGTTACGGCTTTATTTGCACCTTTGCAGATTTAGTTTCTCGTAATAA AGCCGGCAAAGCGGTTATTTCTTTAACCGAAAATGCAAAAGTACTGCCGCCGCAATT ATTGGAAAATGATGAAAATTTATCGCTTGTAGCGATGAGTAATGTTGGCAGAATGTT GGTTTTCCCGGTGAGTGAATTACCTCAACTTTCCAAAGGTAAAGGCAATAAAATCAT CAATATTTCCGCCGCAGCGGCAAAATCCGGTGATGAATATTTAGCCCGTTTATTAGT CATCAAACCAACCAACTCGCTGGTCTTTGTGTCCGGTAAACGCAAAATCACATTAAA ACCGAGCGATGTTGATAACTACCGTGGCGAACGAGCAAGAAAAGGCTCACAGCTAG TGAGAGGGCTTAGTACCAATTCAACGGTAGAGAT SEQUENCE ID. NO. 5 estT (Macrolides)

[0032] ATGAAAAAAAAACTACTTTGGATATTAATTTTAGGACTGATAATAATC AGTTGCAAACAAAGGAAAACAGAAATGAAAGAGAAAATAATTAAAACAAACGGCA TTGAACTCTGTACGGAAAGTTTTGGAAATAAGAAAAATCCAGCAATCCTTTTGGTAG CAGGTGCAACCGTATCAATGCTGTATTGGGACACTGAATTTTGCCAACAATTATCTG AAAAAGGATTTTTTGTTATTCGTTACGACAACAGAGATGTAGGAAAATCCACTAATT ATGAACCAGGTTCTACTCCATACGATATTGTTGACTTAACTAATGACGCTATTTCAAT ATTGGATGGCTACAAGATTGACAAAGCACATTTTGTGGGGATTTCTTTGGGCGGACT AATTTCTCAAATAGCATCAATAAAGTTTGCCGACAGAGTTAACTCCTTAACTCTTAT GTCATCAGGCCCTTGGGGAGACTCAGACCCAACTATACCTGAAATGGACACGAGTA TTTTAGATTTCCATAGTAAAGCAGGTACAGTCAATTGGACAAATGAAGACAGTGTGG TAAACTATTTAATTCAGGGTGCAGAATTAATGAGTGGCAAGAAACAATTTGACAAA CAAAGAAGTGAAAAACTGATAAGAGCTGAGTTCAATAGAGCCAACAATTATATAAG TATGTTCAATCACGCTGCATTGCAAGGTGGTGGTGAAGAATATTGGAACAGATTAAA CGAAATCAAACAACCCACCTTAATTATCCACGGAACAGACGACAAAATTTGGCATT ATAAGAATGCAGGTTTTTTACTAGAAAAAATAAAAGGTTCAAATCTAATCACCCTTG AAGGTACAGGACACGAATTACACGTTGATGATTGGAAATCAATAATTGATGGAATA GAAAAACACATAAATGACTGA SEQUENCE ID. NO. 6 mphE (Macrolides)

[0033] ATGACAATTCAAGATATTCAATCACTTGCTGAAGCACACGGCTTGTTG ATTACGGACAAAATGAATTTCAATGAAATGGGCATTGATTTTAAGGTCGTTTTTGCT CTTGATACAAAGGGGCAACAATGGTTGCTGCGTATTCCTCGTCGTGATGGCATGAGG GAACAAATCAAGAAAGAAAAACGCATTTTAGAATTGGTAAAAAAACATCTTTCTGT AGAGGTTCCTGATTGGAGAATTTCATCTACAGAATTAGTGGCTTATCCCATACTTAA AGATAATCCTGTTTTAAATTTGGATGCTGAAACCTATGAAATAATTTGGAATATGGA CAAAGATAGCCCGAAATACATAACATCTTTGGCAAAAACCTTATTTGAAATCCATAG TATTCCTGAAAAAGAAGTTCGGGAAAATGATTTGAAAATTATGAAACCTTCAGATTT AAGACCTGAAATAGCAAACAATTTGCAGTTAGTAAAATCTGAAATTGGTATAAGTG AGCAATTGGAAACCCGCTACAGAAAATGGTTGGATAATGATGTTCTATGGGCAGAT TTCACCCAATTTATACATGGCGATTTATATGCTGGGCATGTACTAGCTTCAAAGGAT GGAGCTGTTTCAGGCGTTATTGATTGGTCAACAGCCCATATAGATGACCCAGCGATT GATTTTGCTGGGCATGTAACTTTGTTTGGAGAAGAAAGCCTCAAAACTCTAATCATC GAGTATGAAAAACTAGGGGGTAAAGTTTGGAATAAACTATATGAACAGACTTTAGA AAGAGCAGCGGCCTCTCCTTTGATGTATGGTTTATTTGCCTTAGAAACTCAAAATGA AAGCCTTATCGTTGGAGCAAAAGCTCAGTTGGGAGTTATATAA SEQUENCE ID. NO. 7 msrE (Macrolides)

[0034] ATGAGTTTAATTATTAAAGCGAGAAACATACGCTTGGATTATGCTGGG CGTGATGTTTTGGATATTGATGAATTGGAAATTCACTCTTATGACCGTATTGGTCTTG TGGGTGATAACGGAGCAGGAAAGAGTAGTTTACTCAAAGTACTTAATGGCGAAATT GTTTTAGCCGAAGCGACATTACAGCGTTTTGGTGATTTTGCACATATCAGCCAACTG GGCGGAATCGAAATAGAAACGGTCGAAGACCGGGCAATGTTATCTCGCCTTGGTGT TTCCAATGTACAAAACGACACAATGAGTGGCGGAGAGGAAACTCGTGCAAAAATTG CTGCCGCATTTTCCCAACAAGTACATGGCATTCTAGCGGATGAACCAACCAGCCACC TTGATCTCAATGGAATAGATCTACTTATTGGTCAACTTAAAGCATTTGATGGAGCAT TACTTGTTATCAGTCATGACCGATATTTTCTTGATATGGTTGTAGACAAGATATGGG AGTTAAAAGACGGTAAAATTACGGAATATTGGGGTGGTTACTCGGATTACTTGCGTC AAAAAGAAGAAGAGCGACAACACCAAGCCGTAGAATATGAGCTGATGATGAAGGA ACGGGAGCGATTAGAATCTGCTGTGCAAGAAAAACGCCAGCAAGCTAATCGATTAG ACAATAAGAAAAAAGGAGAAAAATCCAAAAACTCTACCGAAAGTGCTGGACGACTT GGGCATGCAAAAATGACTGGCACCAAGCAAAGAAAACTGTATCAGGCAGCTAAGAGTATGGAAAAGCGTTTGGCTGCATTAGAAGATATTCAAGCACCAGAGCATTTGCGTT CTATTCGTTTTCGTCAAAGTTCAGCCCTAGAACTGCACAATAAGTTCCCGATTACGG CAGATGGTCTGAGCTTAAAATTTGGTAGCCGTACTATCTTTGATGACGCTAACTTTAT AATACCGCTTGGCGCTAAAGTCGCTATAACTGGATCGAATGGAACAGGGAAAACGT CCTTGTTAAAAATGATATCAGAACGTGCTGATGGATTAACCATATCTCCAAAAGCTG AAATTGGCTACTTTACACAAACAGGATATAAATTTAACACGCATAAATCTGTGCTCT CCTTTATGCAGGAAGAGTGCGAGTACACAGTTGCGGAAATTCGTGCAGTATTGGCTT CAATGGGGATCGGAGCGAATGATATTCAAAAAAACTTATCCGACTTATCGGGAGGT GAAATCATCAAACTGCTTTTATCCAAAATGCTTTTAGGAAAATATAATATTTTGCTTA TGGATGAACCAGGAAACTATCTTGACCTAAAAAGTATTGCCGCATTAGAAACAATG ATGAAGTCCTATGCAGGAACTATTATCTTCGTATCTCATGACAAGCAATTGGTCGAT AATATTGCTGACATTATCTACGAGATCAAAGACCACAAAATCATCAAGACTTTTGAG AGAGATTGTTAA SEQUENCE ID. NO. 8 erm(42) (Macrolides)

[0035] ATGAATAAAAACACTAAAATAAAAAACAAAAATTTCAACATTAAAGA CTCACAGAATTTTTTGCATAATACTAAATTAGTCGAAGATTTGCTTTTTAAAAGCAAT ATAACTAAGGAGGATTTTGTTGTTGAGATTGGGCCTGGAAAAGGCATAATAACCAA GGCATTAAGCAAAATCTGCAAAGCCGTTAATGCTATTGAGTTCGATAGTGTATTGGC TGATAAGTTGAGCCATGAATTTAAAAGTTCAAATGTGTCTATTATTGAAGCCGATTT TTTAAAATACAATTTACCAGACCATAATTATAAAGTTTTTTCAAACATTCCATTTAAC ATAACGGCAAGTATTTTAAATAAATTGTTAGATAGTGAGAACCCACCCTTAGATACT TTTTTAATTATGCAATATGAACCTTTTTTAAAGTATGCGGGTGCACCATCTTACAAGG AGTCTTATAAATCTTTATTATATAAACCATTTTTCAAAACTAACATATTGCATAGCTT TAGCAAATTTGATTTTAAGCCAGCTCCAAACGCAAACATTATTTTGGGCCAATTTTCT TATAAAGACTTTACAGATATAAACCTTGAAGACAGGCATGCTTGGAAAGATTTTTTA GCCTTTGTCTTTTTAGAAAAGGGAGTTACATTTAAAGAAAAAACAAAACGAATTTTT AGTTATAAGCAACAAAAAATAATTTTAAAAGAAAGCCGAATTAATGATGATTCAAA TATAAGTAATTGGAGTTATGAATTTTGGCTAAAAATGTTTAAACTCTATAATTCGAA CATGGTAAGCAAGGATAAAAAAGTTTTAGTTAACAATTCGTATAAAAGAATGTTAG AACATGAGTCTAGTTTAGAAAAGATTCATAGAAATAGAAAGCAAAATAACAGAAAA TAGSEQUENCE ID. NO. 9 erm(47) (Macrolides)

[0036] ATGAACAGAAAAAGTGTTAGATTTGGACAAAATTTTGTAACTTCTATT AATGATATAAACAAAATATGTAAGAAGATAGACGTGAATTCTAATGATGTTTATTTT GAAATTGGTCCAGGTAAAGGGCATTTTACTCAATACTTTGTGGAAAGGGCTAAACA GGTAATTGCTATTGAAATAGACAGTGAATTAATTCCTATATTAAACAACAAATTTTC AGATCTAGATAATATAAAAATTGTTAATCATGACTTTATGTCTTATGAATTACCATCT ACATTTAAGTATAAAGTTTTTGGAAATATTCCATTTAATTTGAGTACTTCTATTATTC GTAAACTTAGTTTAGAAAAATATGCAGATGAGATTTACTTAATAGTGGAATTAGGGT TTGCAAAAAGATTAGAAGACTTAAACCGTAAAATGGGCCTAATGTTAGCTCCATTTT ATGAAATTTCAATTTTATACAATATTCCTAAAAGATATTTTCATCCCATACCCAGTGT TGAGGTAGTGCTGATAAAACTAAAAAGAACTTCCTATAATATGTCCATGAAAGAAT ATATAAAGTATGAAGACTTTATAGAAAAATGGGTAAAAAAGGATTATAATGTTTTAT TTACAAAAAATCAGCTAAAACAAGCAATCAGATATGGAAATATTGATAATTTAAGA ATCCTAAAAGTTGATCAAATTCTATCCATATTTGAAAGTTACAAATTATTTAATGGG TTAAAGTAA SEQUENCE ID. NO. 10 floR (Phenicol)

[0037] ATGACCACCACACGCCCCGCGTGGGCCTATACGCTGCCGGCAGCACTG CTGCTGATGGCTCCTTTCGACATCCTCGCTTCACTGGCGATGGATATTTATCTCCCTG TCGTTCCAGCGATGCCCGGCATCCTGAACACGACGCCCGCTATGATCCAACTCACGT TGAGCCTCTATATGGTGATGCTCGGCGTGGGCCAGGTGATTTTTGGTCCGCTCTCAG ACAGAATCGGGCGACGGCCAATTCTACTTGCGGGCGCAACGGCTTTCGTCATTGCGT CTCTGGGAGCAGCTTGGTCTTCAACTGCACCGGCCTTTGTCGCTTTCCGTCTACTTCA AGCAGTGGGCGCGTCGGCCATGCTGGTGGCGACGTTCGCGACGGTTCGCGACGTTTA TGCCAACCGTCCTGAGGGTGTCGTCATCTACGGCCTTTTCAGTTCGGTGCTGGCGTTC GTGCCTGCGCTCGGCCCTATCGCCGGAGCATTGATCGGCGAGTTCTTGGGATGGCAG GCGATATTCATTACTTTGGCTATACTGGCGATGCTCGCACTCCTAAATGCGGGTTTCA GGTGGCACGAAACCCGCCCTCTGGATCAAGTCAAGACGCGCCGATCTGTCTTGCCG ATCTTCGCGAGTCCGGCTTTTTGGGTTTACACTGTCGGCTTTAGCGCCGGTATGGGCA CCTTCTTCGTCTTCTTCTCGACGGCTCCCCGTGTGCTCATAGGCCAAGCGGAATATTC CGAGATCGGATTCAGCTTTGCCTTCGCCACTGTCGCGCTTGTAATGATCGTGACAAC CCGTTTCGCGAAGTCCTTTGTCGCCAGATGGGGCATCGCAGGATGCGTGGCGCGTGGGATGGCGTTGCTTGTTTGCGGAGCGGTCCTGTTGGGGATCGGCGAACTTTACGGCTC GCCGTCATTCCTCACCTTCATCCTACCGATGTGGGTTGTCGCGGTCGGTATTGTCTTC ACGGTGTCCGTTACCGCGAACGGCGCTTTGGCAGAGTTCGACGACATCGCGGGATC AGCGGTCGCGTTCTACTTCTGCGTTCAAAGCCTGATAGTCAGCATTGTCGGGACATT GGCGGTGGCACTTTTAAACAGTGACACAGCGTGGCCCGTGATCTGTTACGCCACGGC GATGGCGGTACTGGTTTCGTTGGGGCTGGTGCTCCTTCGGCTCCGTGGGGCTGCCAC CGAGAAGTCGCCAGTCGTCTAA SEQUENCE ID. NO. 11 tet(H) (Tetracycline)

[0038] ATGAATAAATCAATTATTATTATACTGCTGATCACCGTATTAGATGCC ATTGGTATCGGGCTTATCATGCCAGTACTCCCTACTCTATTAAATGAATTTGTCAGTG AAAATTCACTGGCAACCCATTACGGTGTGCTATTAGCGCTCTATGCTACCATGCAGG TTATTTTTGCTCCTATTCTAGGACGACTGTCTGATAAATACGGCAGAAAACCCATCTT GCTGTTTTCCCTTTTAGGCGCGGCACTCGACTATCTTTTAATGGCATTCTCAACCACA CTTTGGATGCTCTATATTGGGCGCATCATTGCGGGGATCACAGGCGCAACAGGTGCC GTATGTGCATCAGCGATGAGTGATGTGACTCCCGCTAAAAATCGAACTCGCTATTTT GGTTTCTTAGGTGGTGCTTTTGGTGTTGGCCTTATTATCGGCCCAATGCTAGGGGGAT TATTAGGTGATATCAGTGCTCATATGCCATTTATTTTTGCCGCTATTTCACACTCGAT ATTATTAATACTCTCTTTGCTCTTTTTCCGAGAAACACAAAAAAGAGAAGCGCTTGT TGCCAATAGGACACCTGAAAACCAAACTGCCTCAAATACAGTCACTGTTTTTTTTAA GAAAAGCCTCTACTTTTGGTTAGCAACCTATTTTATTATCCAGCTTATCGGGCAAATT CCTGCCACCATCTGGGTGCTGTTTACACAATATCGTTTTGATTGGAACACAACTTCTA TCGGTATGTCTTTGGCGGTTCTGGGTGTATTACATATTTTCTTTCAGGCGATTGTCGC TGGGAAATTGGCACAAAAATGGGGCGAAAAAACCACCATTATGATCAGTATGTCTA TTGATATGATGGGCTGTTTATTATTAGCGTGGATAGGCCACGTTTGGGTCATCTTACC AGCATTAATTTGCTTAGCGGCAGGAGGTATGGGGCAACCCGCATTACAAGGTTATTT ATCAAAATCTGTCGATGATAATGCGCAAGGGAAATTACAAGGTACTCTGGTGAGCC TAACCAATATTACCGGGATCATTGGTCCCCTTTTATTTGCCTTTATTTATAGTTATAG CGTCGCTTATTGGGATGGTCTGTTATGGCTGATGGGGGCAATACTTTATGCTATGTTG CTTATTACCGCTTATTTTCACCAAAGAAAAACCACACCTAAAGCTGTTATTTCAACC CCTTAA SEQUENCE ID. NO. 12 tet(A) (Tetracycline)

[0039] GTGAAACCCAACAGACCCCTGATCGTAATTCTGAGCACTGTCGCGCTC GACGCTGTCGGCATCGGCCTGATTATGCCGGTGCTGCCGGGCCTCCTGCGCGATCTG GTTCACTCGAACGACGTCACCGCCCACTATGGCATTCTGCTGGCGCTGTATGCGTTG ATGCAATTTGCCTGCGCACCTGTGCTGGGCGCGCTGTCGGATCGTTTCGGGCGGCGG CCGGTCTTGCTCGTCTCGCTGGCCGGCGCTGCTGTCGACTACGCCATCATGGCGACG GCGCCTTTCCTTTGGGTTCTCTATATCGGGCGGATCGTGGCCGGCATCACCGGGGCG ACTGGGGCGGTAGCCGGCGCTTATATTGCCGATATCACTGATGGCGATGAGCGCGC GCGGCACTTCGGCTTCATGAGCGCCTGTTTCGGGTTCGGGATGGTCGCGGGACCTGT GCTCGGTGGGCTGATGGGCGGTTTCTCCCCCCACGCTCCGTTCTTCGCCGCGGCAGC CTTGAACGGCCTCAATTTCCTGACGGGCTGTTTCCTTTTGCCGGAGTCGCACAAAGG CGAACGCCGGCCGTTACGCCGGGAGGCTCTCAACCCGCTCGCTTCGTTCCGGTGGGC CCGGGGCATGACCGTCGTCGCCGCCCTGATGGCGGTCTTCTTCATCATGCAACTTGT CGGACAGGTGCCGGCCGCGCTTTGGGTCATTTTCGGCGAGGATCGCTTTCACTGGGA CGCGACCACGATCGGCATTTCGCTTGCCGCATTTGGCATTCTGCATTCACTCGCCCA GGCAATGATCACCGGCCCTGTAGCCGCCCGGCTCGGCGAAAGGCGGGCACTCATGC TCGGAATGATTGCCGACGGCACAGGCTACATCCTGCTTGCCTTCGCGACACGGGGAT GGATGGCGTTCCCGATCATGGTCCTGCTTGCTTCGGGTGGCATCGGAATGCCGGCGC TGCAAGCAATGTTGTCCAGGCAGGTGGATGAGGAACGTCAGGGGCAGCTGCAAGGC TCACTGGCGGCGCTCACCAGCCTGACCTCGATCGTCGGACCCCTCCTCTTCACGGCG ATCTATGCGGCTTCTATAACAACGTGGAACGGGTGGGCATGGATTGCAGGCGCTGCC CTCTACTTGCTCTGCCTGCCGGCGCTGCGTCGCGGGCTTTGGAGAAATTCTTCAAATT CCCGTTGCACATAG SEQUENCE ID. NO. 13 tet(T) (Tetracycline)

[0040] ATGAAAATTATTAATATAGGAATATTAGCCCATGTTGATGCAGGAAAA ACAACTGTTACAGAAGGTTTATTATACAAAGGTGGGGCAATTAATAAGATTGGAAG AGTCGATAATGGTACAACGATAACGGACTCGATGGAACTTGAAAGAGATAGGGGGA TAACTATACGAGCATCTACAGTTTCATTTAATTACAATGATACAAAGCTAAATATCA TAGATACCCCTGGGCATATGGATTTTATAGCTGAAGTTGAACGAACACTGAGAGTAT TAGATGGAGCTGTTTTAGTAATTTCAGCAAAAGAGGGAATACAAGTTCAAACGAAA GTGATTTTTAATACCTTAGTGAAATTAAATATTCCAACCCTTATATTTGTGAATAAAA TAGATCGAAAGGGAGTATATTTGGATGAAATATACACTCAAATACAGGAGAAATTAACTTCTAATCTTGCAATAATGCAATCAGTTAAAATAAAAGATAAAGGTGATTTTGAA TTGACAAATGTAAGGGATGATAAAGTAATTCAAAGTCAAATAATAGAGAAGTTACT GGATATAAATGATTATCTAGCAGAAAAATATATAAATGGCGATGTCATTACAGAAA AAGAGTATGATAATGTATTTCTGGATGAGGTAAATAGTTGCAATCTTTATCCTGTTTT GCATGGTTCTGCTTTAAAAGATATTGGGATTGATGAGTTGCTATTTGCTATTACAAA CTATCTTCCTGTAAATAATGATAATATTACAGATAACCTATCTGCGTATGTTTATAAG ATAGATAGGGATGAAGAATCCCGTAAGATTACTTTCCTAAGAGTATTCAGTGGGAAT ATAAGGACACGTCAAGATGTTTATATAAATGGCACAGAAGAAACTTTCAAGATAAA AAGTCTGGAATCAATTATGAATGGTGAAATTGTGAAGGTAGATCAGGTTAATAGTG GGGATATTGCTATTATTTCTAATGCTAATTCTCTGAAGATAGGTGATTATATTGGTAA GAAATATGACGGGATTTTAGATATAAAGATAGCCCAACCGGCATTGAGAGCATCAA TTAAACCTTGTGATTTAAGCAAAAGAAGCAAACTGATAGAAGCACTATTTGAATTAA CTGAAGAAGACCCATTTCTCGATTGTGAAATTAACGGAGATACTGGAGAAATCATAT TGAAGCTATTTGGAAATATTCAGATGGAAGTAATCAAATCACTACTTAAAAACAGAT ATAAAATAGATACTGAATTTGGTGAATTGAAAACAATATATAAAGAACGACCTAAG AGAAACTCTAAAGCAGTAATCCATATAGAGGTTCCACCAAATCCTTATTGGGCATCT ATTGGACTGTCAATAGAACCACTACCAATAGGGTCAGGATTATTATATAAGACAGA GGTGTCCTATGGATATTTAAATAATTCATTTCAAAATGCAGTAAAAGATGCTGTAGA GAAGGCTTGTAAAGAAGGGCTTTATGGATGGGAAGTTACAGATTTAAAAATAACTT TTGACTACGGATTATACTATAGTCCAGTAAGTACCCCCTCTGACTTTAGAAATTTAA CACCATATGTATTTTGGGAAGCTCTTCGAAAAGCAGGAACTGAAATATTAGAACCTT ATTTAAAATATACAGTTCAAGTTCCAAATGATTTCTGCGGAAGAGTTATGAGTGATC TCAGGAAGATGAGGGCTTCTATTGAAGATATAATAGGCAAGGGAGAAGAGACAACT TTAAGTGGAAGGATACCTGTTGATACATCGAAATCCTATCAGGCAGAATTACTTTCT TATTCAAATGGAAAGGGTATATTTATTACTGAGCCTTATGGTTATGATATATATAAT GGTGAGTCTATAACTAATGATATTAGAAATAATGATAATGATAGTAGCAAAGAAGG ACTGAGATATTTATTTCAGAAACAAAGTGAAATTTGADETAILED DESCRIPTION

[0041] The following discussion is presented to enable any person skilled in the art to make and use the technology disclosed and is provided in the context of a particular applicationand its requirements as well as a particular system environment and its requirements. Various modifications to the disclosed implementations will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other implementations and applications without departing from the spirit and scope of the technology disclosed. Thus, the technology disclosed is not intended to be limited to the implementations shown but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0042] The present disclosure includes various assays, kits, and methods for single-step identification of one or more of the genetic determinants in a pathogen associated with phenotypic resistance to one or more antimicrobial therapeutics in order to determine susceptibility of the pathogen to an antimicrobial therapeutic treatment. These assays (and methods of treatment using such assays) can be portable, disposable, and capable of providing fast and accurate results in the field without the need for a laboratory and other complex equipment. The terms “treat,” “treating,” “treated,” or “treatment” (with respect to a disease or condition) is an approach for obtaining beneficial or desired results including and preferably clinical results and includes, but is not limited to, one or more of the following: improving a condition associated with a disease, curing a disease, lessening severity of a disease, delaying progression of a disease, alleviating one or more symptoms associated with a disease, increasing the quality of life of one suffering from a disease, prolonging survival and / or prophylactic or preventative treatment.

[0043] In at least one embodiment, a portable assay or method using the same is provided that includes a loop-mediated isothermal amplification (LAMP) assay that utilizes primers (e.g. primer sets and / or panels). Also disclosed herein are detection methods using LAMP assays that can specifically target and detect the presence of genetic determinants of phenotypic resistance in Mannheimia haemolytica (Mh), In that regard, the assays, kits and methods hereof can be used to rapidly and accurately indicate susceptibility in the field to a particular antibiotic treatment

[0044] LAMP uses 4-6 primers that can recognize 6-8 distinct regions of target deoxyribonucleic acid (DNA) for a highly specific amplification reaction. A strand-displacing DNA polymerase initiates synthesis and two specifically designed primers form “loop” structures to facilitate subsequent rounds of amplification through extension on the loops and additional annealing of primers. DNA products are typically long (>20 kb) and formed from numerous repeats of the short (80-250 bp) target sequence, connected with single-stranded loopregions in long concatamers. These products are not typically appropriate for downstream manipulation, but the achievable target amplification can be so extensive that numerous modes of detection are possible.

[0045] Real-time fluorescence detection using intercalators or probes, lateral flow, and agarose gel detection, for example, are all directly compatible with LAMP reactions.Instrumentation for LAMP typically requires consistent heating to the desired reaction temperature and, where desired, real-time fluorescence for quantitative measurements.Optimized settings for running LAMP assays on isothermal instruments are known in the art, and the assay can be performed using the techniques described in detail in at least Notomi, et al., Nucleic Acids Res. 2000, Jun 15; 28(12) and Nagamine et al., Mol. Cell. Probes 2002; 16: 223-229, both of which are incorporated herein by reference in their entireties.

[0046] In certain instances, LAMP can be so prolific that the products and byproducts of these reactions can be visualized by the naked eye. For example, magnesium pyrophosphate produced during the reaction can be observed as a white precipitate or added indicators (e.g., calcein or hydroxynaphthol blue) can be used to signal a positive reaction or an indicative pH change.

[0047] In certain embodiments, the LAMP assay can be coupled with a colorimetric reagent that is sensitive to magnesium or pH and allows for visualization of the result with the naked eye and / or quantification using a camera. Such colorimetric reagents, for example, can include a phenol red In an exemplary embodiment, the primers described herein are coupled with a composition comprising phenol red, such as, for example and without limitation, Warmstart® LAMP 2 x Master Mix.

[0048] Warmstart® LAMP 2 x Master Mix, which contains phenol red, is characterized by its transition from pink to yello as the LAMP reaction occurs and the pH decreases. Positive and negative results using the primers hereof for detection of BRD pathogens and the pink and yellow color distinction can be observed.

[0049] As discussed, there is a need to development of a rapid diagnostic test to determine antimicrobial susceptibility in Mh and other pathogens. While much information on the phenotypic or genotypic antibiotic resistance of Mh isolates from BRD cases has been examined, there is a lack of comparison of the two types of data. This is important because the presence of antibiotic resistance genes does not guarantee phenotypic resistance. Previousanalysis on phenotype to genotype comparisons in BRD pathogens have focused on analysis of Pasteurella multocida, Histophilus somni, and Mh where genotype-phenotype concordance rates ranged from 17-100%. For a rapid antibiotic susceptibility test to be reliable, the concordance rates would need to be improved considerably. Moreover, conventional precedent does not allow for such determinants to be accurately detected in a field setting. The colorimetric LAMP assays for genetic determinants provided herein are based on high concordance between genotype and phenotype across various isolates and are, therefore, highly accurate indicators of antibiotic susceptibility for determining a course of treatment.

[0050] The colorimetric LAMP assays hereof offer at least six advantages: (1) they can be conducted on the farm / in the field using a simple consumer-grade water bath; (2) they can provide a visual readout and, thus, allow for analysis with the naked eye; (3) they provide a response within 60 minutes; (4) they do not require sample processing (e.g., extraction of nucleic acids); (5) they can detect at least genetic determinants of Mh with a high degree of accuracy; and (6) they utilize a simple non-invasive nasal swab for sampling.

[0051] LAMP is well-suited for point-of-care and field diagnostics using all manner of sample types. Further, the LAMP reaction is robust and tolerant of inhibitors, allowing for crude sample prep and minimal nucleic acid purification, if desired.

[0052] In at least one illustrative LAMP assay hereof, the assay may comprise one or more LAMP primer sets that targets deoxyribonucleic acid (DNA) fragments of a suite of genetic determinants of phenotypic resistance to one or more antimicrobial therapeutics in a sample (e.g., a bovine nasal sample or a bovine water sample taken from a trough from which the cattle drink, for example), wherein the assay allows for single-step identification of one or more of the genetic determinants in a pathogen associated with phenotypic resistance to one or more antimicrobial therapeutics. The pathogen associated with BRD can be, for example, a bacterium (e.g., bacterium or a bacterial mycoplasma), e.g., Mannheimia haemolytica (Mh).

[0053] In certain embodiments, the DNA fragments comprise Mh genes of mutant-type (MT) gyrA of SEQ. ID. NO. 1, wild-type (WT) gyrA of SEQ ID. NO. 2, MT parC of SEQ ID. NO. 3, WT parC of SEQ ID. NO. 4, estT of SEQ ID. NO. 5, mphE of SEQ ID. NO. 6, msrE of SEQ ID. NO. 7, erm(42) of SEQ ID. NO. 8, erm(47) of SEQ ID. NO. 9, floR of SEQ ID. NO. 10, tet(H) of SEQ ID. NO. 11, tet(A) of SEQ ID. NO. 12, and tet(T) of SEQ ID. NO. 13. In examples, the at least one LAMP primer set is one or more of primer sets of one or morefragments of each of SEQ. ID. NOS. 1-13. Each assay can also include various primer sets drawn to any combination of the foregoing. In certain embodiments, each LAMP primer set is at least about 98% specific to the targeted DNA fragment. By way of examples, Primer sets may span <200 bp of a target gene sequence. The LAMP primer set can also comprise loop primers (e.g., labelled as LF (loop forward) and / or LB (loop backward)). Embodiments of the assay can process and provide a visual result in 60 minutes or less. For example, the visual result can be indicative of the presence or absence of genetic determinants of antibiotic resistance in the sample.

[0054] Embodiments of such assays can process results to indicate susceptibility to an antibiotic treatment based in indications of the presence or absence of genetic determinants of antibiotic resistance. By way of example, such assays may process results to indicate susceptibility to multiple classes of antibiotics, including, e.g., quinolones (e.g., fluoroquinolone), macrolides (e.g., tilmicosin, tulathromycin) phenicols (e.g., florfenicol, thiamfenicol), and tetracycline (e.g., oxytetracycline, chlortetracycline).

[0055] The visual result can, for example, identify the type of pathogen present in the sample. In certain embodiments, the visual result is a color-coded or colorimetric result.

[0056] In certain embodiments, the assay comprises one or more indicators. Such indicators, for example, can comprise a pH- or magnesium-sensitive indicator. In some embodiments, the indicator comprises a magnesium-based indicator. In some embodiments, the indicator is a fluorescent indicator.

[0057] In certain embodiments, each LAMP primer set is associated with a colorimetric reagent. The colorimetric reagent can be pH sensitive or magnesium sensitive, for example. In certain embodiments, the colorimetric reagent is phenol red.

[0058] In examples, a method is provided for identifying deoxyribonucleic acid (DNA) fragments of a suite of genetic determinants of resistance to one or more antimicrobial therapeutics in a sample. In at least one embodiment, the method comprises: providing a LAMP primer panel that targets DNA fragments of a suite of respective genetic determinants of resistance to one or more antimicrobial therapeutics in a sample; obtaining a sample from a subject; combining the sample and the LAMP primer panel into a mixture; heating the combination to initiate amplification of the targeted DNA fragment; and detecting a visual result in the heated combination indicative of the presence or absence genetic determinants ofresistance. There, the at least one LAMP primer set can be, for example, one or more of primer sets of one or more fragments of each of SEQ. ID. NOS. 1-13.

[0059] In some cases, the methods can provide a visual result in 60 minutes or less of initiating the heating step and the sample is a bovine nasal sample (or a bovine water sample).

[0060] The step of detecting a visual result can also further comprise measuring a relative clarity of the heated combination using a turbidimeter; and analyzing colorimetric data in the visual result using one or more of a fluorescent reader, an ultraviolet light reader, or camera. The visual result can be indicative of the presence or absence of the targeted DNA determinant. In certain embodiments, if the visual result is indicative of the presence of a targeted DNA determinant, the method further comprises treating the subject (or cohort (e.g., herd)) for the targeted pathogen.

[0061] In certain embodiments of the method, the at least one LAMP primer set is coupled with a colorimetric reagent that is pH sensitive or magnesium sensitive. For example, the colorimetric agent can be phenol red.

[0062] In examples, a kit is provided for performing methods identifying DNA fragments of a suite of genetic determinants of resistance to one or more antimicrobial therapeutics in a sample. In at least one embodiment, the kit comprises a fluorescent indicator, and a fluorescent reader, an ultraviolet light reader, or a camera to provide color metric result data indicative of the presence or absence of a targeted pathogen in the sample. In at least one embodiment, the at least one primer set is coupled with a colorimetric reagent that is pH sensitive or magnesium sensitive. In certain embodiments, the colorimetric agent is phenol red. In examples, the kit provides a solid support medium on which LAMP assay can be performed. In the alternative, the kit provides a liquid medium in which LAMP assay can be performed.

[0063] In certain embodiments, the at least one swab comprises a nasal swab and the kit further comprises a sealable container with a transport media therein. In certain embodiments, the heating element is a water bath.

[0064] The kits hereof can be portable and capable of use in a non-laboratory setting (e.g., the field).

[0065] The targeted DNA of each pathogen is, preferably, a DNA segment or region that has little to no homology with non-genetic determinants of phenotypic resistance in Mh or nontargeted BRD-associated pathogens. Each LAMP primer set of the assay is designed to targetand amplify the targeted gDNA determinant from Mh, while maintaining little to no amplification of other pathogens or negative samples. Each LAMP primer set can include 4 to 6 DNA primers for each determinant (however the number of primers used can be modified, as desired).

[0066] The results of the LAMP assays hereof can, in some embodiments, be seen with the naked eye. While conventional versions of LAMP assays require SYBR Green staining for signal detection (which necessitates opening the tube after thermal incubation) the LAMP assays hereof can be performed with a turbidimeter (e.g., a Loopamp real-time turbidimeter) to detect a positive signal. A turbidimeter measures the relative clarity of the sample and does not require opening the tube, which reduces the risk of environmental diffusion and cross-contamination during gene amplification.

[0067] In certain embodiments, magnesium pyrophosphate produced during the reaction can be observed as a white precipitate or added indicators (eg., calcein, magnesium -based indicators, or hydroxynaphthol blue) can be used to signal a positive reaction or an indicative pH change.

[0068] In certain embodiments, the LAMP assays hereof can be coupled with or include indicators (e.g., colorimetric reagents or indicators) to allow for visual inspection of assay results without opening the reaction tube. Such assay results can provide a visual result that corresponds to the presence or absence of the targeted gDNA in the sample. In some cases, the visual result is color-coded and / or colorimetric, and in other cases the result can be a letter, number, word, symbol, lines, or other representation indicative of the presence or absence of the targeted gDNA. For example, if one of the LAMP primer sets is targeted to a DNA fragment of a genetic determinant of phenotypic resistance, the LAMP primer set will identify and amplify that DNA fragment. Where the assay further includes an indicator associated with each LAMP primer set, the indicator associated with the amplified will be easily detectable in the results.

[0069] Fluorescence can also be employed to facilitate signal detection. In at least one embodiment, the LAMP assays hereof further comprise fluorescent dye in the reagents mix for assay or a fluorescent tag coupled with the primers themselves. Fluorescent data / intensities can thereafter be collected (using thermocyclers or a fluorometer, for example) and analyzed. In the above non-limiting example where a loop primer is directed to a unique DNA fragment associated genetic determinants of phenotypic resistance, a particular fluorescent indicator can becoupled with such primer so that visualization of the fluorescence of that particular fluorescent indicator is indicative of the sample being positive that primer.

[0070] Colorimetric reagents can be coupled with the primer set(s) of the LAMP assays described herein. In certain embodiments, the colorimetric agent is pH sensitive (e.g., phenol red). While specific embodiments and examples are provided herein, it will be appreciated that any colorimetric reagent sensitive to pH or magnesium can be employed.

[0071] Where multiple primer sets are used in the same assay, and each primer set is directed to a different genetic determinant, indicators can be used to easily identify in the visual results which determinant is present in the sample. In at least one embodiment, for example, the first primer set can be labeled (at their 5'-ends, for example) with a stable, fluorescent material of a first intensity, the second primer set can be labeled with a stable, fluorescent material of a second intensity, and the third primer set can be labeled with a stable, fluorescent material of a third intensity using methods commonly known in the relevant arts. When the relevant primer set anneals to a complementary target amplicon (i.e. the amplified DNA fragment of the targeted determinant), the 5' — >3' exonucleolytic activity of DNA polymerase detaches the label from the primer, which results in an enhanced fluorescence signal at the intensity of the fluorescent material used for the primer set with which there was a match. Accordingly, assessment of the resulting intensity can identify which determinant is present within the sample. While fluorescent indicators are described above, it will be appreciated that any type of indicators can be used with the novel assays of the present disclosure, including other indicators now known or hereinafter developed.

[0072] Additionally, certain embodiments of the LAMP assays can optionally utilize a fluorescent reader, an ultraviolet light reader, and / or a camera for signal detection and / or the display of assay results (e g., where indicators are used). In these embodiments, the visual results may be colorimetric and / or digitally provided, such as, for example, through a wireless device, laptop computer, or cell phone and may utilize WiFi, Bluetooth, or cellular data.

[0073] The LAMP assays (and primer sets thereof) can detect the targeted pathogenic DNA fragments in various sample types and, in certain embodiments, does not require that such samples be processed prior to running the assay. For example, a sample can comprise a simple water sample or an unprocessed bovine nasal sample (e g., obtained via a nasal swab). The ability to use unprocessed samples is advantageous for several reasons, at least one of whichbeing that the assay translates easily to field use due to the ease of incubation. In certain embodiments, the samples, once collected, can be housed in a tube or vial containing a transport medium suitable for the collection, transport and / or handling of the specimen. For example, and without limitation, the transport medium can be liquid amies transport media.

[0074] Kits for testing one or more samples are also provided. Such diagnostic kits can be configured for field use such as, for example, in a feed lot or on-site at another type of cattle operation. Accordingly, the kits can be portable and capable of use in a non-laboratory setting.

[0075] In at least one alternative embodiment, the kits hereof comprise a multiplex qPCR diagnostic assay comprising one or more of qPCR primer sets. In certain embodiments, such assays comprise one or more of the qPCR primer sets that comprise one or more primers of one or more SEQ ID NOS. 1-13. Primers may be designed using, e.g.., Primer-BLAST by NCBI. https: / / www.ncbi.nlm.nih.gov / tools / primer-blast / . Other software primer design tools constant for use herewith include Primer3, Primer3Plus, PrimerQuest, OligoPerfect, PeriPrimer, OLIGO, GenScript Online PCR Primers Designs Tool, AutoPrime, RExPrimer, BatchPrimer3, Eurofins, and Genomics' Primer Design Tool.

[0076] The kit may further comprise at least one swab for obtaining a sample from a subject (e.g, a bovine) and / or a vial or other container for receiving the at least one swab after a sample is collected. In at least one exemplary embodiment, the container can be used as the incubation environment for the collected sample and one or more LAMP primer sets (i.e. where the amplification reaction is performed on the collected sample). Accordingly, the container can contain a transport media or the like as is known in the art, and / or any additional reagents that are useful in facilitating the DNA amplification reaction and / or visualizing the results thereof. For example, in at least one embodiment, UDG / UTG can be added to the media within the container to degrade leftover amplicons present therein after amplification of the targeted DNA.

[0077] In at least one embodiment, the container is sealable and is at least partially transparent such that visual results present within the container can be visualized without opening the container itself.

[0078] The assay of each kit can further comprise an indicator associated with each LAMP primer set. As described above, the LAMP primer sets can be configured to include the indicator (e.g., a fluorescent indicator coupled with an end of each primer) or the indicator can be added to the media housed by the container.

[0079] In certain embodiments, the indicator of each kit comprises a colorimetric reagent. For example, in certain embodiments, one or more of the LAMP primer sets can be coupled with a colorimetric reagent that is pH sensitive or magnesium sensitive. In certain embodiments, the colorimetric agent is phenol red.

[0080] The kit may further comprise a heating element to initiate amplification of the targeted DNA fragment when the at least one LAMP primer set and the sample are combined, for example, in the container. In certain embodiments, the heating element is a water bath. The kit can also, optionally, comprise a fluorescent reader, an ultraviolet light reader, or a camera to provide color metric result data indicative of the presence or absence of a targeted pathogen in the sample.

[0081] With reference to Fig. 1,, methods for identifying a genetic determinant of phenotypic resistance in a sample and, based thereon, determining susceptibility to a given antibiotic for use in designing a treatment are also provided. In the illustrated embodiment, the method comprises, in 102, providing a panel of LAMP primer sets each set targeting a DNA fragment of a genetic determinant of phenotypic resistance to an antibiotic in a sample.; in 104, obtaining a sample from a subject; at 106, combining the sample and the panel of LAMP primer sets into a mixture; at 108, heating the combination to initiate amplification of the targeted DNA fragment; and, at 110, detecting a visual result in the heated combination indicative of the presence or absence of the targeted determinant in the sample. In certain embodiments, the method may, at least in part, be performed on a paper substrate. In the same or certain other embodiments, a substrate on which the method may, at least in part, be performed is housed within a device. In one example, the device is a field-operable, handheld device.

[0082] gDNA determinants include Mh genes of mutant-type (MT) gyrA of SEQ. ID. NO. 1, wild-type (WT) gyrA of SEQ ID. NO. 2, MT parC of SEQ ID. NO. 3, WT parC of SEQ ID. NO. 4, estT of SEQ ID. NO. 5, mphE of SEQ ID. NO. 6, msrE of SEQ ID. NO. 7, erm(42) of SEQ ID. NO. 8, erm(47) of SEQ ID. NO. 9, floR of SEQ ID. NO. 10, tet(H) of SEQ ID. NO. 11, tet(A) of SEQ ID. NO. 12, and tet(T) of SEQ ID. NO. 13.

[0083] In certain embodiments, of the visual result indicates the presence of at least one genetic determinant in the sample, the method further comprises identifying an antibiotic to which Mh is susceptible. Susceptibility / resistance may be based on the detection of the presence and / or absence of one or more genetic determinants, wherein(a) the detected absence of both MT gyrA (SEQ. ID. NO. 1) (and, optionally, the presence of WT gyrA (SEQ. ID. NO. 2) as a control) and MT parC (and, optionally, the presence of WT parC (SEQ. ID. NO. 4) is indicative of susceptibility to treatment with a quinolone, e.g., fluoroquinolone;(b) the detected absence of each of estT (SEQ ID. NO. 5), mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), erm(47) (SEQ. ID. NO. 9) is indicative of susceptibility to treatment with an antibiotic on the macrolide class;(c) the detected presence estT (SEQ ID. NO. 5) and one of mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), or erm(47) (SEQ. ID. NO. 9) is indicative of resistance to treatment with an antibiotic on the macrolide class;(d) the detected presence estT (SEQ ID. NO. 5) and one the absence of each mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), and erm(47) (SEQ. ID. NO. 9) is indicative of susceptibility to treatment with the tulathromycin, tilmicosin, and / or tildipirosin; (e) the detected presence of flor (SEQ. ID. NO. 10) is indicative of resistance to treatment with phenicol;(f) the detected absence of flor (SEQ. ID. NO. 10) is indicative of susceptibility to treatment with phenicol;(g) the detected presence of one of tet(H) (SEQ ID. NO. 11), tet(A) (SEQ ID. NO. 12), and tet(T) (SEQ ID. NO. 13) is indicative of resistance to treatment with tetracycline (e.g., oxytetracycline, chlortetracycline); and(h) the detected absence of each of tet(H) (SEQ ID. NO. 11), tet(A) (SEQ ID. NO. 12), and tet(T) (SEQ ID. NO. 13) is indicative of susceptibility to treatment with tetracycline (e.g., oxytetracycline, chlortetracycline);

[0084] Figs. 2A-D present example panel results of genetic determinant detection and antibiotic susceptibility in a sample.

[0085] In Figs. 2A: positive for one of MT gyrA / MT parC — resistant to fluoroquinolone; positive for estT and negative for each of erm(42) / erm(47) / mphE / msrE — susceptible to tulathromycin / tilmicosin / tildipirosin; negative for flor — susceptible to phenicol; positive for one of tet(H) / tet(A) / tet(T) — resistant to tetracycline.

[0086] In Fig. 2B: negative for MT gyrA / MT parC — susceptible to fluoroquinolone; positive for estT and positive for one of erm(42) / erm(47) / mphE / msrE — resistant to macrolideclass; positive for flor — resistant to phenicol; positive for one of tet(H) / tet(A) / tet(T) — resistant to tetracycline.

[0087] In Fig. 2C: positive for one of MT gyrA / MT parC — resistant to fluoroquinolone; negative for estT and negative for each of erm(42) / erm(47) / mphE / msrE — susceptible to macrolide class; positive for flor — resistant to phenicol; negative for each of tet(H) / tet(A) / tet(T) — susceptible to tetracycline.

[0088] In Fig. 2D: positive for one of MT gyrA / MT parC — resistant to fluoroquinolone; negative for estT and positive for one of erm(42) / erm(47) / mphE / msrE — resistant to macrolide class; negative for flor — susceptible to phenicol; positive for one of tet(H) / tet(A) / tet(T) — resistant to tetracycline.

[0089] In at least one embodiment, if the visual result is indicative of the presence of the genetic determinant, the method further comprises treating the subject for the targeted pathogen. As used herein, a “subject” is a mammal, preferably a bovine mammal, but it can also be a human or non-human animal (including, without limitation, a laboratory, an agricultural, a domestic, or a wild animal). Thus, the assays and methods described herein are applicable to both human and veterinary disease and applications. In certain embodiments, subjects that can be addressed using the methods hereof include subjects identified or selected as having or being at risk for having BRD. Such identification and / or selection can be made by clinical or diagnostic evaluation.Materials and MethodsIsolates

[0090] Isolates of Mh (n = 90) were obtained from culture collections from Purdue University Animal Disease Diagnostic Laboratory (IN; n = 23), Kansas State University Veterinary Diagnostic Laboratory (KS; n = 49), and Texas A& M University Veterinary Medical Diagnostic Laboratory (TX; n = 18). Isolates were originally collected from nasopharyngeal, deep lung swabs, or lung tissue. Sequence preparation was completed using an Illumina DNA Prep kit (Illumina, San Diego, USA) and sequenced with Illumina MiSeq (2 x 300 cycles). Additional Mh isolates (n = 24) sequenced by Owen et al., G3 Genes|Genomes|Genetics, 7(9), 3059-3071. (Owen et al.) (OA) were included in the current study, which is incorporated herein by reference in its entirety. Sequence reads from OA isolates were downloaded from theNational Center for Biotechnology Information Sequence Read Archive (NCBI SRA BioProject PRJNA306895).Genome Assembly

[0091] Raw sequences for all OA, IN, KS, and TX isolates were trimmed for quality using Trimmomatic (v 0.39) and sequence quality was assessed with fastQC (v 0.11.9). See Bolger et al Bioinformatics 30, 2114-2120; Andrews et al., Babraham Bioinformatics, August 1, 2019, both of which are incorporated herein by reference in their entireties. Quality filtered sequences were assembled using SPAdes (v 3.13.0) using default parameters for assembly with the exception of the “—careful" parameter to reduce the number of mismatches and short contigs. See Bankevich, et al. Journal of Computational Biology 19, 455-477; Nurk et al., 17th Annual International Conference, RECOMB 2013, Proceedings, Lecture Notes in Computer Science 158-170, both of which are incorporated herein by reference in their entireties.

[0092] Assembled reads were filtered to remove all contigs < 500bp, and assembly quality was assessed using Quast (v 3.2) with default parameters. See Gurevich et al., Bioinformatics 29, 1072-1075; Mikheenko et al., Bioinformatics 32, 1088-1090, both of which are incorporated herein by reference in their entireties.. Number of contigs, max contig, total length, N50, GC content, and coding sequences for IN, KS, TX, and OA isolates are listed in supplementary.Sequence Read and Assembly Quality

[0093] The quality of the sequence reads varied among isolates from different origins. OA isolates had the shortest length (average length 2,646,899 bp) and the lowest quality compared to the isolates sequenced at IN and KS (average length 2,655,093 bp). Following assembly, the quality (longest contig, N50) of OA isolate assemblies was low compared to the IN and KS isolate assemblies. See Owens et al.. The average max contig size was 157,647 bp for OA while average N50 was 64,906 bp. Meanwhile all other isolates had an average max contig size of 296,157 bp and average N50 of 91,346. Notably, isolate MH084 (IN) had a genome twice the expected size (4.79 Mb). As with all isolates, matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS) identified the isolate a Mh. It is possible that this isolate was a mixed culture of Mh with another isolate. However, MH084 was retained in the analysis due to the classification of it sMh.Antibiotic Susceptibility Testing (AST) and ARG Annotation

[0094] The antibiotics included in this study were limited to those commonly used to treat BRD: danofloxacin, enrofloxacin, florfenicol, tetracycline, tilmicosin, tulathromycin, and tildipirosin. Additionally, if ten or fewer isolates were resistant or sensitive to an antibiotic it was excluded from the analysis. Ceftiofur, for example, was excluded because there was only one phenotypically resistant isolate. Additionally, isolates with a diversity of phenotypic resistance profdes were included. The minimum inhibitory concentration (MIC) values for OA isolates, originally published in the paper by Owen et al., were determined using Sensititre Vet Bovine / Porcine AST plate BOPO6F (ThermoFisher; United Kingdom). IN isolates were assayed with AST plate BOPO7F while KS used either BOPO7F or BOPO6F. Isolates from TX were assayed for antibiotic susceptibility using Kirby-Bauer disk diffusion. Due to variability in the testing methods used by the diagnostic labs, not all tested antibiotics have data from the same number of isolates. For example, BOPO6F does not include assays for tildipirosin and tetracycline. Isolates with an MIC interpreted as intermediate were considered resistant in this study. This decision was made as it was decided that a false positive (GR / PS) was better than a false negative (GS / PR) when selecting an antibiotic for animal treatment.

[0095] ARGs were annotated using the Complete Antibiotic Resistance Database (CARD; v. 3.1.1) Resistance Gene Identifier (RGI; v. 5.2.0) with the following parameters:“—input type contig -d wgs —local —exclude _nudge —clean See Alcock et al., Nucleic Acids Res 48, D517-D525; McArthur et al., Antimicrobial Agents and Chemotherapy 57, 3348-3357. doi: 10.1128 / aac.00419-13, both of which are incorporated herein by reference in their entireties.

[0096] The “loose” matches were excluded, leaving only “strict” and “perfect” hits, to have the most confidence in the annotations from CARD. Concordance was calculated for each antibiotic by taking the sum of the isolates with matching genotype (G) and phenotype (P) of antibiotic susceptible (S) or resistant (R) isolates (GR+PR and GS+PS) and dividing by the total sum of isolates (i.e., GR / PR and GS / PR and GR / PS and GS / PS) (Table 3).

[0097] No ARGs for the fluoroquinolone antibiotics were identified with CARD-RGI in this study. However, a previous study by Woolums et al. had identified fluoroquinolone resistance mutations in genes gyrA and parC in Mh. The reference genes from NCBI, also utilized by the Woolums et al. study, gyrA (NCBI gene: AJE07574) and parC (NCBI gene:AJE08043), were used in the creation of a BLAST (v. 2.12.0) database using the following parameters:“makeblastdb -in Mh RefGenes.txt -dbtype prot -out MH _protein db See Woolums et al., Veterinary Microbiology 221, 143-152 (Woolums et al.), which is incorporated by reference in its entirety.

[0098] This database was created to search for the gyrA and parC genes in the genomes that were collected. From the BLAST results, multiple sequence alignments were created for each gene to determine the potential resistance mutations in Mh to fluoroquinolones. The same reference genes were utilized for alignment and mutation identification. Presence of any of the previously identified mutations S83F, S83Y, D87G and D87N in gyrA and E89K in parC were used to designate an isolate as genotypically resistant in the current study, even though these SNPs are not included in the CARD database. See Woolums et al. Other fluoroquinolone resistance SNPs are included in the CARD database (CARD-RGI; v. 3.1.1; v. 5.2.0) for gi rd and parC, but they were unable to be identified in the Mh isolates included in the current study because these CARD reference sequences are from other bacterial species.

[0099] It should be noted that the resistance gene estT was used to identify genotypic resistance to all macrolides, except tulathromycin. The original study that first identified estT, found that this gene only confers phenotypic resistance to 16-membered ring macrolides such as tilmicosin and tildipirosin. See Dhindwal et al., Proceedings of the National Academy of Sciences 120, e2219827120 (Dhindwal et al.), which is incorporated herein in its entirety. In the current study, it was determined after analysis of the training set that estT was not concordant with a resistant phenotype to tulathromycin. Therefore, it was decided to exclude estT from tulathromycin analysis. Overall, a total of eleven ARGs were utilized to identify genotypic resistance in this study (Table 6).Example 1 — Antibiotic Susceptibility Phenotypes

[0100] While 39% of the isolates (n=44) exhibited susceptibility to all listed antibiotics, 37% of the isolates (n=42) exhibited phenotypic resistance to more than 3 of the antibiotics tested in this study (Table 1). Interestingly, the most common resistance profile was resistance to all seven BRD antibiotics, with 20 of the 114 isolates identified with this profile. MIC values and interpretations for individual IN, KS, TX, OA isolates are listed in Tables 6. Specifically, 39% of isolates were resistant to tulathromycin, while only 1% of isolates were resistant to ceftiofur.

[0101] In this study some antibiotic resistance profiles were more common than others. Isolates that were resistant to danofloxacin were always resistant to enrofloxacin as well (Table 1). Both antibiotics are part of the fluoroquinolone family, making co-resistance unsurprising. Resistance to tilmicosin and tetracycline were the most prevalent, with 61 and 45 isolates recorded as resistant, respectively. Tetracycline resistance was often found to occur with other resistant phenotypes, such as tilmicosin and fluoroquinolone antibiotics. Florfenicol resistance was recorded the least often, with only 35 isolates having a resistant phenotype. Of these 35 isolates, 30 also exhibited tetracycline resistance.Table 1: Phenotypic antibiotic resistance profile of M. haemolytica isolates (n = 114 total).ADDL Model Creation(n = 96) External Antibiotic Resistant Phenotype Validation KS, TX, and Owen et al. (n= pjj IN Isolates (2017)(n = 72) (n = 24)No Resistant Phenotypes 32 2 10 TET 6TILM 13DANO, ENRO 1FLOR, TULA 1 TILM, TULA 1DANO, ENRO, TILM 4FLOR, TILM, TULA 2DANO, ENRO, TILM, TULA 1 DANO, ENRO. FLOR, TET 1TET, TILDIP, TILM, TULA 1 DANO, ENRO, TET, TILM, TULA 4 2 DANO, ENRO, FLOR, TILM, TULA 2FLOR, TET, TILDIP, TILM, TULA 1DANO, ENRO, FLOR, TET, TILDIP, TILM 1 DANO. ENRO, FLOR, TET, TILM, TULA 7DANO, ENRO, TET, TILDIP, TILM, TULA 2DANO, ENRO, FLOR, TET, TILDIP, TILM, TULA 17 3Total Resistant to One or More Antibiotics 49 22 8Example 2: Annotated Antibiotic Resistance Genes

[0102] In addition to differing numbers of observed phenotypically antibioticresistant isolates, there were varying antibiotic resistance genes found in the isolates (Table 2). Atotal of 78 ARGs and SNPs were identified. Forty-six isolates (40%) had no resistance genes, but 39 isolates (34%) encoded more than 3 antibiotic resistance genes. Similar to the phenotypic drug resistance profiles, the most common ARG profile included 7 ARGs for the antibiotics included in this study (21 / 114 isolates). Two macrolide resistance genes, mphE and msrE, were always found together in the same isolate (35 isolates); however, neither mphE nor msrE was observed in any of the OA isolates. The isolates from each origin (KS, OA, IN, TX) did share ARGs. The disinfectant resistance gene qacG (98%) and tetracycline resistance gene tetH (58%) were the most prevalent ARGs, and sul2 and aminoglycoside resistance genes aph(3’)-Ia, aph(6)-Id, aph(3”)-Ib were found in nearly 50% of the isolates. While a few other genes were found in about 30% of the isolates, more than 40 of the 78 total ARGs were found in less than 5% of the isolates.Table 2: Genotypic antibiotic resistance profile of M. haemolytica isolates (n = 114 total).ADDL Model Creation(n = 96) External Antibiotic Resistant Genotype, Validation KS, IX, and Owen et al.IN Isolates (2017)1’ (n = 72) (n = 24)No Resistant Genotypes 35 10 gyrA 1 1 tet(H) 4 2 floR, tet(A) 1 gyrA. tet(H) 4 estT, tet(H) 12 gyrA, estT, tet(H) 1 mphE, msrE, tet(H) 1 erm(42), floR, tet(H) 3 gyrA, erm(42), floR, tet(H) 2 gyrA, parC, erm(47) tet(H), tet(T) 1 gyrA, parC, mphE, msrE, tet(H) 3 1 gyrA, parC, erm(42). floR, tet(H) 1 gyrA, parC, erm(42), estT, tet(H) 1 gyrA, erm(42). mphE, msrE, tet(H) 1 gyrA. parC, mphE, msrE, floR, tet(H) 1 gyrA, parC, estT, mphE, msrE, tet(H) 3 1 gyrA, parC, erm(42), estT, mphE, msrE 1 gyrA. parC, erm(42), mphE, msrE, tet(H) 1 gyrA, parC, erm(42), mphE, msrE, floR, tet(H) 12gyrA, parC, erm(42), estT, mphE, msrE, tet(H) 2 gyrA, parC, erm(42), estT, mphE, msrE, floR, tet(H) 6 2Total With One or More ARGs 37 24 8

[0103] A subset of antibiotic resistance genes identified in the current study confer resistance to antibiotics used in the treatment of BRD - emrR, emrA, emrB, gyrA, and parC (fluoroquinolone); erm(42), erm(47), estT, mphE, and msrE (macrolide-lincosamide-streptogramin; MLS); floR and Ccol ACT CHL (phenicol); and tetA, tetH, tetT, tet38, mepA, and mepR (tetracycline). Many of these resistance genes only appeared in one isolate and did not correspond to phenotypic resistance. The final list of ARGs that were used to determine genotypic resistance are given in Table 3.Table 3: Antibiotic resistance genes used to identify genotypic resistance. Minimum inhibitory concentrations (MICs) used to identify phenotypic resistance.MIC Breakpoints ig / ml)* Antibiotic Class Antibiotic Resistance GenesSusceptible Resistant Danofloxacin < 0.25 > 1 Fluoroquinolone - gyM, parC - Enrofloxacin < 0.25 > 2 Tilmicosin < 8 > 32 Macrolide TulathromycineStT**mSrE>m^E-erm(42)’- - - erm(47) - ITG Tildipirosin <4 > 16 Phenicol Florfenicol floR <2 > 8 Tetracycline Tetracycline tet(H), tet(A), tet(T) <2 > 8 *From Clinical and Laboratory Standards Institute

[0048] for Mh isolated from cattle. Isolates with a minimum inhibitory concentration (MIC) between the susceptible and resistant breakpoints, typically identified as intennediate, were classified as resistant in this study.**The gene estT was only utilized for identifying tilmicosin and tildipirosin resistance.Example 3: Concordance between Resistance Phenotypes and Genotypes

[0104] A high degree of agreement was observed between ARGs and phenotypic resistance for all antibiotics. Concordance between ARGs and phenotypic resistance was 95% or greater for danofloxacin, enrofloxacin, tildipirosin, tetracycline, and tulathromycin while for florfenicol and tilmicosin it was 90% and 91% respectively (Table 4).Table 4: Antibiotic Resistance Gene Classification Metrics. P: Phenotype; G: Genotype; R:Resistant; S: SusceptibleP: R P: SAntibiotic Precision Sensitivity Specificity Fl Concordance Discordance G: R G: S G: R G: SDANO 33 2 2 53 0.94 0.94 0.96 0.94 0.96 0.04 ENRO 37 2 2 55 0.95 0.95 0.96 0.95 0.96 0.04 FLOR 25 6 4 63 0.86 0.81 0.94 0.83 0.90 0.10 TET 32 2 0 32 1.00 1.00 1.00 1.00 1.00 0 TILDIP 19 1 1 28 0.95 0.95 0.97 0.95 0.96 0.04 FILM 47 6 3 40 0.94 0.89 0.93 0.91 0.91 0.09 TULA 24 1 1 49 0.96 0.96 0.98 0.96 0.97 0.03

[0105] Of the seven tested antibiotics, ARGs were the best at predicting phenotypic resistance when compared to machine learning models (Table 5).Table 5: Comparison of phenotypic resistance to Fl -scores and concordance between resistance database query (ARG) or machine learning (ML) for each tested antibiotic.ARG Machine LearningAntibioticFl Concordance Fl ConcordanceDANO 0.95 0.96 0.80 0.88ENRO 0.95 0.96 0.90 0.94FLOR 0.83 0.90 0.67 0.88TET 0.97 0.97 0.87 0.92TILDIP 0.94 0.95 0.75 0.88TILM 0.91 0.91 0.86 0.81TULA 0.96 0.97 0.78 0.84

[0106] While the concordance between machine learning models and phenotype AST was greater than 80% for all tested antibiotics, the precision was less than 80% for 5 out of 7 antibiotics (Table 6). Concordance between ML models and phenotypic resistance was higher than 90% for two antibiotics, enrofloxacin (94%) and tetracycline (92%).Table 6: Machine learning concordance. Machine learning classification metrics from KO VER output. P:phenotype; G: genotype; R: resistant; S: susceptibleP: R P: SAntibiotic Precision Sensitivity Specificity Fl Concordance Discordance G: R G: S G: R G: SDANO 8 1 3 20 0.73 0.89 0.87 0.80 0.875 0.125ENRO 9 0 2 21 0.82 1.00 0.91 0.90 0.94 0.06 FLOR 4 1 3 24 0.57 0.8 0.89 0.67 0.88 0.12 TET 7 2 0 15 1.00 0.78 1.00 0.87 0.92 0.08 TILDIP 3 1 1 11 0.75 0.75 0.92 0.75 0.88 0.12 TILM 19 0 6 6 0.76 1.00 0.50 0.86 0.81 0.19TULA 7 0 4 14 0.64 1.00 0.78 0.78 0.84 0.16Example 4: Antibiotic Resistance Prediction Using Machine LearningAssembled genomes (n = 96) were used to create a k-mer table (k=31; presence or absence of k- mers in each genome). K-mer length 31 was used as recommended by KO VER (v 2.0.0). See Drouin et al. BMC Genomics 17, 754; 2 (Drouin et al.), which is incorporated herein in its entirety.

[0107] Suffixerator and Tallymer, both implemented through GenomeTools (v 1.5.9), were used to create and count k-mers, respectively See Kurtz et al., BMC Genomics 9, 517; Gremme et al., IEEE / ACM Trans. Comput. Biol, and Bioinf. 10, 645-656, both of which are incorporated herein in their entireties. A metadata file was created for each antibiotic that contained assembly names and whether isolates were phenotypically resistant (indicated by “1”) or susceptible (indicated by “0”) to a particular antibiotic. To create a model of antibiotic resistance prediction, more than 10 isolates in both the resistant and susceptible groups were required. See Drouin et al. The k-mer table and metadata were used to create a dataset to predict resistance in KOVER with the Set Covering Machine algorithm. See Drouin et al; see also Marchand, et al., The Set Covering Machine. Journal of Machine Learning Research 3 (2002); Drouin et al., Sci Rep 9, 4071 (Drouin et al. II), both of which are incorporated herein in their entireties.. Briefly, the dataset was split into training and testing groups (66 and 33% of the isolates, respectively). A model was developed for each antibiotic using the training data and validated with the testing data. To control for the small dataset, 5-fold cross-validation was employed. For each antibiotic model, the classification error, specificity, sensitivity, and precision were determined for both the training and testing groups (Table 6). Additionally, rules were assigned an importance based on how often the rule was accurate in predicting resistance and equivalent rules (ER) - the set of k-mers considered equally important - were also listed (Table 7). See Drouin et al; Drouin et al 11.Table 7: KO VER model for breakpoint MIC. Model type: presence (P) or absence (A) of the ruleset predicts resistance: Importance: weighted value for rule - designates how often the rule was found in themodel; For multiple rulesets - Conjunction: all rulesets predict resistance (logical AND) or Disjunction: one ruleset needs to be present or absent to predict resistance (logical OR).., Model type Equivalent.Antibiotic x i Annotation(importance) rules countP (0.69) 7 Sequence associated with tetRp m 1Sequence upstream of toxin-antitoxin genes n ■ r ■\ higA / higBDanofloxacin Disjunction o n(Q 7) 35 Sequence upstream of IS481 transposase P (0.48) 31 Section of DNA topoisomerase IVP (0.70) 7 Sequence associated with tetRP (048) 31 Sequence upstream of toxin-antitoxin genes Enrofloxacin Disjunction ' ' _ GgA hiitt __1..1„ Five regions associated with virulence and survivalT,,r, ^..,. P (0.76) 7 Sequence associated with tetR I'lUll lil ul DiS | dll Lltjli r\ r\ i r\ n • i • i TV P(0.45) 55 Sequence associated with tetRP to Q6t 1 ^UP''cated sequence between hypothetical protein,. ‘ • ) CDS and exodeoxyribonuclease I Ictiacyclinc ConjunctionJMultiple copies of IS Sod 13 / IS 1595 transposase.. ^.. P (0.58) 251 Five regions with AR and MGE sequences Tildipiivsm Dujuttcltonp (0 50) 3|Hypothetical protein CDSA 1,36 Three regions associated with hypothetical (0.54) 8 proteins and a heme utilization protein.. A. - Two duplicate regions associated with host lilmicosin Conjunction 15 °x.(0.43) specificity protein JTAm? n41 Four regions associated with survival (U. Z 1 ) P (0.76) 7 Sequence associated with tetR ATulathromyci £)isjunction(038) * Unspecific sequence matchesm23 Upstream and start of Laccase(O. Jo)Model Alignment to Genomes

[0108] To further characterize the output of KO VER model analysis (modified from

[0030] ) the antibiotic resistance model rules, including ERs, were first aligned by BLAST against genomes from the family Pasteur ellaceae (NCBI:txid712). See Altschul et al., Journal of Molecular Biology 215, 403-410; Johnson et al., Nucleic Acids Research 36, W5-W9, both of which are incorporated herein in their entireties. However, alignments of all ERs to NCBI reference genomes were not always high confidence. The best alignments (as determined through e-value, length, and percent coverage of query to subject) to the reference sequenceswere considered reference alignments. Tn a case where all e-values were higher than 1 O'50and / or coverage was less than 100%, the best alignment possible was considered.

[0109] Second, the reference alignments were downloaded from NCBI as GenBank fdes so the ERs could be aligned to the reference sequence using UGENE (v 40.1). See Okonechnikov et al., Bioinformatics 28, 1166-1167, which is incorporated herein in its entirety. The UGENE function “Find Pattern” was used to exclusively consider perfect matches between the model rule sets and the reference alignment(s). Through this process ERs were assembled into contigs that could then be functionally annotated.Annotation and Target Selection

[0110] Isolate genome assemblies from the current study were annotated using Rapid Annotation and Subsystem Technology (RAST; v. 2.0). See Aziz et al., BMC Genomics 9, 75; Overbeek et al., Nucleic Acids Res 42, D206-214; Brettin et al., Sci Rep 5, 8365, each of which is incorporated herein in its entirety. Using assembled ERs from UGENE, a BLAST search was performed within RAST, with the assembled ERs as the query and the isolate genomes as the subject. For each model, if the model type was “presence”, either conjunction or disjunction, the genomes considered to be in the resistant group were the subject. If the model type was “absence”, either conjunction or disjunction, the genomes considered to be sensitive were the subject. Resulting output was searched not only to determine the sequence location within the genomes, but to examine the surrounding areas on the contigs for genes of interest.Machine Learning Model Alignment and Annotation

[0111] Each ML model created for the seven tested antibiotics resulted in multiple rules (Table 7). The rules for danofloxacin, enrofloxacin, florfenicol, tildipirosin, and tulathromycin (Table 7) were part of disjunctive models (i.e., the presence / absence of any one rule must be satisfied) while tetracycline and tilmicosin rules were conjunctive (i.e., all rules must be satisfied). The importance value calculated for each rule showed the proportion of predictions that included the rule. For example, the importance of the first rule for danofloxacin was 0.69, while the importance of the third rule was only 0.17, suggesting the first rule accurately predicted danofloxacin resistance in more cases than the third rule. Each ML model had more than one rule for every antibiotic, and danofloxacin had the most rules, four. Finally, equivalent rules could often be assembled into larger contigs.

[0112] While the ML models for the seven antibiotics were not as accurate as ARGs, the ML models were still analyzed to determine genetic context for the rules and to better understand the KO VER software predictions. A few case examples of ML models are described below.

[0113] First, some rules may be markers of multi-drug resistance, as the same rule was found for antibiotics of different classes. Four antibiotics shared the same first rule -danofloxacin, enrofloxacin, florfenicol, and tulathromycin (Table 7). The assembled rule was 37 bases long (from 7 ER) and matched a small, non-coding sequence found upstream of the tetracycline repressor gene tetR (Table 7). Out of all training isolates, the majority of isolates with resistance to any of these four antibiotics (n = 42) were resistant to at least three (n = 33) or all four antibiotics (n = 24). Thus, finding the same rule for multiple antibiotics may also be due to many of the isolates having the similar resistance profile.

[0114] Second, many of the rules identified were mobile genetic elements or non-coding sequences in close proximity to MGEs. Tetracycline rule 2 was split into two regions, both associated with transposases; one region was 684 bases long and matched ISSodl3, while the other was 436 bases long and matched IS 1595. Danofloxacin rule 3 was 81 bases long and matched a non-coding sequence (nCDS) just upstream of an IS481 -like transposase and was surrounded by MGE genes - integrases, plasmid stabilization proteins, and unclassified mobile genetic elements. While the sequences are likely not resistance genes themselves, they may be associated as a marker gene of resistant isolates or with another resistance gene in the genome as MGEs often encode a variety of ARGs.

[0115] Third, many of the ML learning identified marker sequences were in close proximity to tetR or tetH. As mentioned previously, rule 1 for danofloxacin, enrofloxacin, florfenicol, and tulathromycin were all the same 37 bp sequence near tetR. The second florfenicol rule was divided into three regions, one 49 bases long and matching a section of the tetH gene and two different regions upstream of tetR that were 54 and 61 bases long (Table 7). Tildipirosin rule 1 was split into five regions, all of which were found within the same section of one contig. Regions 2 and 3 were different sequences associated with tetR and tetH (i.e., between the two genes, upstream of tetR, or downstream of tetH).External Validation

[0116] Eighteen isolates (14 KS, 2 TX, 2 IN) not used in creation of the ML model were used for external validation of the models. The isolates represent -15% of the total number ofisolates used in the study. Validation was performed by predicting the resistance profile of the external validation isolates using the best predictor (ARGs; Table 7) for each antibiotic as the query in a BLAST search. The predictions were then compared to the phenotype AST results (Table 8).Table 8: External Validation Testing.Prediction Model Antibiotic Correct Predictions Prediction Method Validation Error Training Error DANO 18 / 18 ARG 0% 4% ENRO 18 / 18 ARG 0% 4% FLOR 16 / 18 ARG 11% 10% TET 17 / 18 ARG 5.5% 3% TILDIP 8 / 9* ARG 11% 5% TILM 17 / 18 ARG 5.5% 9% TULA 17 / 18 ARG 5.5% 3%Performance of Models on Validation Data

[0117] The performance of each antibiotic resistance prediction model was evaluated using an external set of isolates (Table 8) and utilized the most accurate prediction method, ARGs, for each antibiotic. Overall, the seven antibiotic models predicted resistance correctly for -89% of the validation isolates. In the case of tildipirosin, eight out of the eighteen predictions could not be evaluated due to lack of reference AST data. Among the remaining ten samples with reference AST data, tildipirosin resistance was correctly predicted in nine isolates. Notably, danofloxacin, enrofloxacin, and tilmicosin demonstrated the highest performance, all achieving perfect accuracy in resistance detection. The difference between the concordance of the training / testing dataset and the validation datasets were all 6% (Table 8).Data Availability

[0118] Data from Owen et al. is available through their published work. See Owen et al. Sequence reads and assembled genomes from IN, KS, and TX isolates can be found at NCBI SRA and Genome Repository, respectively (BioProject PRJNA1115110). Bioinformatics scripts and input files for KOVER and CARD are available at GitHub (github.com / CWickware / CWickware Purdue_ AnSci / tree / main / Bovine-Respiratory -Disease).

[0119] In the current study, antibiotic resistance genes, identified from bacterial genome assemblies, were compared to phenotypic AST data. Presently, two methods of detecting antibiotic resistance are commonly used in veterinary laboratories: 1) culture-based antibiotic susceptibility testing (AST), or 2) molecular assays to determine presence of antibiotic resistance genes (ARGs). In the case of BRD, however, these two methods have distinct issues that make their use impractical for accurate identification of resistance. First, bacterial culture and AST can be slow, requiring days for fast-growing species such as E. coli or Salmonella spp. or weeks for slow-growing species such as Mycobacterium bovis See Neidhardt, F. C., 1996. Escherichia Coli and Salmonella: Cellular and Molecular Biology. ASM Press; Beste et. al, PLOS ONE 4, e5349, both of which are incorporated herein in their entireties. Second, ARGs must be discovered and catalogued, thus determination of antibiotic resistance genes is dependent on previous determination of gene function. High genotype-phenotype concordance has been observed in well-studied organisms with assembled genomes from many isolates of the species. For instance, a study on the concordance of ARGs and phenotypic AR of non-typhoidal Salmonella enterica found 97.8% of approximately 3,400 isolates had AR genotype and phenotype that agreed. See Neuert et al., Front Microbiol 9, 592, which is incorporated herein in its entirety In contrast, when studying 64 isolates from three pathogens of the family Pasteurellaceae associated with BRD, Owen et al. found less than 75% concordance between AR genotype and phenotype. See Owen et al. In the case of BRD pathogens, there are additional genome sequences and assemblies that are publicly available; however, these genomes lack accompanying phenotypic antibiotic resistance information which limits ability to determine concordance with a larger sample size.Comparison to Owen et al. Original Results and Concordance

[0120] The original Owen et al. study reported an overall concordance of 72.7% for all antibiotics and bacteria tested, which is much lower than the overall ARG concordance rate of 94% for the current study. However, in the study by Owen et al., the overall low rate of concordance for isolates of the three Pasteurellaceae analyzed together may be confounded by the low rate of concordance for P. multocida and H. somni. The difference in phenotypic-genotypic agreement becomes much smaller when only comparing Mh isolates and the antibiotics shared between the two studies, with 90.8% concordance for Owen et al., and 93.5% for the current study. Additionally, compared to Owen et al., the current study employed over100 Mh isolates, which effectively increased the sensitivity of the models for identifying additional kinds of genetic combinations not observed in Owen et al..

[0121] The current study identified ARGs mphE, msrE, erm(42), and estT as strong indicators of phenotypic resistance to MLS antibiotics either individually or in combination. Often mphE, msrE, and erm(42) were found together (n=14 / 114 isolates). The co-occurrence of these genes has been observed previously in other Mh studies, including in an ICE or on the same contig. See Clawson et al., BMC Genomics 17, 982 (Clawson et al.); Snyder et al., Veterinary Microbiology 235, 110-117; Deschner et al., Applied and Environmental Microbiology 90, e00502-24, each of which is incorporated herein in its entirety. In the OA isolates, erm(42), ermF and estT were the only MLS resistance genes identified. See Owen et al. The estT ARG is a macrolide esterase that was first reported in 2023, and was identified in many bacterial hosts, including Mh, and therefore was unknown at the time of the Owen et al. study. See Owen et al; Dhindwal et al. Inclusion of estT improved tilmicosin concordance in the Owen et al. from 25% to 71%. The SNPs for gyrA and parC conferring resistance to fluoroquinolone antibiotics were not yet part of CARD for Mannheimia haemolytica at the time of this study. To the authors’ knowledge these two resistance conferring SNPs in Mh were first identified in Katsuda et al. and were also observed in Woolums et al.. See Katsuda et al. Veterinary Microbiology 139, 74-79, which is incorporated herein in its entirety. In the study by Owen et al., the authors utilized CARD and acknowledged these genes, but could not identify the SNPs in their genomes resulting in predicting all six danofloxacin resistance isolates as sensitive. In the current study, however, the presence of these SNPs was identified, by conducting a BLAST search, in the OA isolates as well as the newly collected isolates for this study, resulting is correctly predicting resistance in nearly 40 resistant isolates about 95% of the time for both danofloxacin and enrofloxacin.

[0122] Another difference between the Owen et al. study and the current study is that the Owen et al. study classified isolates with an intermediate MIC interpretation as susceptible, while the choice was made to classify intermediate isolates as resistant in the current study. Intermediate, as a classification of microbial susceptibility to antibiotics, implies that the drug may not reach sufficient physiological concentration to efficiently kill the infection. See Rodloff et al., Dtsch Arztebl Int 105, 657-662, which is incorporated herein in its entirety. Intermediates were considered as resistant as a precautionary choice.ARGs as Indicators of Phenotypic Resistance in Mannheimia haemolytica

[0123] When identified resistance genes were examined in the Mh genomes, some resistance genes were predictive of resistance, while others were not and perhaps should not be considered as resistance genes in Mh. For florfenicol resistance, floR was highly concordant for resistance, but Ccol_ACT_CHL, which was only found in 4 isolates, was never concordant with resistance. Ccol ACT CHL is a chloramphenicol acetyltransferase first identified in Campylobacter coli, so it appears Ccol ACT CHL is not a determinant of florfenicol resistance in Mh. For fluoroquinolone resistance, emrR, emrA, and emrB were found in only one isolate susceptible to danofloxacin and enrofloxacin, so again perhaps these genes are not determinants of fluoroquinolone resistance in Mh. For tilmicosin, it seems that the macrolide resistance genes msrE, mphE, estT, and erm(42) are good predictors (>90% accuracy) of phenotypic resistance (Table 3). These four genes can be found in different combinations with or without each other. Other genes are rare but were concordant for resistance. For MLS resistance, erm(47) was only found in isolate MH074, and it was concordant for resistance to tildipirosin, tilmicosin, and tulathromycin. For tetracycline resistance, tetA was only found in MH084 and tetT was only found in MH074, both of which were resistant to tetracycline. More isolates with low-occurring genes need to be isolated and tested to be able to understand if the genes are consistently reliable indicators of resistance in Mh. In the absence of more data, the genes listed in Table 6 are the best identified resistance genes for antibiotics used to treat BRD.Previously Identified Resistance Phenotypes in Mh

[0124] Phenotypic resistance to tulathromycin and spectinomycin has been documented in BRD-associated pathogens, including Mh, in previous studies. Althoughtrimethoprim / sulfamethoxazole is rarely used for the treatment of BRD, resistance has been observed in Mh previously with similar levels of phenotypic resistance to those found in the current study. Ceftiofur, a third-generation cephalosporin, has been employed in the treatment of BRD due to its broad-spectrum bactericidal action and low incidence of resistance in respiratory pathogens. Similarly, few ceftiofur resistant isolates were identified in the current study, thus ceftiofur was not utilized for ARG or ML analysis. Fluoroquinolone resistance has previously been documented in Japanese studies. Past studies have noted resistant isolates were all categorized as serotype 6, however in the current study antibiotic resistant isolates were categorized as both serotype 1 and 6. A similar North American study utilized the same referencegenes as the current study and observed similar resistance profiles. Interestingly, The study observed florfenicol resistance in >60% of Mh isolated from cattle identified with BRD, and almost half of the cattle treated with florfenicol had to receive a secondary treatment with ceftiofur.Previously Identified Resistance Genes in Mh

[0125] Many of the resistance genes identified in the current study, such as those conferring resistance to MLS antibiotics, had previously been found in another study by Clawson et al. involving North American cattle. MLS resistance genes erm(42), msrE, and mphE were found in 37 of 312 lung collected isolates

[0010] , Both Clawson et al. and the current study found the ARGs msrE and mphE were always found together (Table 2). In contrast, erm(42) and estT were found either alone or in combination with mphE, and msrE, contributing to a variety of antibiotic resistance gene profiles. The study by Clawson et al. also identified the florfenicol resistance gene floR, which was also seen to occur in 28 of the isolates collected for the current study. The tetracycline resistance gene tetH was found in 42 of the isolates collected in the current study and in all (n=26) of the Owen et al. isolates. Clawson et al. found tetH in over 300 of their isolates, indicating its ubiquitous nature within Mh isolates.

[0126] Fluoroquinolone resistance SNPs in gyrA and parC, identified in the current study, were previously observed in cattle from both North America and Japan. Single nucleotide polymorphisms (SNP) in gyrA - S83F, S83 Y, D87N, and D87G - have been associated with fluoroquinolone resistance in Mh, and all were used to indicate genotypic resistance in this study. Fewer fluoroquinolone resistance SNPs have been observed for parC, E89K and S85I.Interestingly the most common resistant genotype was the combination of S83F, D87N (gyrA) and E89K (parC), and the second most common resistant genotype was S89Y (gyrA). There was only one instance of S85I (parC) and one instance of D87G (gyrA) which appeared in isolate MH012. A different isolate, MH082, had two different parC substitutions in the same locations as resistance-conferring SNPs, S85A and E89G, however this isolate lacked any gyrA resistance mutations, so it was classified as genotypically susceptible, which was concordant with phenotypic susceptibility.Considerations for the Use of Non-coding Sequences as Antibiotic Resistance Predictors

[0127] When identifying the rules of the ML output, non-coding sequences were identified as predictive of antibiotic resistance. Tulathromycin model rule 1 matched a non-coding sequence, and it is currently unclear how non-protein-coding sequences would confer an antibiotic resistance phenotype. Four hypotheses were considered: the model rule sequences are (i) marker genes of resistant bacteria, (ii) epigenetic controls, such as methylation and acetylation, resulting in a “dormant” survival phenotype, (iii) regulatory sequences or binding sites (e.g., sigma factors) facilitating stress-response mechanisms during exposure to antibiotics, or (iv) evidence of the impact of ICE, and more broadly MGE, on phenotypic antibiotic resistance.

[0128] Bovine respiratory disease poses a significant threat to both dairy and beef cattle producers, underscoring the importance of studying its causative agents. Through the current study, resistance genes and SNPs that enhance the prediction of antibiotic resistance were considered, particularly for antibiotics commonly employed in BRD treatment in the United States. In the future, targeting these known resistance genes would be the most reliable method to predict phenotypic antibiotic resistance for all seven antibiotics studied. This study highlighted the need to incorporate all available knowledge of resistance determinants for under-studied bacterial species; however, this study also demonstrated a higher genotype-phenotype concordance compared to previous research efforts. Leveraging ARGs to assess resistance in a rapid diagnostic assay could greatly assist producers and veterinarians in devising effective BRD treatment strategies and warrants further exploration.LAMP Primers

[0129] LAMP primer sets may be generated using Primer Explorer V5 (http: / / primerexplorer.jp / lampv5e / index.html). Primer sets may be of any appropriate length in base pairs. As an example, LAMP primers that span <200 bp of a target gene sequence, may have 18-21 bp loop primers. LAMP primers may include forward inner primers (FIP), F3 primers, backward inner primers (BIP), and B3 Primers, on any combination thereof. In some embodiments, LAMP primers for screening may have dG values of no more than -4.0 kcal / mol for the 3 ' end of F2 and the 5 ' end of F 1 c of FIP and the 3 ' end of B2, and the 5 ' end of B 1 c fpr BIP. For each gene target, a total of three different LAMP primer sets may be designed.LAMP Assay

[0130] LAMP reactions may be conducted by following manufacturer instructions of the Warmstart® LAMP Kit (DNA & RNA) (E1700L New England Biolabs, Ipswich, MA). 25 pL reactions were comprised of 12.5 pL of WarmStart® LAMP 2x Master Mix (40 mM Tris-HCl,20 mM (NH4)2SO4, 100 mM KCl, 16 mM MgSO4, 2.8 mM dNTPs, 0.28 pM dUTP, 0.64 U / pL Warmstart Bst 2.0 DNA polymerase, 0.6 U / pL Warmstart Reverse Transcriptase [RTx], 4x10-4 U / pL Antarctic Thermolabile UDG, 0.2% Tween 20, pH 8.8 @ 25 °C), 2.5 pL of a lOx LAMP primer mixture (2 uM F3, 2uM B3, 4 uM LF, 4 uM LB, 16 uM FIP, 16 uM BIP) 5 pL of a 1: 101 dilution of the included LAMP fluorescent dye, and 5 pL of the template DNA containing solution.

[0131] Antarctic Thermolabile UDG and dUTP may be added to the LAMP reaction mixture for detection limit and complex cross-reactivity studies to minimize carryover contamination during assay preparation. In-house validation experiments confirmed that the addition of UDG / UTP did not affect reaction performance. Unless specified, the final concentration of template DNA for qLAMP reactions may be 1 ng / reaction.

[0132] Reactions may be pipetted into wells of white 96-well full-skirted PCR plates (AB- 0800W Thermo Fisher Scientific. Waltham, MA). Wells may be sealed with VersiCap Mat Cap Strips (AB 1820 Thermo Fisher Scientific, Waltham, MA) and may be inserted into either a CFX96 Touch Real Time PCR Detection System (Bio Rad) or a qTOWER3G (Analytik Jena, Jena, Germany) for real-time fluorescent measurement. Reaction plates may be incubated at 65 °C for 1 hour with fluorescent scans taken using the FAM / SYBR Green I filter every minute. A ramp rate of 6 °C / s and 8 °C / s may be used on the CFX96 and qTOWER3G respectively. A ramp rate of 0.1 °C / may be used on the qTOWER3G for detection limit and complex reactivity experiments to improve the overall detection limit of the LAMP reactions.

[0133] Paper based LAMP assemblies may also be used constant herewith. The paperbased LAMP assembly may have dimensions of about 24 x 54 mm. The paper- based LAMP assembly may comprise: (i) a reading layer, (ii) 2 reaction strips, and (iii) a spreading layer. The reading layer may include a transparent 3 millimeter Melinex® backer for support. The Melinex® may be attached a reaction surface, which may be each formed of one or more strips of 5 mm x 20 mm chromatography paper (e.g., Whatman® Grade 1 chromatography paper) contacted by a double-sided adhesive (ArClean® 90178). Multiple test strips may be separated by 2.5 x 20 mm 10-millimeter polystyrene spacers to prevent cross-talk between the two test strips. The spreading layer may comprise a polyester sulfone mesh (Saaticare® PES 105 / 52). Multiplex qPCR

[0134] Multiplex qPCR reactions may be performed, e.g., on a 7500 Fast Real Time PCR system (Applied Biosystems, Bleiswijk, The Netherlands) using the QuantiFast Multiplex Kit RT-PCR kit (Qiagen, Venlo, The Netherlands). The PCR assay may be run in a 20 pL reaction mix containing 5 pL of the nucleic acid sample, 250 nM of each primer, 100 nM of each MGB probe, 1 x QuantiFast Multiplex RT-PCR Master Mix (with ROX dye) and sterile deionized water. An initial denaturation / activation for 60 s at 95 °C may be followed by 50 cycles of 10 s at 95 °C and 30 s at 60 °C.

Claims

WHAT TS CLAIMED IS:

1. A method for identifying a genetic determinant of phenotypic resistance in a sample, the method comprising:providing a panel of LAMP primer sets each set targeting a DNA fragment of a genetic determinant of phenotypic resistance to an antibiotic in Mannheimia haemolytica (Mh); obtaining a sample from a subject;combining the sample and the panel of LAMP primer sets into a mixture;heating the combination to initiate amplification of the targeted DNA fragment; and, detecting a visual result in the heated combination indicative of the presence or absence of the targeted determinant in the sample.

2. The method of claim 1, wherein the genetic determinant is one or more of mutant-type (MT) gyrA of SEQ. ID. NO. 1, MT parC of SEQ ID. NO. 3, estT of SEQ ID. NO. 5, mphE of SEQ ID. NO. 6, msrE of SEQ ID. NO. 7, erm(42) of SEQ ID. NO. 8, erm(47) of SEQ ID. NO. 9, floR of SEQ ID. NO. 10, tet(H) of SEQ ID. NO. 11, tet(A) of SEQ ID. NO. 12, and tet(T) of SEQ ID. NO. 13.

3. The method of claim 2, further comprising identifying an antibiotic to which Mh is susceptible based on the visual result indicative of the presence or absence of the targeted determinant, wherein(a) the detected absence of both MT gyrA (SEQ. ID. NO. 1) (and, optionally, the presence of WT gyrA (SEQ. ID. NO. 2) as a control) and MT parC (and, optionally, the presence of WT parC (SEQ. ID. NO. 4) is indicative of susceptibility to treatment with a quinolone, e.g., fluoroquinolone;(b) the detected absence of each of estT (SEQ ID. NO. 5), mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), erm(47) (SEQ. ID. NO. 9) is indicative of susceptibility to treatment with an antibiotic on the macrolide class; (c) the detected presence estT (SEQ ID. NO. 5) and one of mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), or erm(47) (SEQ. ID. NO. 9) is indicative of resistance to treatment with an antibiotic on the macrolide class;(d) the detected presence estT (SEQ ID. NO. 5) and one the absence of each mphE (SEQ. ID. NO. 6), msrE (SEQ. ID. NO. 7), erm(42) (SEQ. ID. NO. 8), and erm(47) (SEQ. ID. NO. 9) is indicative of susceptibility to treatment with the tulathromycin, tilmicosin, and / or tildipirosin;(e) the detected presence of flor (SEQ. ID. NO. 10) is indicative of resistance to treatment with phenicol;(f) the detected absence of flor (SEQ. ID. NO. 10) is indicative of susceptibility to treatment with phenicol;(g) the detected presence of one of tet(H) (SEQ ID. NO. 11), tet(A) (SEQ ID. NO. 12), and tet(T) (SEQ ID. NO. 13) is indicative of resistance to treatment with tetracycline (e g., oxytetracy cline, chlortetracycline);(h) the detected absence of each of tet(H) (SEQ ID. NO. 11), tet(A) (SEQ ID. NO. 12), and tet(T) (SEQ ID. NO. 13) is indicative of susceptibility to treatment with tetracycline (e.g., oxytetracy cline, chlortetracycline).

4. The method of claims 1, wherein the visual result is provided in 60 minutes or less of initiating the heating step and the sample is a bovine nasal swab.

5. The method of claim 4, wherein detecting a visual result further comprises one or more of: measuring a relative clarity of the heated combination using a turbidimeter; and analyzing colorimetric data in the visual result using one or more of a fluorescent reader, an ultraviolet light reader, or camera.

6. The method of claim 1, wherein each LAMP primer set is coupled with a colorimetric reagent that is pH sensitive or magnesium sensitive.

7. The method of claim 6, wherein the colorimetric agent for at least one LAMP primer set is phenol red.

8. A kit for identifying a genetic determinant of phenotypic resistance in a sample, the kit comprising:a panel of LAMP primer sets each set targeting a DNA fragment of a genetic determinant of phenotypic resistance to an antibiotic mMamiheimia haemolytica (Mh), wherein the genetic determinant is one or more of mutant-type (MT) gyrA of SEQ. ID. NO. 1, MT parC of SEQ ID. NO. 3, estT of SEQ ID. NO. 5, mphE of SEQ ID. NO. 6, msrE of SEQ ID. NO. 7, erm(42) of SEQ ID. NO. 8, erm(47) of SEQ ID. NO. 9, floR of SEQ ID. NO.10, tet(H) of SEQ ID. NO. 11, tet(A) of SEQ ID. NO. 12, and tet(T) of SEQ ID. NO. 13.

9. The kit of claim 8 further comprising a colorimetric reagent that is pH sensitive or magnesium sensitive

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