WO2025096569A1 - Methods and compositions for real-time antimicrobial resistance profiling - Google Patents

Abstract

It is important to determine the existence of antimicrobial resistance genes (AMR) in microorganisms of the microbiome. Doing so allows for targeted treatment of individuals possessing these microorganisms, and furthers the understanding of how they arise, and how they can be prevented. The present invention provides methods and compositions which aid in the detection of AMRs in the microbiome that can be used for treatment decision making in clinical settings.

Description

METHODS AND COMPOSITIONS FOR REAL-TIME ANTIMICROBIAL RESISTANCE PROFILING CROSS-REFERENCE TO RELATED APPLICATIONS This application claims benefit of U.S. Provisional Application No.63/594,120, filed October 30, 2023, and U.S. Provisional Application No.63/713,413, filed October 29, 2024, both of which are hereby incorporated herein by reference in their entirety. GOVERNMENT SUPPORT CLAUSE This invention was made with government support under Hatch Act Project Nos. PEN04646, PEN04731 and PEN04752 awarded by the United States Department of Agriculture. The Government has certain rights in the invention. BACKGROUND Antimicrobial resistance (AMR) is a global health threat that requires a systems approach for the surveillance of antimicrobial resistance genes (ARG) to improve the understanding of their transmission, evolution, and enrichment. The rapid detection and characterization of resistant bacteria is a major component of the CDC’s Solutions Initiative that invests in national infrastructure to detect, respond, contain, and prevent resistant infections across healthcare settings, food, and communities. Increasing the frequency of isolate testing is effective to identify outbreaks faster and improve health outcomes. Still, this approach is limited in its ability to capture metagenomic resistance trends reflected in resistomes (i.e., collections of all ARG in a sample). The most comprehensive characterization of resistomes can be achieved with shotgun metagenomics. However, shotgun metagenomics is prohibitively costly because it sequences DNA in an untargeted manner and requires great sequencing depths to achieve the desired sensitivity. The lack of an affordable and comprehensive resistome characterization method is hindering resistome research and AMR surveillance. For example, among studies investigating the effects of antibiotic consumption on the human gut microbiome, only a few studies conducted a comprehensive resistome characterization using the shotgun metagenomic approach (Dubinsky 2020; Forslund 2013; Willmann 2018; Li 2019; Carr 2020). A cost-effective and comprehensive resistome profiling method is therefore critically needed to accelerate resistome research and facilitate implementation of broad resistome surveillance. While the sole presence of a given ARG does not imply its expression and function, the global increase in resistome diversity and/or relative abundance in a sample can be used as an indicator of increased risk for phenotypic resistance. The output of resistome characterization efforts could therefore enable informed development of effective interventions to mitigate transmission and enrichment of ARG through the interconnected environment, animal, and human systems in a one health context. In addition to shotgun metagenomics mentioned above, the currently used techniques for surveillance and tracking of ARG mostly rely on culturing bacteria from samples. A subset of isolates is then tested for antimicrobial susceptibility using time-consuming phenotypic assays (e.g., standard broth microdilution). Advances in next-generation sequencing (NGS) allow for a broader whole-genome sequencing (WGS) of isolates in parallel to phenotypic testing in research and surveillance. Characterization of resistomes using culture independent methods can provide insight into the ARG status of a given sample without biases introduced by bacterial isolation. The state-of-the-art culture-independent resistome characterization methods rely on untargeted shotgun sequencing (Raghavendra 2018), which has advantages as well as limitations discussed above. Specifically, of the total genetic material in a given sample, less than 0.1% of the DNA is represented by ARG (Noyes 2017). Low relative abundance of ARG in the context of host genome therefore requires large and expensive sequencing depths to ensure the detection of ARG. The resistome characterization challenge therefore needs to be addressed. The existing enrichment-based methods for detection of ARG (bait-capture method Noyes 2017) and AmpliSeq (Urbaniak 2018)) have limitations. Noyes et al. developed an ARG enrichment method based on bait-capture. Their method increased the percentage of reads mapping to ARG in metagenomes from 0.1% to 11% (up to 1400 ARG detected), on average (Noyes 2017). However, this method is very costly. It also results in approximately 90% of the data being discarded as it does not map to ARG. Furthermore, the method application requires relatively high technical expertise. Another approach to enrichment is via target amplification. This approach is utilized by Illumina’s AmpliSeq coupled with Illumina’s Antimicrobial Resistance Panel that targets 478 ARG (Urbaniak 2018). AmpliSeq is more cost-effective compared to bait-capture and shotgun metagenomics however it is limited in the number of ARG that can be profiled. In addition to Illumina, Paragon Genomics developed a CleanPlex technology that could potentially be further developed for resistome characterization. CleanPlex technology allows for ultra-scalable and -sensitive NGS amplicon sequencing (Paragon Genomics). It utilizes a proprietary multiplex PCR primer design algorithm and a patented background cleaning chemistry that reduces redundant molecular barcodes from a template- dependent primer extension reaction (Liu 2018). CleanPlex has recently been applied to detect SARS-CoV-2 variants (Truong 2021; Alteri 2021; Shen 2021; Fernandez-Cadena 2021), however it is not available for resistome characterization. CleanPlex was not deemed feasible for resistome characterization in complex samples with thousands of different species. Like CleanPlex technology, a self-avoiding molecular recognition system (SAMRS) and novel hot- start polymerases have improved the specificity of multiplexed PCR reactions. However, these technologies have not yet been developed to a point where they could be applied to amplify thousands of targets, which is needed for comprehensive resistome characterization (Sharma 2014; Yang 2020). What is needed in the art are cost-effective and comprehensive methods to effectively implement ARG and pathogen surveillance. SUMMARY The present invention relates to a method of detecting two or more antimicrobial resistance (AMR) genes in a biological sample, the method comprising: (a) providing a reaction mixture comprising (i) two or more oligonucleotide primer pairs, wherein each primer pair is specific for an AMR gene target (the gene or a variant thereof), and further wherein each primer has a cleavage domain positioned 5' of a blocking group and 3' of a position of hybridization with a target nucleic acid, wherein the blocking group is linked at or near the end of the 3 '-end of the oligonucleotide primer, wherein the blocking group prevents primer extension and/or inhibits the primer from serving as a template for DNA synthesis, (ii) a biological sample comprising two or more target nucleic acids, (iii) a cleaving enzyme, and (iv) a polymerase; (b) exposing the two or more oligonucleotide primer pairs to the biological sample, wherein if one or more target nucleic acids are present in the sample, at least one primer and at least one target form a hybridized target/primer substrate; (c) cleaving the one or more primers of the hybridized primer/substrate of step b) with the cleaving enzyme at a point within or adjacent to the cleavage domain to remove the blocking group from the primer; (d) extending the primer with the polymerase, thereby obtaining amplified target nucleic acid; (e) barcoding each amplified target nucleic acid with a unique barcoded adapter to produce barcoded samples; and (f) sequencing the barcoded samples of step (e), thereby detecting AMR gene targets (genes and variants). Also disclosed are specific primers which can be used to amplify AMR genes, methods of treating a subject, and kits. Additional aspects and advantages of the disclosure will be set forth, in part, in the detailed description and any claims which follow, and in part will be derived from the detailed description or can be learned by practice of the various aspects of the disclosure. The advantages described below will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE FIGURES The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate certain examples of the present disclosure and together with the description, serve to explain, without limitation, the principles of the disclosure. Like numbers represent the same elements throughout the figures. FIG.1A-G shows rhPCR methodology. Each individual sample undergoes a 2-step PCR where a) custom rhPrimers anneal to target sequences, b) RNAse H2 recognizes RNA base and cleaves blocking moiety, allowing c) DNA polymerase to amplify target sequence. Amplicons from PCR undergo a second indexing PCR where d) uniquely barcoded primers identify samples before e) sequencing adapters are attached to final amplicon. Real-time sequencing f) is per- formed with a MinION device and g) analysis is performed with established pipelines. FIG.2 shows the feasibility of rhPCR-based antimicrobial resistance gene amplification (rhAMR amplification). Amplicons were sequenced using Illumina MiSeq with ~15,000 reads per sample. FIG.3A-D shows rhAMR results. Selected results from proof-of-concept experiments in (A) mock microbiome; (B) turkey cloacal swabs; and (C) human fecal samples. Limit of Detection (D) of genes specific to serial diluted spike-in sample. FIG.4 shows workflow of LOD analysis using a human fecal sample and S. sonnei. FIG.5 shows gene counts of each unique ARG present in S. sonnei at 1:100, 1:1,000, and 1:10,000 dilutions, but absent in the fecal sample resistome as determined by whole genome sequencing. FIG.6A-B shows correlation between log relative abundance of ARG counts and dilution factor of S. sonnei DNA inoculated into human fecal sample DNA. ARGs with counts ≥25 are shown in (A) and ARGs with counts <25 are shown in (B). FIG.7 shows regression coefficient (R²) distributions characterizing the correlation between ARGs and inoculated S. sonnei dilution. The R² distributions were significantly different for ARGs with counts ≥25 and ARGs with counts <25 (p = 0.0063, Wilcoxon test). Figure 8A-B shows Illumina-sequencing based experiments (A) and illumina versus nanopore comparison (B). In (A), the stepwise workflow includes 1) selecting antimicrobial resistance genes (ARGs) associated with AMR phenotypes from MEGARes v2.0 database, then 2) collecting samples from at least three sources (mock microbiome, human, turkey), then 3a) whole-genome sequencing of samples; 3b) serially diluting spike-ins of individual bacterial isolates that have unique ARGs to a microbial community without said gene; and 4) amplifying and analyzing for the presence of ARGs. Figure 9 shows a subset pool PCA (annotated). Figure 10 shows rhAMR workflow. Figure 11A-B shows compositional rhAMR vs. WGS mock microbiome. (A) is a principal coordinate analysis graphical representation, and (B) shows a bar comparison. Figure 12A-C shows composition rhAMR versus shotgun in human samples. (A) shows metagenome (all detected); (B) shows metagenome in rhAMR only; and (C) shows rhAMR. Figure 13A-C shows a composition rhAMR versus shotgun in turkey samples. (A) shows metagenome (all detected); (B) shows metagenome in rhAMR only; and (C) shows rhAMR. Figure 14 shows rhAMR paired with nanopore sequencing. Figure 15A-B shows individual primer performance under serial dilutions of gene targets. (A) shows specific genes and (B) shows antimicrobials. Figure 16 shows EC-K12 ROC curve. Figure 17A-B shows rhAMR FP/TP/FN/TN in mock microbiome pools. DETAILED DESCRIPTION Definitions Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In case of conflict, the present document, including definitions, will control. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing of the present invention. The terms “antibiotic” and “antimicrobial compound” are used interchangeably herein and are used herein to describe a compound or composition which decreases the viability of a microorganism, or which inhibits the growth or reproduction of a microorganism. “Inhibits the growth or reproduction” means increasing the generation cycle time by at least 2-fold, preferably at least 10-fold, more preferably at least 100-fold, and most preferably indefinitely, as in total cell death. As used in this disclosure, an antibiotic is further intended to include an antibacterial, bacteriostatic, or bactericidal agent. Non-limiting examples of antibiotics useful in aspect of the invention include penicillins, cephalosporins, aminoglycosides, sulfonamides, macrolides, tetracyclins, lincosamides, quinolones, chloramphenicol, glycopeptides, metronidazole, rifampin, isoniazid, spectinomycin, folate inhibitors, sulfamethoxazole, and others. The terms “resistant” and “resistance”, as used herein, refer to the phenomenon that a microorganism does not exhibit decreased viability or inhibited growth or reproduction when exposed to concentrations of the antimicrobial agent that can be attained with normal therapeutic dosage regimes in humans. It implies that an infection caused by this microorganism cannot be successfully treated with this antimicrobial agent. The term “microorganism”, as used herein, refers in particular to pathogenic microorganisms, such as bacteria, yeast, fungi and intra- or extra-cellular parasites. In preferred aspects of the present invention, the term refers to pathogenic or opportunistic bacteria. These include both Gram-positive and Gram-negative bacteria. By way of Gram-negative bacteria, mention may be made of bacteria of the following genera: Pseudomonas, Escherichia, Salmonella, Shigella, Enterobacter, Klebsiella, Serratia, Proteus, Campylobacter, Haemophilus, Morganella, Vibrio, Yersinia, Acinetobacter, Branhamella, Neisseria, Burkholderia, Citrobacter, Hafnia, Edwardsiella, Aeromonas, Moraxella, Pasteurella, Providencia, Actinobacillus, Alcaligenes, Bordetella, Cedecea, Erwinia, Pantoea, Ralstonia, Stenotrophomonas, Xanthomonas and Legionella. By way of Gram-positive bacteria, mention may be made of bacteria of the following genera: Enterococcus, Streptococcus, Staphylococcus, Bacillus, Listeria, Clostridium, Gardnerella, Kocuria, Lactococcus, Leuconostoc, Micrococcus, Mycobacteria and Corynebacteria. By way of yeasts and fungi, mention may be made of yeasts of the following genera: Candida, Cryptococcus, Saccharomyces and Trichosporon. The term “sample”, as used herein, refers to a substance that contains or is suspected of containing an analyte, such as a microorganism to be characterized. A sample useful in a method of the invention can be a liquid or solid, can be dissolved or suspended in a liquid, can be in an emulsion or gel, and can be bound to or absorbed onto a material. A sample can be a biological sample, environmental sample, experimental sample, diagnostic sample, or any other type of sample that contains or is suspected to contain the analyte of interest. As such, a sample can be, or can contain, an organism, organ, tissue, cell, bodily fluid, biopsy sample, environmental sample (such as water, wastewater, or swab), soil, animal bedding, or fraction thereof. In a biological context, a sample can include biological fluids, whole organisms, organs, tissues, cells, microorganisms, culture supernatants, subcellular organelles, protein complexes, individual proteins, recombinant proteins, fusion proteins, viruses, viral particles, peptides and amino acids. The term “quantifying”, as used herein, refers to any method for obtaining a quantitative measure. For example, quantifying a microorganism can include determining its abundance, relative abundance, intensity, concentration, and/or count, etc. "Complement" or "complementary" as used herein means a nucleic acid, and can mean Watson-Crick (e.g., A-T/U and C-G) or Hoogsteen base pairing between nucleotides or nucleotide analogs of nucleic acid molecules. "Fluorophore" or "fluorescent label" refers to compounds with a fluorescent emission maximum between about 350 and 900 nm. "Hybridization" as used herein, refers to the formation of a duplex structure by two single-stranded nucleic acids due to complementary base pairing. Hybridization can occur between fully complementary nucleic acid strands or between "substantially complementary" nucleic acid strands that contain minor regions of mismatch. "Identical" sequences refers to sequences of the exact same sequence or sequences similar enough to act in the same manner for the purpose of signal generation or hybridizing to complementary nucleic acid sequences. "Primer dimers" refers to the hybridization of two oligonucleotide primers. "Stringent hybridization conditions" as used herein means conditions under which hybridization of fully complementary nucleic acid strands is strongly preferred. Under stringent hybridization conditions, a first nucleic acid sequence (for example, a primer) will hybridize to a second nucleic acid sequence (for example, a target sequence), such as in a complex mixture of nucleic acids. Stringent conditions are sequence-dependent and will be different in different circumstances. Stringent conditions can be selected to be about 5-10°C lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength pH. The Tm can be the temperature (under defined ionic strength, pH, and nucleic concentration) at which 50% of an oligonucleotide complementary to a target hybridize to the target sequence at equilibrium (as the target sequences are present in excess, at Tm, 50% of the probes are occupied at equilibrium). Stringent conditions can be those in which the salt concentration is less than about 1.0 M sodium ion, such as about 0.01-1.0 M sodium ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 30°C for short probes (e.g., about 10-50 nucleotides) and at least about 60°C for long probes (e.g., greater than about 50 nucleotides). Stringent conditions can also be achieved with the addition of destabilizing agents such as formamide. For selective or specific hybridization, a positive signal can be at least 2 to 10 times background hybridization. Exemplary stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C. The terms "nucleic acid," "oligonucleotide," or "polynucleotide," as used herein, refer to at least two nucleotides covalently linked together. The depiction of a single strand also defines the sequence of the complementary strand. Thus, a nucleic acid also encompasses the complementary strand of a depicted single strand. Many variants of a nucleic acid can be used for the same purpose as a given nucleic acid. Thus, a nucleic acid also encompasses substantially identical nucleic acids and complements thereof. A single strand provides a probe that can hybridize to a target sequence under stringent hybridization conditions. Thus, a nucleic acid also encompasses a probe that hybridizes under stringent hybridization conditions. Nucleic acids can be single stranded or double stranded, or can contain portions of both double stranded and single stranded sequences. The nucleic acid can be DNA, both genomic and cDNA, RNA, or a hybrid, where the nucleic acid can contain combinations of deoxyribo- and ribonucleotides, and combinations of bases including uracil, adenine, thymine, cytosine, guanine, inosine, xanthine hypoxanthine, isocytosine and isoguanine. Nucleic acids can be obtained by chemical synthesis methods or by recombinant methods. A particular nucleic acid sequence can encompass conservatively modified variants thereof (e.g., codon substitutions), alleles, orthologs, single nucleotide polymorphisms (SNPs), and complementary sequences as well as the sequence explicitly indicated. "Polymerase Chain Reaction (PCR)" refers to the enzymatic reaction in which DNA fragments are synthesized and amplified from a substrate DNA in vitro. The reaction typically involves the use of two synthetic oligonucleotide primers, which are complementary to nucleotide sequences in the substrate DNA which are separated by a short distance of a few hundred to a few thousand base pairs, and the use of a thermostable DNA polymerase. The chain reaction consists of a series of 10 to 40 cycles. In each cycle, the substrate DNA is first denatured at high temperature. After cooling down, synthetic primers which are present in vast excess, hybridize to the substrate DNA to form double- stranded structures along complementary nucleotide sequences. The primer-substrate DNA complexes will then serve as initiation sites for a DNA synthesis reaction catalyzed by a DNA polymerase, resulting in the synthesis of a new DNA strand complementary to the substrate DNA strand. The synthesis process is repeated with each additional cycle, creating an amplified product of the substrate DNA. "Primer," as used herein, refers to an oligonucleotide capable of acting as a point of initiation for DNA synthesis under suitable conditions. Suitable conditions include those in which hybridization of the oligonucleotide to a template nucleic acid occurs, and synthesis or amplification of the target sequence occurs, in the presence of four different nucleoside triphosphates and an agent for extension (e.g., a DNA polymerase) in an appropriate buffer and at a suitable temperature. "Probe" and "fluorescent generation probe" are synonymous and refer to either a) a sequence-specific oligonucleotide having an attached fluorophore and/or a quencher, and optionally a minor groove binder or b) a DNA binding reagent, such as, but not limited to, SYBR® Green dye. "Quencher" refers to a molecule or part of a compound, which is capable of reducing the emission from a fluorescent donor when attached to or in proximity to the donor. Quenching may occur by any of several mechanisms including fluorescence resonance energy transfer, photo-induced electron transfer, paramagnetic enhancement of intersystem crossing, Dexter exchange coupling, and exciton coupling such as the formation of dark complexes. The term "RNase H PCR (rhPCR)" refers to a PCR reaction which utilizes "blocked" oligonucleotide primers and an RNase H enzyme. "Blocked" primers contain at least one chemical moiety (such as, but not limited to, a ribonucleic acid residue) bound to the primer or other oligonucleotide, such that hybridization of the blocked primer to the template nucleic acid occurs, without amplification of the nucleic acid by the DNA polymerase. Once the blocked primer hybridizes to the template or target nucleic acid, the chemical moiety is removed by cleavage by an RNase H enzyme, which is activated at a high temperature (e.g., 50°C or greater). Following RNase H cleavage, amplification of the target DNA can occur. In one embodiment, the 3' end of a blocked primer can comprise the moiety rDDDDMx, wherein relative to the target nucleic acid sequence, "r" is an RNA residue, "D" is a complementary DNA residue, "M" is a mismatched DNA residue, and "x" is a C3 spacer. A C3 spacer is a short 3-carbon chain attached to the terminal 3' hydroxyl group of the oligonucleotide, which further inhibits the DNA polymerase from binding before cleavage of the RNA residue. As used herein, a primer is “specific,” for a target sequence if, when used in an amplification reaction under sufficiently stringent conditions, the primer hybridizes primarily to the target nucleic acid. Typically, a primer is specific for a target sequence if the primer-target duplex stability is greater than the stability of a duplex formed between the primer and any other sequence found in the sample. One of skill in the art will recognize that various factors, such as salt conditions as well as base composition of the primer and the location of the mismatches, will affect the specificity of the primer, and that routine experimental confirmation of the primer specificity will be needed in many cases. Hybridization conditions can be chosen under which the primer can form stable duplexes only with a target sequence. Thus, the use of target-specific primers under suitably stringent amplification conditions enables the selective amplification of those target sequences which contain the target primer binding sites. The term “non-specific amplification,” as used herein, refers to the amplification of nucleic acid sequences other than the target sequence which results from primers hybridizing to sequences other than the target sequence and then serving as a substrate for primer extension. The hybridization of a primer to a non-target sequence is referred to as “non-specific hybridization” and is apt to occur especially during the lower temperature, reduced stringency, pre-amplification conditions, or in situations where there is a variant allele in the sample having a very closely related sequence to the true target as in the case of a single nucleotide polymorphism (SNP). The term “primer dimer,” as used herein, refers to a template-independent non-specific amplification product, which is believed to result from primer extensions wherein another primer serves as a template. Although primer dimers frequently appear to be a concatamer of two primers, i.e., a dimer, concatamers of more than two primers also occur. The term “primer dimer” is used herein generically to encompass a template-independent non-specific amplification product. The term “reaction mixture,” as used herein, refers to a solution containing reagents necessary to carry out a given reaction. An “amplification reaction mixture”, which refers to a solution containing reagents necessary to carry out an amplification reaction, typically contains oligonucleotide primers and a DNA polymerase or ligase in a suitable buffer. A “PCR reaction mixture” typically contains oligonucleotide primers, a DNA polymerase (most typically a thermostable DNA polymerase), dNTP's, and a divalent metal cation in a suitable buffer. A reaction mixture is referred to as complete if it contains all reagents necessary to enable the reaction, and incomplete if it contains only a subset of the necessary reagents. It will be understood by one of skill in the art that reaction components are routinely stored as separate solutions, each containing a subset of the total components, for reasons of convenience, storage stability, or to allow for application-dependent adjustment of the component concentrations, and that reaction components are combined prior to the reaction to create a complete reaction mixture. Furthermore, it will be understood by one of skill in the art that reaction components are packaged separately for commercialization and that useful commercial kits may contain any subset of the reaction components which includes the blocked primers of the invention. For the purposes of this invention, the terms “non-activated” or “inactivated,” as used herein, refer to a primer or other oligonucleotide that is incapable of participating in a primer extension reaction or a ligation reaction because either DNA polymerase or DNA ligase cannot interact with the oligonucleotide for their intended purposes. In some embodiments when the oligonucleotide is a primer, the non-activated state occurs because the primer is blocked at or near the 3′-end so as to prevent primer extension. When specific groups are bound at or near the 3′-end of the primer, DNA polymerase cannot bind to the primer and extension cannot occur. A non-activated primer is, however, capable of hybridizing to a substantially complementary nucleotide sequence. For the purposes of this invention, the term “activated,” as used herein, refers to a primer or other oligonucleotide that is capable of participating in a reaction with DNA polymerase or DNA ligase. A primer or other oligonucleotide becomes activated after it hybridizes to a substantially complementary nucleic acid sequence and is cleaved to generate a functional 3′- or 5′-end so that it can interact with a DNA polymerase or a DNA ligase. For example, when the oligonucleotide is a primer, and the primer is hybridized to a template, a 3′-blocking group can be removed from the primer by, for example, a cleaving enzyme such that DNA polymerase can bind to the 3′ end of the primer and promote primer extension. The term “cleavage domain” or “cleaving domain,” as used herein, are synonymous and refer to a region located between the 5′ and 3′ end of a primer or other oligonucleotide that is recognized by a cleavage compound, for example a cleavage enzyme, that will cleave the primer or other oligonucleotide. For the purposes of this invention, the cleavage domain is designed such that the primer or other oligonucleotide is cleaved only when it is hybridized to a complementary nucleic acid sequence, but will not be cleaved when it is single-stranded. The cleavage domain or sequences flanking it may include a moiety that a) prevents or inhibits the extension or ligation of a primer or other oligonucleotide by a polymerase or a ligase, b) enhances discrimination to detect variant alleles, or c) suppresses undesired cleavage reactions. One or more such moieties may be included in the cleavage domain or the sequences flanking it. The term “RNase H cleavage domain,” as used herein, is a type of cleavage domain that contains one or more ribonucleic acid residue or an alternative analog which provides a substrate for an RNase H. An RNase H cleavage domain can be located anywhere within a primer or oligonucleotide, and is preferably located at or near the 3′-end or the 5′-end of the molecule. The terms “cleavage compound,” or “cleaving agent” as used herein, refers to any compound that can recognize a cleavage domain within a primer or other oligonucleotide, and selectively cleave the oligonucleotide based on the presence of the cleavage domain. The cleavage compounds utilized in the invention selectively cleave the primer or other oligonucleotide comprising the cleavage domain only when it is hybridized to a substantially complementary nucleic acid sequence, but will not cleave the primer or other oligonucleotide when it is single stranded. The cleavage compound cleaves the primer or other oligonucleotide within or adjacent to the cleavage domain. The term “adjacent,” as used herein, means that the cleavage compound cleaves the primer or other oligonucleotide at either the 5′-end or the 3′ end of the cleavage domain. Cleavage reactions preferred in the invention yield a 5′-phosphate group and a 3′-OH group. In a preferred embodiment, the cleavage compound is a “cleaving enzyme.” A cleaving enzyme is a protein or a ribozyme that is capable of recognizing the cleaving domain when a primer or other nucleotide is hybridized to a substantially complementary nucleic acid sequence, but that will not cleave the complementary nucleic acid sequence (i.e., it provides a single strand break in the duplex). The cleaving enzyme will also not cleave the primer or other oligonucleotide comprising the cleavage domain when it is single stranded. Examples of cleaving enzymes are RNase H enzymes and other nicking enzymes. The term “blocking group,” as used herein, refers to a chemical moiety that is bound to the primer or other oligonucleotide such that an amplification reaction does not occur. For example, primer extension and/or DNA ligation does not occur. Once the blocking group is removed from the primer or other oligonucleotide, the oligonucleotide is capable of participating in the assay for which it was designed (PCR, ligation, sequencing, etc). Thus, the “blocking group” can be any chemical moiety that inhibits recognition by a polymerase or DNA ligase. The blocking group may be incorporated into the cleavage domain but is generally located on either the 5′- or 3′-side of the cleavage domain. The blocking group can be comprised of more than one chemical moiety. In the present invention the “blocking group” is typically removed after hybridization of the oligonucleotide to its target sequence. General Description To mitigate the transmission and enrichment of antimicrobial resistance (AMR) in hosts and the environment, it is critical to characterize the repertoire of AMR genes (i.e., resistome). Short read (SR) targets are used because the rhPCR technology has been optimized for Illumina SR sequencing, however, Oxford Nanopore Technologies® (ONT) platforms have also been successfully used, thus there is novelty in applying ONT for real-time data acquisition. As disclosed herein, rhPCR panels are used to profile ARGs in microbiomes and wastewater samples and the panel is expanded to include the detection of SARS-CoV-2 variants, foodborne pathogens of interest and high-resolution subtyping of select foodborne bacteria. Disclosed herein is a novel resistome characterization method, providing the resolution of shotgun sequencing with the cost-effectiveness of targeted PCR. Real-time results can be obtained, for example, using a portable Nanopore MinION sequencer. Figure 14 shows the application of highly parallel multiplexed amplicon generation (to enrich for ARGs from complex samples) and real-time sequencing with the pocket-sized sequencer Nanopore MinION Mk1C. This method is based on a dual-enzyme approach that uses rhPCR technology (Dobosy 2011) with rhPrimers which contain a 3’ blocking group and a single RNA base. This DNA-RNA junction is recognized and cleaved by RNase H2 (Fresnedo-Ramirez 2019). Only after cleaving, extension by DNA polymerases becomes possible (Dobosy 2011, herein incorporated by reference in its entirety). This approach reduces off-target amplification and mitigates primer dimer creation, allowing for massive multiplexing (Fresnedo-Ramirez 2019) with high specificity and real-time sequencing. To provide more detail, rhPCR, also referred to as RNAse H-dependent PCR, can be used (U.S. Patent Application No. US2019/0218611, herein incorporated by reference in its entirety for its teaching concerning rhPCR). rhPCR is drawn to a method of utilizing blocked-cleavable rhPCR primers (see U.S. Patent Application Publication No. US 2009/0325169 A1, incorporated by reference herein in its entirety) and a DNA polymerase with high levels of mismatch discrimination. This is illustrated in Figure 1A-C. This can be accomplished using primers which are specifically designed to target AMR genes (see Example 1, for instance). The methods described herein can be performed using any suitable RNase H enzyme that is derived or obtained from any organism. Typically, RNase H-dependent PCR reactions are performed using an RNase H enzyme obtained or derived from the hyperthermophilic archaeon Pyrococcus abyssi such as RNase H2. Thus, in one embodiment, the RNase H enzyme employed in the methods described herein desirably is obtained or derived from Pyrococcus abyssi, preferably an RNase H2 obtained or derived from Pyrococcus abyssi. In other embodiments, the RNase H enzyme employed in the methods described herein can be obtained or derived from other species, for example, Pyrococcus furiosis, Pyrococcus horikoshii, Thermococcus kodakarensis, or Thermococcus litoralis. After the sample has been amplified, the amplicons can be fitted with barcoded adapters which allow for the individual amplicons to be identified. They can then be pooled and sequenced. Methods of barcoding sequences which are compliant with the sequencing methods disclosed herein are known to those of skill in the art. For example, U.S. Patent Application US 2018/0087050 and PCT Application WO2022/067019 (both incorporated by reference herein), teach methods of barcoding nucleic acid which can be used with the present invention. Oxford Nanopore Technologies (ONT) relies on a nanoscale protein pore, or ‘nanopore’, that serves as a biosensor and is embedded in an electrically resistant polymer membrane. In an electrolytic solution, a constant voltage is applied to produce an ionic current through the nanopore such that negatively charged single-stranded DNA or RNA molecules are driven through the nanopore from the negatively charged ‘cis’ side to the positively charged ‘trans’ side. Translocation speed is controlled by a motor protein that ratchets the nucleic acid molecule through the nanopore in a step-wise manner. Changes in the ionic current during translocation correspond to the nucleotide sequence present in the sensing region and are decoded using computational algorithms, allowing real-time sequencing of single molecules. Importantly, this invention can be used with any sequencing technology which allows for high-speed, high- throughput sequencing of a large amount of nucleic acid. Specifically, disclosed herein is a method of detecting two or more antimicrobial resistance (AMR) gene targets in a biological sample, the method comprising: (a) providing a reaction mixture comprising (i) two or more oligonucleotide primer pairs, wherein each primer pair is specific for an AMR gene target, and further wherein each primer has a cleavage domain positioned 5' of a blocking group and 3' of a position of hybridization with a target nucleic acid, wherein the blocking group is linked at or near the end of the 3'-end of the oligonucleotide primer, wherein the blocking group prevents primer extension and/or inhibits the primer from serving as a template for DNA synthesis, (ii) a biological sample comprising two or more target nucleic acids, (iii) a cleaving enzyme, and (iv) a polymerase; (b) exposing the two or more oligonucleotide primer pairs to the biological sample, wherein if one or more target nucleic acids are present in the sample, at least one primer and at least one target form a hybridized target/primer substrate; (c) cleaving the one or more primers of the hybridized primer/substrate of step b) with the cleaving enzyme at a point within or adjacent to the cleavage domain to remove the blocking group from the primer; (d) extending the primer with the polymerase, thereby obtaining amplified target nucleic acid; (e) barcoding each amplified target nucleic acid with a unique barcoded adapter to produce barcoded samples; and (f) sequencing the barcoded samples of step (e), thereby detecting AMR gene targets. AMR gene targets are well characterized, and an example list can be found below in Table 1. The primers disclosed herein can be used to detect any AMR gene target from any organism, including pathogenic and non-pathogenic microorganisms, such as bacteria and fungi. A “gene target” can mean a nucleic acid sequence within a gene which is the target of detection efforts. The method disclosed herein can be used to carry out multiplex detection, so that 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more gene targets can be detected at the same time. By “3’ end” or “5’ end” is meant the terminal nucleotide in a nucleic acid, such as a primer. By “near the end” is meant within 1, 2, 3, 4, or 5 nucleotides of the terminal nucleic acid. By “primer pair” is meant a pair of primers which is suitable for amplification of a gene target, such as a forward and reverse primer (one of each per pair). Once the primer has been extended, it can be labeled with an adaptor (also referred to herein as a “barcode”) which can allow for the sequencing of the amplified product. This can be done using a variety of methods known in the art, such as with nanopore technology described above. In some embodiments, the method may comprise detecting the presence, absence or amount of a target polynucleotide by detecting a signal output. The signal may be characteristic of an attached barcode. EXAMPLES To further illustrate the principles of the present disclosure, the following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how the compositions, articles, and methods claimed herein are made and evaluated. They are intended to be purely exemplary of the invention and are not intended to limit the scope of what the inventors regard as their disclosure. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperatures, etc.); however, some errors and deviations should be accounted for. Unless indicated otherwise, temperature is °C or is at ambient temperature, and pressure is at or near atmospheric. There are numerous variations and combinations of process conditions that can be used to optimize product quality and performance. Only reasonable and routine experimentation will be required to optimize such process conditions. EXAMPLE 1: HIGHLY MULTIPLEXED AMPLICON SEQUENCING WITH NANOPORE FOR DETECTION OF PATHOGENS AND ARG TARGETS Overview An ARG primer panel has been successfully designed in collaboration with bioinformaticians at IDT using the MEGARes2.0 database. The primer panel contains 2,451 pairs of primers that have been tested in silico and found to detect 7,364 ARG targets in 2 separate reactions. A subset of this panel has been tested in vitro with mock microbiomes and human fecal samples using both short read sequencing (SRS) and Oxford Nanopore Technology (ONT) sequencing. Short read targets are used because the rhPCR technology has been optimized for Illumina short read sequencing, however, success has been proven in obtaining data from ONT platforms, therefore ONT can be applied for real-time data acquisition. This panel is then combined to primer panels targeting SARS-CoV-2 variants of concern. Primers are developed to target major foodborne pathogens of concern in the US. The rhPCR primer panel is designed for compatibility for multiplexing in as few reactions as possible and for subsequent subtyping based on amplicon sequences. A (i) primer panel based on MLST schemes for bacterial pathogens of interest is designed and (ii) the panel performance is evaluated using mock microbiomes and spiked wastewater samples. High resolution subtyping of foodborne bacteria is also conducted. Genomic regions of high discriminatory ability for subtyping of foodborne pathogens is carried out in Salmonella and E. coli. (i) Diagnostic SNPs are identified and used to develop a large scale MLST scheme that are used for (ii) primer panel design and testing using mock microbiomes and spiked wastewater samples. This process yields (i) optimized ARG rhPCR panel, (ii) rhPCR primer panel for detection of SARS-CoV-2 variants of concern, (iii) a foodborne pathogen detection rhPCR panel, and (iv) a high-resolution foodborne bacteria subtyping rhPCR panel. Technical Details Design of comprehensive rhPCR primer panels: ARG primer panels were successfully designed using the MEGARes2.0³ database. Short read sequencing (SRS) were first used because the rhPCR technology has been optimized for Illumina SRS sequencing. Success has also been achieved in obtaining data from ONT platforms. A subset of designed primers for a proof-of-concept experiment on a mock microbiome which contained 30 strains (10 Campylobacter jejuni, 6 Shigella sonnei, 4 Escherichia coli, 6 Salmonella Typhimurium, and 4 Salmonella Heidelberg) were purchased. The ability of rhPCR to detect 96 genes with 85.4% sensitivity, with as few as 15,000 sequencing reads per sample (without any experimental optimization) was proven. For comparison, shotgun metagenomics can reliably detect resistome with 60 million sequencing reads per sample, which is three orders of magnitude higher. Strains were selected to be included in the mock microbiome based on their ARG profiles identified using WGS. Detected ARG (a total of 96) were used as the “ground truth” to evaluate the performance of the rhPCR primer panel. This primer panel was also applied on DNA from human fecal samples inoculated with DNA from bacteria containing unique ARGs in serial dilutions to demonstrate the feasibility of amplification of the target genes in a complex sample, and assess primer efficiency, and limit of detection. Shotgun sequencing of human fecal samples was used to determine bacteria for inoculation and as ground truth. This experiment has been repeated with ONT sequencing technology with similar results. This can also be done with fecal samples from any animals, including poultry such as turkeys. The methods disclosed herein (rhPCR coupled with real time sequencing) are a better- performing alternative to shotgun sequencing for resistome, SARS-CoV-2, and foodborne pathogen profiling due to improved sensitivity, and reduced cost and time-to-results. Optimize and apply highly multiplexed amplicon sequencing with Nanopore for detection of pathogen and ARG targets: Primers are designed and validated in silico followed by validation using mock microbiomes comprised of a well-characterized panel of pathogens and wastewater and clinical samples spiked with various strains. Using well-defined mock microbiomes allows for initial sensitivity, specificity, limit of detection, and primer efficiency assessment, while the use of environmental and clinical samples demonstrate the feasibility for application on complex samples (clinical samples, wastewater). rhPCR can detect targets present in a mock microbiome with a high >80% sensitivity and specificity. Furthermore, rhPCR can detect targets present in a strain inoculated into a complex sample (wastewater, clinical samples) at low concentrations (e.g., 1:100,000) demonstrating good limit of detection. Optimize and evaluate the performance of rhPCR for ARG using a mock microbiome and spiked samples: The CDC’s enteric diversity panel from Antibiotic Resistance Isolate Bank is used to assemble a mock microbiome and to spike samples. Strains are grown in appropriate media, DNA is extracted, quantified, and mixed in equal ratios based on genome equivalents which is used for amplification with the rhPCR panel and sequencing as described in Figure 1. Whole genome sequencing data (WGS) for isolates is used as ground truth data for tests using mock microbiomes. Samples are profiled and diluted at different concentrations to estimate the method's limit of detection. Samples’ ARGs are characterized prior to inoculation using shotgun metagenomics (at ~60 M reads/sample) and with the rhPCR panel to assess the proportion of targets that were detected using both methods. Additionally, rhPCR amplicons are sequenced both with ONT and Illumina as the gold standard for rhPCR-based sequencing. The generated data is used to optimize and evaluate the performance of rhPCR for AMR characterization of different sample types. Primer design and in-silico testing: rhPCR ARG primers were designed to target over 7,800 ARG targets included in the recently updated MEGARes 2 database (Doster 2020). Primer design and in silico testing was performed by IDT scientists based on the MEGARes v2.0 database using their proprietary software. IDT has provided primer sequences to the Ganda lab in FASTA and BAM formats as well as assay IDs for primer purchase. Based on initial results, individual primers are purchased in 96-well plate format to allow for mix-and-match optimization of the panel. Preparation of mock microbiomes and spiked samples: The CDC’s Enteric Diversity Panel (EDP) is used for preparation of mock microbiomes that are used in the sensitivity, specificity, and primer efficiency evaluation. The EDP panel includes 30 strains for which WGS are available and ARG are known. Strains are selected that harbor 3 or more unique ARG each to include in a mock microbiome for the initial sensitivity and specificity evaluation in a well- defined and controlled system. Subsequently, all 30 strains are used in the limit of detection (LOD) and primer efficiency assessments. Nucleic acid extraction: DNA is isolated from 1.5 ml of each culture using the MagMAX Microbiome Ultra Nucleic Acid Isolation Kits (ThermoFisher, MA), according to the manufacturer's instructions. DNA concentration and quality are assessed with a Qubit and Nanodrop, respectively. rhPCR, library preparation, nanopore sequencing, and data processing. Extracted genomic DNA undergoes dual-PCR rhPCR library preparation (Figure 1) following manufacturer’s instructions. Briefly, extracted DNA is combined with rhPCR primers and rhAmpSeq library mix to amplify targets of interest. The rhPCR amplicons from step 1 are prepared for sequencing with the Oxford Nanopore Rapid Barcoding Kit. After barcoding, the samples are pooled into one library and loaded into the R9 flow cell. Sequencing is run for up to 40 hours, and data is processed with EPI2ME using the standard Fastq Antimicrobial Resistance Workflow. Assay controls. Nucleic acid extractions, rhPCR and rhPCR library preparation is each carried out as described above with reagents only (no sample DNA added) and is included in sequencing runs as negative controls. Commercially available, validated microbiomes standards are used as positive controls. Illumina sequencing and data processing. In experiments, data obtained with Nanopore and Illumina chemistry is compared, given that rhPCR sequencing has been developed and optimized with Illumina platforms. For shotgun sequencing, nucleic acids are shipped to the lab where libraries are prepared using a Nextera flex library prep kit and sequenced using either a MiSeq or Novaseq platform. Data is analyzed as follows: briefly, read quality are assessed on a random subset of forward and reverse reads in several samples using FastQC (Andrews 2010). Trimming is performed using Trimmomatic (Bolger 2010) to remove bases under Phred score 30 at the trailing end and to remove reads under 50 bp length. Paired-end reads are merged in FLASH with max overlap of 150 and converted to fasta format with seqkit (Shen 2016). A BLAST database is constructed from the entire MEGARes database (v2.0) (Doster 2020) and a local alignment performed and filtered to 100% identity. The AmrPlusPlus workflow (Doster 2020; Lakin 2017) is used instead of BLAST where applicable. Top hits are identified as present ARG. Gene counts are constructed from the summed number of local alignments per sample, and relative abundances calculated. Sensitivity and specificity: Mock microbiomes are prepared with a leave-one-out design to assess the sensitivity and specificity for detection of genes that are unique to a given strain or variant that is left out in each experiment. Each strain is grown in appropriate conditions and extracted nucleic acids are mixed in equal ratios to form a mock microbiome. Mock microbiomes are used to determine the sensitivity and specificity of rhPCR to detect the unique targets of a total of targets present in the selected strains. The WGS data of strains are used as gold standard for calculation of sensitivity (true positive rate) and specificity (true negative rate). Limit of detection and primer efficiency: Mock microbiomes are prepared comprised of strains that do not carry a specific gene or variant (e.g. gene “X”) and inoculate them with a “spike strain” that carries the gene or variant. The strains are grown in appropriate conditions and extracted nucleic acids are mixed in equal ratios to form a mock microbiome. The spike strain is inoculated into the mock microbiome in different concentrations (at 1:1, 1:10, 1:100, 1:1,000, 1:10,000, 1:100,000 and 1:1,000,000 ratio). With the increasing concentration of a spiked strain, an increasing relative abundance of the gene that is uniquely present in the spiked strain is used. The proposed dilutions were selected based on data that showed that the target ARGs can be detected even at 1:10,000 dilution. The primer efficiency is measured by calculating the regression coefficient for a linear regression model describing the relationship between the target gene read count and the dilution of the strain carrying the target gene. Once the performance of the primer panel is satisfactory, the primer panel is applied to characterize wastewater samples. Data analysis: Target counts are determined from the summed number of local alignments per sample, and relative abundances are calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are calculated according to Dohoo (Dohoo 2009). Design and test a rhPCR panel for detection of SARS-CoV-2 variants of concern. A database is developed containing publicly available SARS-CoV-2 variants of concern (Zhou 2021; Korber 2020; Volz 2021) and other coronavirus genomes. Alignments are performed to identify regions of greatest diversity among closely and distantly related viruses, and diagnostic single nucleotide polymorphisms (SNPs) are identified. These regions and SNPs are used for panel design in collaboration with IDT as described above. Testing is carried out as described above, with the following exceptions: SARS-CoV-2 variants of concern available in our BSL-3 laboratory are grown in appropriate media and conditions, and properly inactivated prior to experiments performed. Nucleic acid (RNA) extraction is performed from inactivated pure cultures of SARS-CoV-2 variants of concern with an established protocol using a KingFisher (ThermoFisher Scientific) with the MagMAX Viral/Pathogen extraction kit (ThermoFisher Scientific), and reverse transcriptase is performed to produce cDNA which is used for variant detection tests. Design a primer panel based on MLST schemes for pathogens of interest: A panel of rhPCR primers are designed to target foodborne bacteria (E. coli, Salmonella, Campylobacter, Vibrio, Listeria monocytogenes, Shigella, Yersinia enterocolitica) (Scallan 2011). Target regions are selected based on previously published and evaluated primer targets (e.g., MLST genes) and are used to design rhPCR primers^. Primers are designed for selected targets. The primer design is guided by in silico testing to ensure efficient amplification in a multiplexed reaction. Evaluate the performance of the panel using a mock microbiome and spiked wastewater: Stocks of microorganisms are obtained from the CDC or other culture collections (e.g., Penn State, ATCC). Primer efficiency is assessed by rhPCR using dilutions of DNA extracted from each pathogen. Next, the primer panel is assessed on a mock microbiome comprised of all targets represented in equal concentrations to assess the performance of primers in a multiplexed reaction. Next, mock microbiomes are prepared using a leave-one-out approach to assess the specificity of primers. Finally, microbiomes are prepared by inoculating individual target microorganisms in decreasing concentrations to estimate the method's limit of detection. This also allows for the assessment of primer amplification efficiency. The primer efficiency is measured by calculating the regression coefficient for a linear regression model describing the relationship between the target gene read count and the dilution of the strain carrying the target gene. Wastewater samples are also sequenced using shotgun metagenomic sequencing and results are compared with those obtained using rhPCR with the pathogen detection primer panel. Lastly, the LOD assessment that has previously been carried out using a well-defined mock microbiome is carried out using wastewater samples with the same methods described above. Identify diagnostic SNPs in genomes of Salmonella and E. coli. A database is developed containing genomes of pathogens of interest (e.g., E. coli O157:H7). Alignments are performed to identify regions of greatest diversity among closely and distantly related organisms, with an emphasis on genes associated with disease potential including O antigens and virulence genes. Databases of all known E. coli and Salmonella O antigen diagnostic gene targets are publicly available ( Diagnostic single nucleotide polymorphisms (SNPs) will be identified and a large-scale MLST scheme will be developed targeting hundreds of regions per genome. These regions and SNPs are used for panel design. Design rhPCR primer panel targeting regions of high-resolution subtyping ability. The regions identified herein are used to design rhPCR primers. Target number and panel discriminatory ability optimization are performed with various iterations of input parameters in silico to determine the most effective subtyping panel to be used for in-vitro tests, which are performed with the same methods described above. TABLE 1: Organisms and resistance profiles in the CDC’s enteric pathogen diversity panel. All organisms are BSL 2 and have WGS. Bolded strains are used in sensitivity and specificity analysis. Organism Resistance Mechanisms _{Salmonella Albert} ^{aph(3')-Ic, CMY-2, floR, gyrA83(M), strA, strB,} sul2, tet(A), tet(B) Salmonella Cubana dfrA14, KPC-2, strA, strB, sul2, TEM-1A aac(3)-IId, aadA2, aadA5, aph(3')-Ia, armA, catA2, Salmonella Stanley dfrA17, mph(A), mph€, msr(E), strA, strB, sul1, TEM-1B, tet(A) Salmonella Heidelberg CMY-2 Salmonella Senftenberg ^{aac(6')-Ib, aac(6')Ib-cr, aadA1, aadB, gyrA83(M),} g_{yrA87(M), OXA-9, parC80(M), SHV-12, TEM-1A} Salmonella Corvallis QnrS1, strA, strB, sul2, tet(A) ^{Salmonella Concord aac(6')-IIc, catA2, CTX-M-15, dfrA18, ere(A),} Q_{nrA1, SHV-12, strA, strB, sul1, sul2, tet(D)} Salmonella Typhimurium CMY-2, floR, strA, strB, sul2, tet(A) Salmonella Typhimurium Salmonella Infantis ^{aac(3)-Iva, aadA1, aph(3')-Ic, aph(4)-Ia, CTX-M-} 6_{5, dfrA14, floR, fosA, sul1, tet(A)} Campylobacter coli OXA-61, tet(O) Campylobacter jejuni tet(O) Campylobacter jejuni aph(3')-III, gyrA86(M), OXA-61, tet(O) Campylobacter jejuni gyrA86(M), tet(O) Campylobacter jejuni aph(3')-III, tet(O) Campylobacter coli aph(3')-III, gyrA86(M), OXA-61, tet(O) _{Campylobacter coli} ^{aac(6')-aph(2''), ant(6)-la, aph(3')-III, gyrA86(M),} t_et(O) Campylobacter coli OXA-61 Campylobacter jejuni Campylobacter coli aac(3)-IIa, aadA1, catA1, CTX-M-3, dfrA1, dfrA5, Shigella flexneri ere(A), OXA-1, strA, strB, sul1, sul2, TEM-1B, tet(B) S_{higella sonnei} ^{aadA5, catA1, dfrA17, mph(A), strA, strB, sul2,} T_{EM-1B, tet(B)} aac(6')Ib-cr, aadA1, aadA16, ARR-3, catA1, dfrA1, Shigella flexneri dfrA27, gyrA83(M), OXA-1, parC80(M), QnrB6, sul1, tet(A), tet(B) S_{higella flexneri} ^{aadA1, aadA5, catA1, dfrA1, dfrA17, erm(B),} mph(A), OXA-1, sul1, TEM-1B, tet(B) Shigella flexneri Shigella sonnei Escherichia coli O157 Escherichia coli O157 Escherichia coli O157 Escherichia coli O157 Following the completion of the rhAMR amplification and library preparation according to IDT’s protocols, the samples are sequenced in real-time using Oxford Nanopore’s MinION device. The library is prepared for sequencing according to the protocol for the Oxford Nanopore Rapid Barcoding Kit. These barcode-specific adapters are attached to each of the samples run in order to identify them in the output data. After barcoding, the samples are pooled into one library, which was used for sequencing. The flow cell is properly primed, library added, and sequencing is run for 40 hours. Upon completion of sequencing, nanopore sequencing data is processed with EPI2ME using the Fastq Antimicrobial Resistance Workflow. The sequencing results demonstrated successful detection of AMR genes in the samples from 7,725 reads. Results are shown in Table 2. Table 2. Results of MinION real-time sequencing data analysis in EPI2ME demonstrating detectable genes using IDT’s rhPCR amplification protocol. Sample # of detected genes average coverage match (%)

Control Broiler Samples 10 86.2 Treatment Broiler Samples 14 86.9

EXAMPLE 2: POULTRY FEEDING STRATEGIES AND THEIR EFFECT ON THE RESISTOME In a second example, poultry feeding strategies and their effect on the resistome are explored. Poultry is one of the leading sources of animal proteins in the human diet (USDA 2019). Foodborne pathogens commonly linked with poultry include the antimicrobial-resistant pathogens Campylobacter, non-typhoidal Salmonella, and extended spectrum β-lactamase producing Enterobacteriaceae (ESBLs) (Nhung 2017; Mor-Mur 2010). Hence the human exposure to these AMR pathogens via poultry meat is a concern. A recently published review of NARMS records from 2012-2017 indicates that poultry had the highest prevalence of AMR bacteria isolated from retail meat (Innes 2021) regardless of organic or conventional production type. The poultry food supply chain therefore presents an important source of not only these pathogenic bacteria, but also commensal bacteria carrying ARG that may be transmitted to pathogenic bacteria in humans, with recently published evidence of horizontally acquired ARG present in chicken gut microbiota commensals (Juricova). To mitigate the transmission of ARG through the food supply chain, efforts need to be placed into reducing the prevalence of antimicrobial-resistant microorganisms in animal agriculture and their transmission between animals and humans (van Bunnick 2017). This includes investigating the effects of different food animal production practices on the microbiome and resistome dynamics that is expected to lead to the identification of best practices for the reduction of ARG transmission in the food value chain (and simultaneous improvement in feed conversion). Several practices, including antibiotics, feed additives, litter management, and animal density, have already been linked with shifts in microbiome composition (Oakley 2018; Schokker 2017; Siwek 2018; Shi 2019; Feye 2020; Crippen 2019; Cressman 2014) which in some cases coincides with resistome enrichment; however, the magnitude of their effects is still unknown. Since 2017, medically important antibiotics have been prohibited for use in feed for growth promotion, leaving the poultry industry seeking alternative feeding strategies to maintain feed conversion and gut health. Although medicated feed is still allowed in flocks at high risk of infection, most conventional poultry producers have decreased or completely stopped the use of antimicrobials, either because of regulations or market pressure imposed by purchasers demanding antibiotic-free poultry meat. Withdraw of antibiotics for growth promotion is estimated to have increased production costs by $0.03 per broiler. Recently, many feed additives such as phytotherapeutics, probiotics, prebiotics, and essential oils for pathogen suppression have become available in the market. However, very little research has been done to understand the specific changes in the gut microbes caused by these products. Most importantly, no controlled study has evaluated the effect of various antibiotic-free additives on the poultry gut resistome. For example, a single report describing the impact of oregano-based additives on the poultry gut microbiome (Betancourt 2019) has pointed to selective enrichment of short-chain fatty acid-producing microbes, which are also enriched in birds fed sub-lethal doses of antibiotics (Banerjee 2018). Although a growing body of evidence exists indicating that antibiotic-free modulation of the poultry gut microbiota holds promise, (Ricke 2018) one must be aware that antimicrobial resistance still exists in antibiotic-free poultry (Bailey 2020; Baily 2019). The phenomena of co-resistance and co-selection between heavy metals and antimicrobials has been extensively reported (Bailey 2019); therefore, it is expected that some co-selection of ARG or increase of certain types of efflux pumps will occur in animals fed antibiotic-free feed additives. Despite antimicrobial resistance in poultry being studied in many ways in a farm-to-table approach, very few publications are available comprehensively characterizing the poultry resistome, particularly in birds fed different classes of antibiotic-free feed additives in a controlled setting. Disclosed herein is a method of applying rhAMR to generate quantitative data describing the effects of different feeding strategies on broiler resistomes. The results of this study provide critical data that informs feeding strategies that mitigate ARG in the poultry food value chain. Evaluation of probiotics in poultry production outcomes and microbiome: 720 Nicolas Select tom turkeys were purchased from a commercial hatchery and distributed across 24 pens at the Pennsylvania State Poultry Education and Research Center (PERC). Animals were randomized into control or probiotics at the pen level and grow out occurred over 19 weeks. Birds receiving the probiotic improved feed conversion ratio (FCR) by 0.08 when compared to control (P<0.0001), had improved weight gain, and higher concentrations of Bacillus in fecal and litter samples analyzed via culture (Erb et al. Journal of Applied Poultry Research 30(3): Dec.2021). Economic analysis indicated that probiotic supplementation reduced overall feed costs by $0.20 per bird. This project led to a secondary collaborative investigation to describe the effect of direct fed microbial in the environment (boot sock swabs from each pen) and various body sites of a subset of turkeys. The primary objective of the study was to determine if boot sock swabs would reflect the same differences between treatment and control group as expected to be observed in different gut sections. Sterile plastic boot covers (Romer Labs, Inc., Newark, DE, USA) were donned as the researcher stepped into each pen to limit cross contamination from their boots, the hallway floors, and other pens. Wearing a pair of sterile boot swabs moistened with buffered peptone water (Romer Labs, Inc., Newark, DE, USA) on top of the sterile plastic boot covers, the researcher circumnavigated each pen and made multiple crosses over the center to collect a representative sample of the environmental microbiome. The main motivation of this study was to gather information on sampling strategies to guide future studies. It was found that boot sock swabs had significant differences between treatment and control pens on beta diversity, at an effect size similar to what was observed in specific body sites of sampled animals. Although clustering is not obvious, groups were significantly different in all sample types when compared with adonis. Together, results indicate that boot sock swabs are an acceptable sampling method, providing evidence and guidance for designing future studies evaluating the effect of feed additives on the microbiome of birds in production settings. In further pursuit of understanding the effect of feed additives in the microbial profiles of poultry, a study was carried out to longitudinally describe the microbial profile of birds fed two antibiotic-free feed additives. Briefly, day-old Cobb 500 male chicks were purchased at a local hatchery, transported to PERC and randomly allocated into cages of 10 birds each where they were grown for 21 days. Cages were randomly allocated to receive a control diet (no additive), a diet supplemented with an essential oil blend, a probiotic at an inclusion of 3 x 10⁵ CFU per gram of feed (Calsporin, QTI), or antibiotic at an inclusion of 50g/ton as a positive control (BMD, bacitracin methylene disalicylate, Zoetis). Eight replicates (8 cages, 80 birds) per treatment were carried out, with a total of 320 birds. Fecal samples of each cage were collected by laying a sterile collection paper under the cage for one hour. Papers were put in a whirl-pak disposable bag and stored in a -80˚C freezer until DNA extraction and amplification of the V4 region of the 16S rRNA gene for microbial profiling. Birds were weighed 3 times; at the start of the trial, in the end of the starter phase and in the end of the grower phase (day 21) for determination of weight gain and feed conversion ratio. Improved feed conversion ratio (FCR) and lower variability for animals receiving the treatments were observed when compared to controls. A tendency of treatment effect on FCR was observed (Fonseca et al. Investigating antibiotic free feed additives for growth promotion in poultry: effects on performance and microbiota. Poult Sci.2024 May;103(5):103604). Characterize the frequency and scope of AMR gene presence in broilers fed var- ious feed supplements. Sixteen replicates of each of 6 treatments randomly allocated at the pen level with a complete block design fill the 96 pens. Briefly, day-old Cobb 500 birds are sourced from a local hatchery and allocated into pens with 36 birds each where they are grown to market size (5-7 weeks; 3,456 birds total). A standard diet is formulated according to published nutrient recommendations (Cobb500 Broiler Performance & Nutrition Supplement) and is referred to as the control. Commercially available feed supplements are added to the control diet at their respective recommended levels. Feed and refuse weights are recorded at the pen level to calculate total feed intake. Animals are weighted at the end of the starter period and at the end of the grower period to calculate feed conversion ratio. Baseline diet is fed and referred to as a negative control; a positive control diet is formulated with bacitracin methylene disalicylate (BMD) – Zoetis which is approved for prevention and control of necrotic enteritis in commercial poultry (50 grams/ton). Four other groups assess the impact of a probiotic, an oregano-based supplement, a saponin-based supplement, and a capsaicin-based supplement. Because the microbiome has been demonstrated to change throughout the production cycle (Ijaz 2018), a sampling scheme has been designed to longitudinally characterize the resistome across the broiler production cycle, while keeping sampling practical. Samples are collected at baseline (1-day old chick data is gathered from chick paper that lines the boxes in which chicks are shipped from the hatchery to the grower farms, representing the baseline microbiome and resistome of each flock). In addition to chick paper, samples are collected every other week. Sampling is performed as described in Thompson et al. (2018) and sanitized boots are covered by a sterile moistened boot sock. Study personnel circumnavigate each pen and make multiple crosses over the center to collect a representative sample of the environmental mi- crobiome. Each pair of boot socks are stored in a plastic bag on ice until processing. This sampling method was chosen because boot sock swabs have been demonstrated to be a good non-invasive approach for surveillance of the microbiome and pathogens present in poultry houses (Kers 2019), which was confirmed. Once in the laboratory, samples are stored at -80°C until processing, as freezing has been shown to preserve the integrity of the microbiota composition (Fouhy 2015) and the time of storage at this temperature should not significantly affect it (Lauber 2010). At the time of DNA extraction, 50 mL of DNA free water is added to each boot sock sample, massaged for 2 minutes, and used as starting material for DNA extraction. DNA extraction, rhAMR testing, sequencing and data analysis is performed as described above. Negative control boot socks exposed to the farm air but not used in the pens, as well as unopened boot socks are also included. Resistome is characterized using the rhAMR method as described above. Sample size calculation: The software GPower was used to calculate sample sizes for one-way ANOVA. With an expected difference of 10% in AMR gene prevalence between groups, a sample size of 82 samples is required to detect a difference with 80% power and an alpha of 0.05. Analysis and interpretation: Numbers of reads classified to each ARG are considered as raw counts, which is quantile normalized with normalization scale factor based on count shift distribution within the samples using the MetagenomeSeq R package. The hypotheses that resistomes differ among treatment groups is tested by principal coordinate analysis, ANOSIM, PERMANOVA, adonis, and reference frames. Multiple hypothesis testing corrections are performed using Bonferroni (1936) or Benjamini-Hochberg false discovery rate (1995). EXAMPLE 3: AN EVALUATION OF THE PERFORMANCE OF RNASE H2 DEPENDENT PCR (^RHA^MPS^EQ ) ^FOR R^ESISTOME C^{HARACTERIZATION IN A} C^OMPLEX S^AMPLE Genotyping is important in detecting antimicrobial resistance genes (ARGs) in a sample (i.e., resistome ). The current state of the art method for comprehensive resistome characterization is shotgun metagenomics, which requires sequencing at cost prohibitive depth. Therefore, alternative, cost effective methods for rapid resistome characterization are needed, such as RNase H2 dependent PCR ( rhAmpSeq ). Here, the limit of detection of a rhAmpSeq method that contained 414 primers targeting 98 ARGs was determined (Dobosy 2011). The results showed ARGs with counts ≥25 follow the expected negative correlation between relative abundance of an ARG and S. sonnei dilution, indicating good primer amplification efficiency. EXAMPLE 4: NANOPORE SEQUENCING OF ANTIMICROBIAL RESISTANT GENE TARGETS Materials and Methods Assay targets and primer design The MEGARes 2.0 database (Doster 2020) was chosen as it is currently the most comprehensive antimicrobial resistance gene (AMRg) database. Working with Integrated DNA Technologies (IDT) for assay development, all 7,885 AMRg accessions and sequences in the MEGARes 2.0 database were provided to construct primer panels using IDT’s rhAMPseq amplicon design tool. In brief, primers were designed along the database sequences, accounting for target insert size and off-target effects. Primers were scored on in silico sensitivity and specificity, both individually and as a panel. Additional quality thresholds dictated that primers were designed to have under 70% GC content and no more than one ambiguous base in a 25- base pair sliding window. Mock microbiome design To evaluate rhAMR performance, we designed mock microbiomes using four enteric bacteria (Campylobacter spp. N=10, Escherichia coli N=4, Salmonella enterica N=9, and Shigella sonnei N=6) for which we had access to both viable cultures and AMR genotypic data (Table 3). Strain AMR genotypes were obtained by genome assembly alignment to the MEGARes 2.0 database via Abricate (Seemann 2020) with the default 80% thresholds for gene coverage and percent identity. In addition to an all-strain mock microbiome, we designed smaller mock microbiomes based on principal component analysis (PCA) to identify strain clusters with similar AMRg (Figure 2). Using PCA clusters and strain-specific AMRg profiles, we identified 1) spike-in strains used to assess rhAMR’s detection limit with spike-in specific AMRg and 2) leave-one-out strains to assess rhAMR primer specificity and sensitivity for leave-one-out strain specific AMRg. Metagenomic samples To evaluate rhAMR performance in complex samples, we identified samples from two previous studies for which we had both shotgun metagenomic sequence data and extracted sample DNA. To determine the baseline AMR profiles for the first study involving human fecal samples (N=8), raw sequence reads were downloaded from SRA using SRR accession IDs. Reads were processed through the AMRPlusPlus Bioinformatic Pipeline (v2.0.2) (Doster 2020) using default parameters. In brief, raw reads were processed with Trimmomatic (Bolger 2014), removing low quality bases (Q score < 48) from 5’ and 3’ ends, and along a 4-base sliding window. Reads shorter than 36 base pairs were also removed. Host reads were filtered and removed using the reference Homo sapiens genome, GRCh38 (NCBI RefSeq; GCF_000001405.26). The remaining trimmed, host-decontaminated reads were mapped to the MEGARes 2.0 database with default gene coverage of 80% and processed through the remainder of the AMRPlusPlus Bioinformatic Pipeline. Similarly, to determine the baseline AMR profiles for the second metagenomic study involving turkey-associated boot sock samples (N=6) and pre- harvest cloacal swabs (N=2), raw sequence reads were analyzed through the AMRPlusPlus Bioinformatic Pipeline. In this instance, an Aviagen Nicholas turkey (Melagris gallopavo) genome assembly (NCBI RefSeq: GCF_000146605.3) was used for host read decontamination. The resultant count matrices from both studies were filtered to remove gene groups requiring SNP confirmation to be AMR-conferring. For gene groups for which multiple variants were detected, the counts for all variants within a group were summed. For each gene in each sample, reads-per-million (RPM) was calculated using the gene counts and trimmed, host- decontaminated sample read counts. Using the previously established metagenomic RPM cutoff of ≥ 0.1 for a gene to be considered present, binary AMRg presence-absence profiles were defined. Using these AMRg profiles and the established mock microbiome strain AMRg profiles, spike-in pools were designed as above. Mock microbiome strain growth, DNA extraction, and mock microbiome pooling For Campylobacter spp., strains were grown on Karmali agar (Oxoid, Hampshire, UK) plates under microaerophilic conditions for 48 hours at 41.5°C. Escherichia coli (E. coli), Salmonella enterica (S. enterica), and Shigella sonnei (S. sonnei) strains were grown overnight at 37°C in brain-heart infusion broth (BHI; RPI Corp, IL, USA) cultures. Following manufacturer instructions, genomic DNA was extracted from broth cultures and Campylobacter colonies using the MagMAX Microbiome Ultra (Applied Biosystems, CA, USA) extraction kit on the KingFisher Flex (Thermo Fisher Scientific, CA, USA) platform. DNA was quantified and diluted according to calculated genome equivalents such that, when pooling strain DNA to create mock microbiome pools, no strain would be overrepresented. Sample pools were then created according to aforementioned pool designs. For mock microbiome pools without spike-ins, DNA was pooled 1:1 by volume. For complex samples without spike-ins, no additional DNA was added. For mock microbiome and complex sample pools with spike-ins, spike-in strain DNA was serially diluted into the pools by volume. For leave-one-out pools, the excluded strain was not included in the pool. Escherichia coli K12 (ATCC 10798) DNA was included as a control. rhAmpSeq PCR, library preparation, and sequencing rhAmpSeq PCR and library preparation was performed according to IDT’s “rhAmpSeq Library Preparation For Targeted Amplicon Sequencing” protocol using the rhAmpSeq Library Kit (Cat. No.10000066; Integrated DNA Technologies) and Agencourt AMPure XP purification beads for amplicon and library clean-up (Beckman Coulter, CA, USA). Samples were processed through targeted rhAmp PCR, amplicon clean-up, indexing PCR, and indexed library clean-up steps. Libraries were sequenced at Penn State College of Medicine Genome Sciences Facility on the Illumina MiSeq platform (Illumina, CA, USA) yielding 250x250bp paired-end reads. Briefly, the same workflow was followed with the exception that half of the rhPCR/Illumina barcoding product went directly into the Miseq preparation workflow and the other half was library-prepped with additional barcodes derived from Nanopore Rapid PCR Barcoding Kit 24 V14 (Cat. No. SQK-RPB114.24). Reads were obtained from both Nanopore and Illumina were subject to the similar analytical pipelines with minor adjustments to the alignment of the MEGARes database to the amplicons as the current AMR++ pipeline (v3.0) does not support Nanopore data. All samples were run in duplicate. Bioinformatics and statistical analyses Raw sequence reads were processed through the AMRPlusPlus Bioinformatic Pipeline (v2.0.2) in the same manner as previously described for establishing the AMRg profiles of complex samples. Host reads in mock microbiome and human fecal sample pools were removed using the human reference genome (GRCh38) while host reads in the turkey-associated sample pools were removed using the Nicholas turkey genome (NCBI RefSeq GCF_000146605.3). As above, reads were then aligned to the MEGARes 2.0 database with default gene coverage of 80% and analyzed through the rest of the AMRPlusPlus Bioinformatic Pipeline. The output count matrices were used for downstream analysis, all performed in R v4.4.0. An overview of the workflow to this point is outlined in Figure 10. As with establishing baseline AMRg profiles for complex samples, an RPM threshold was applied to denote if a gene was considered present or absent. To determine the appropriate RPM threshold, a receiver operator curve was generated using the E. coli K12 control. Iterating through possible RPM thresholds 1-50,0000, all possible true and false positive rates were plotted, yielding a ROC curve with AUC=0.925 (Figure 16). We identified an optimal RPM threshold of 23. Following within-sample AMRg RPM calculations using the filtered and host decontaminated reads, the RPM threshold of 23 was then applied to create a presence-absence binary wherein AMRg with RPM < 23 were considered absent. AMRg above this threshold were considered present. For all samples, AMRg were denoted as true or false positives or negatives based on if genes were 1) detected based on the RPM threshold, 2) in the rhAMR primer panel used, and 3) expected in a sample’s AMRg profile. Sensitivity, specificity, and accuracy were calculated by primer panel, sample pool, and combinations thereof. Principal component analysis and within- sample AMRg relative abundances were used respectively to assess rhAMR’s ability to differentiate samples and to compare expected AMRg profiles to those generated via rhAMR. Results Primer panel and pool design Accounting for differences in primer compatibility while maximizing target inclusion, the final assay design included three panels (p1-3) covering 7,371 of the original 7,885 sequence accessions in MEGARes 2.0. Specifically, p1, p2, and p3 panels account for 6,196, 3,596, and 7 accessions each, covering 1,258, 335, and 5 AMRg groups, respectively. There is no AMRg target redundancy across panels; however, as some targets cover different lengths of same accession, some AMRg accessions and AMRg groups are covered in multiple panels. Specific accessions excluded from these panels due to incompatibility during assay development include 32 AMRg groups. Nevertheless, the antimicrobial classes associated with these groups are still covered in the final rhAMR panels via other AMRg groups within the same classes. Overall, five base mock microbiome pools were created: all 29 strains (N=29; MM1), Campylobacter spp. only (N=10; MM2), S. sonnei (N=6) and E. coli (N=1; MM3), S.enterica (N=9) and E. coli (N=2; MM4), and a 10 strain pool with Campylobacter spp. (N=3), E. coli (N=2) and S. enterica (N=5; MM5). For each base mock microbiome pool and all metagenomic samples (Turkey, N=8; Human, N=8), log10-fold serially diluted spike-in and leave-one-out pools were produced. In total, this yielded 447 primer panel-pool combinations for assessing rhAMR. rhAMR product sequence evaluation Forty samples (8.9%) failed sequencing. All were products from the smallest panel, p3, and thirty-seven of them were metagenomic pool variations. Ten additional p3 panel products, while they did not fail sequencing, had insufficient reads to be processed through the AMRPlusPlus Bioinformatic Pipeline. Of the original 447 samples, 397 were used for downstream analysis. Among these, sequencing reads averaged 155,128 per sample, ranging from 2-2,552,250 reads (Table 4). By primer panel (p1-3), sequencing reads averaged 352,320, 57,531, and 548, respectively. By sample type, mock microbiome samples averaged 338,632 reads/sample, turkey-associated complex samples averaged 82,227 reads/sample, and human- associated complex samples averaged 87,914 reads/sample (Table 4). The single strain E. coli K12 control averaged 829,325 reads. Overall, 92.2% of raw reads, averaging 142,986 reads per sample, were successfully mapped to the MEGARes v2.0 database (Table 4). By primer panel (p1-p3), recovery rates, or the proportion of mapped reads from total raw reads, were 91.4%, 96.6%, and 100%, respectively. When comparing the AMRg recovery rate of rhAMR products versus traditional metagenomic sequence data when both are uniformly processed through AMRPlusPlus, the recovery rate in rhAMR was substantially higher than traditional metagenomic sequencing. For example, the highest recovery rate in among the traditionally metagenomic sequenced samples was 1.3% where its rhAMR-based counterpart yielded a recovery rate of 99.9% (Table 5). rhAMR distinguishes mock microbiome communities with high specificity and accuracy rhAMR performance was assessed using sensitivity, specificity, and accuracy wherein gene detection was defined by an RPM greater than the calculated threshold of 23 RPM, and a gene had to be in both 1) the primer panel used and 2) in the pool’s aggregated whole-genome sequence AMRg profile to be considered expected. Based on these definitions of expected and detected, the overall false positive rate among mock microbiome samples was 7.7% while the overall false negative rate was 6.1%. Specific incidence of true and false positive and negatives by gene group within pool from panel 1 are available in Figure 17. For the largest panel, p1, rhAMR accuracy ranged from 77.4-87.4%, specificity from 83.4-90.4%, and sensitivity from 59.7-85.5%. Performance with p2 and p3 panels are detailed in Table 9. In the single strain Escherichia coli K12 control, sensitivity, specificity, and accuracy were 98.0%, 94.1%, 95.2%, respectively. When comparing AMRg profiles generated with the most comprehensive rhAMR panel (p1) to those expected based on WGS, rhAMR allowed for differentiation of the subset mock microbiome pools MM2-MM5 (Figure 11) with MM1 and MM5, the pools accounting for the most diverse strain subsets, being most central to all other pool clusters (Figure 11A). Relative to the WGS expected profiles, the within pool relative abundance of aminoglycoside and tetracycline resistance gene groups was elevated in rhAMR (Figure 11B). Metagenomic samples with distinct AMRg profiles are differentiated by rhAMR rhAMR performance in complex samples was assessed using the same criteria as in the mock microbiome pool for sensitivity, specificity, and accuracy. Based on this, the overall false positive rates were 1.47% and 1.02% in turkey- and human-associated samples, respectively, with corresponding false negative rates of 7.53% and 3.64%. As in the mock microbiome pools, in metagenomic samples rhAMR specificity was higher than sensitivity (Table 7), and while sensitivity was generally lower in complex samples relative to mock microbiome pools, specificity was higher. Across all panels, the lowest specificity rate in metagenomic samples was 94.2%, and in rhAMR pools the lowest was and 81.4%. A proportion of sample AMRg profiles were comprised of genes not covered in any panel, denoted by grey in Figures 12A and 13A. Upon filtering these based on the gene groups in p1, compared to metagenome-based AMRg profiles, rhAMR-generated profiles showed a higher relative abundances of aminoglycoside, nucleoside, and MLS resistance groups (Figure 5-6). Regarding rhAMR’s ability to differentiate communities based on AMRg diversity, the metagenomic samples with relatively distinctive AMRg profiles formed more distant clusters than those with similar profiles (Figures 12C, 13C). For example, in the human-associated samples, H4 has a relatively higher proportion of glycopeptide resistance group genes (Figures 12A-B) and is more differentiable than other samples (Figure 12C). Similarly, this is evident in the turkey-associated metagenomic samples, T1 and T5 (Figure 13). Nanopore sequencing is suitable for rapid data acquisition. Both Illumina Sequencing and Nanopore yielded AMR data in the comparison experiments. While the throughput of Illumina was higher, it was possible to obtain accurate results through the Nanopore method (Figure 14), 96 unique genes were detected with 67.0% sensitivity and 96.9% specificity, as compared to 83.5% sensitivity and 96.7% specificity of Illumina method (Table 8). Assay optimization is required for limit-of-detection proof-of-concept experiments The limit-of-detection and leave-one-out components of these proof-of-concept experiments yielded inconsistent result across and within sample pools. Due to having no technical replicates, possible issues in target amplification, library prep or sequencing in a given sample would preclude analysis of that panel-pool combination in the context of detection limit. These aberrant results occurred across all panels and all pools (Figure 15). As a result, we were unable to concretely establish rhAMR detection limits at this step of assay development. Beyond the scope of this preliminary proof-of-concept work, future experiments will apply optimized primer panels and include multiple replicates to account for experimental variability. Table 3 StudyID Species SRA_Accession BioSample Strain CA1 Campylobacter coli SRR16256409 SAMN20838409 PS00312 CA2 Campylobacter jejuni SRR16256375 SAMN20838441 PS00344 CA3 Campylobacter jejuni SRR16256392 SAMN20838425 PS00328 CA4 Campylobacter jejuni SRR16256397 SAMN20838420 PS00323 CA5 Campylobacter jejuni SRR16256431 SAMN20838389 PS00273 CA6 Campylobacter jejuni SRR16256447 SAMN20838375 PS00258 CA7 Campylobacter jejuni SRR16256462 SAMN20838361 PS00242 CA8 Campylobacter jejuni SRR16256467 SAMN20838347 PS00228 CA9 Campylobacter spp. SRR16256350 SAMN20838465 PS00369 CA10 Campylobacter spp. SRR16256388 SAMN20838428 PS00331 K12 Escherichia coli SRR5364300 SAMN06624121 ECK12_ATCC EC1 Escherichia coli SRR7613598 SAMN09726418 PSU-0863 EC2 Escherichia coli SRR7613605 SAMN09725162 PSU-0803 EC3 Escherichia coli SRR7613625 SAMN09725136 PSU-0798 EC4 Escherichia coli SRR7613740 SAMN09725146 PSU-0770 SE1 Salmonella enterica SRR6000471 SAMN07571561 SC-14 SE2 Salmonella enterica SRR6113232 SAMN07571641 SC-23 SE3 Salmonella enterica SRR6219168 SAMN07509479 SC-37 SE4 Salmonella enterica SRR6219170 SAMN07571646 SC-30 SE5 Salmonella enterica SRR6219206 SAMN07571570 SH-03 SE6 Salmonella enterica SRR6219294 SAMN07571600 SH-11 SE7 Salmonella enterica SRR6219306 SAMN07571579 SC-02 SE8 Salmonella enterica SRR6427282 SAMN08273618 SH-01 SE9 Salmonella enterica SRR7353158 SAMN07509468 SC-01 SS1 Shigella sonnei SRR5990599 SAMN07571594 PSU_0216 SS2 Shigella sonnei SRR6113231 SAMN07571567 PSU_0213 SS3 Shigella sonnei SRR6114438 SAMN07571571 PSU_0211 SS4 Shigella sonnei SRR6219657 SAMN07571584 PSU_0197 SS5 Shigella sonnei SRR6219723 SAMN07571618 PSU_0205 SS6 Shigella sonnei SRR6220265 SAMN07571614 PSU_0203

Table 4. Mapped Reads Raw Reads (Counts) Mapped Reads (Counts) Min Mean Median Max Min Mean Median Max

T6 2 80110 23968 634165 2 80049 23962.0 634099 T7 6 150630 13750 1287115 4 150030 13721.0 1286939 T8 2 53119 7934 447026 2 52877 7263.0 446983 Table 5. Amr Read Recovery Rates MG vs rhAMR for panel 1 Metagenome (%) rhAMR (%) Human-Associated H1 0.88 99.99 H2 0.78 99.85 H3 0.83 99.99 H4 0.94 99.68 H5 1.18 99.99 H6 0.96 99.93 H7 0.97 99.95 H8 1.00 99.35 Turkey-Associated T1 0.12 99.72 T2 0.87 99.98 T3 0.86 99.99 T4 0.92 99.99 T5 0.08 97.42 T6 0.96 99.98 T7 1.26 99.98 T8 0.89 99.97

Table 6. Performance Mock Microbiome: Panel 1 Sensitivity (%) Specificity (%) Accuracy (%) Panel 1 (p1) Escherichia coli K12 98.0 94.1 95.2 MM1 - All Strains 83.2 90.4 86.3 MM2 - Campylobacter spp. 68.9 85.5 80.0 MM3 - Salmonella enterica 85.5 88.9 87.4 MM4 - Shigella sonnei 59.7 89.4 77.4 MM5 - All Strains Subset 82.1 83.4 82.7

Table 7. Performance Complex Samples: Panel 1 Sensitivity (%) Specificity (%) Accuracy (%) Human-Associated H1 58.8 98.6 91.9 H2 59.2 99.0 94.2 H3 56.1 98.8 91.2 H4 59.2 99.3 93.3 H5 50.4 98.9 90.9 H6 61.9 98.6 94.7 H7 57.6 99.0 94.6 H8 64.0 98.8 95.6 Turkey-Associated T1 33.3 97.9 84.8 T2 50.2 97.5 73.3 T3 51.5 97.7 73.9 T4 53.8 97.8 72.9 T5 49.1 98.8 86.0 T6 45.4 97.0 69.5 T7 55.5 96.9 70.1 T8 49.6 97.9 72.6

Table 8. Illumina versus Nanopore comparisons Sensitivity (%) Specificity (%) Accuracy (%) Illumina

Table 9. Performance with p2 and p3 panels Sensitivity (%) Specificity (%) Accuracy (%) Panel 2 (p2) Escherichia coli K12 28.6 87.0 84.5 MM1 - All Strains 70.0 89.9 86.3 MM2 - Campylobacter spp. 62.2 81.4 79.6 MM3 - Salmonella enterica 50.9 94.9 89.3 MM4 - Shigella sonnei 51.2 94.9 89.0 MM5 - All Strains Subset 53.5 92.5 86.3 Panel 3 (p3) MM1 - All Strains 0.0 98.2 95.8 MM2 - Campylobacter spp. 22.2 91.7 90.6 MM3 - Salmonella enterica 10.5 95.2 94.2 MM4 - Shigella sonnei 8.3 92.1 91.2 MM5 - All Strains Subset 7.4 87.3 85.5 Table 10. Primer Sequences Tested In Vivo, Set One Sequence Name Bases Tm (50mM NaCl) C

RH.9377F088F5E9413Z0Z.F 22 63.35394742 RH.48F8BBA2DE634B6Z0Z.F 25 59.34355028

RH.2FAD5B6C52C54E0Z0Z.F 25 58.03013369 RH.F0F23F3367E54D6Z0Z.F 27 59.39773183

RH.F663F2D0DF8447CZ0Z.F 27 58.47907698 RH.B13AAA4CF85748DZ0Z.F 27 56.17096552

RH.D78FCAF790FC4B9Z0Z.F 21 66.29207953 RH.3836E94E58AF44BZ0Z.F 28 59.835741

RH.80FC84E0C138469Z0Z.F 23 63.3758322 RH.6FABE6C91F5C413Z0Z.F 26 57.9561353

RH.90FF43B1AA16458Z0Z.F 22 62.84707374 RH.CCC553AF4332467Z0Z.F 28 57.88732512

RH.C4D388E86A894DCZ0Z.F 26 60.69626134 RH.A87F690199B643AZ0Z.F 26 63.13058947

RH.EFA87DEC5DCD4E6Z0Z.F 23 60.50356469 RH.F817C5C57397452Z0Z.F 30 57.41092341

RH.A5D29C545CA2409Z0Z.F 24 59.34858202 RH.626CCBE3AD7A486Z0Z.F 24 60.55246094

RH.B169332E08D2437Z0Z.F 31 57.90908235 RH.0F71A9528A1C4ADZ0Z.F 24 59.89180619

RH.8D9C01EFDCEA455Z0Z.F 24 58.14150369 RH.DDC2CEE4ABBF4FAZ0Z.F 21 62.08287723

RH.6AFCC17C4DE24C0Z0Z.F 23 61.34953638 RH.97B27B1E6A774B2Z0Z.F 29 59.61214471

RH.BAA5F418E98F454Z0Z.F 25 59.89555423 RH.C71071517C5A436Z0Z.F 27 58.56840361

RH.48F8BBA2DE634B6Z0Z.R 26 56.57514842 RH.8A26CCF742744C1Z0Z.R 32 58.93674776

RH.F0F23F3367E54D6Z0Z.R 28 58.3396199 RH.DCBE78F01CF541BZ0Z.R 28 58.1952551

RH.B13AAA4CF85748DZ0Z.R 27 58.21138655 RH.315FCC345F26446Z0Z.R 22 61.64882989

RH.3836E94E58AF44BZ0Z.R 28 61.72713989 RH.E84F8D3BF35D4CEZ0Z.R 30 57.92902522

RH.6FABE6C91F5C413Z0Z.R 22 59.35114842 RH.0A78C199BB6A445Z0Z.R 23 62.19067389

RH.CCC553AF4332467Z0Z.R 30 58.94095076 RH.843A99D084C7489Z0Z.R 31 55.12283667

RH.A87F690199B643AZ0Z.R 25 62.78064398 RH.25C2DC664AE940EZ0Z.R 28 59.17350865

RH.F817C5C57397452Z0Z.R 28 57.12018241 RH.7A3FB4F567EE400Z0Z.R 24 61.11970374

RH.626CCBE3AD7A486Z0Z.R 25 59.61785682 RH.1CBF0194C75E46CZ0Z.R 26 57.78688462

RH.0F71A9528A1C4ADZ0Z.R 25 59.58223326 RH.C5A27E3AB4F044CZ0Z.R 25 57.72779973

RH.DDC2CEE4ABBF4FAZ0Z.R 21 63.14368573 RH.B43653FE9E894CCZ0Z.R 23 58.14379037

RH.97B27B1E6A774B2Z0Z.R 28 61.15058355 RH.47F62CCD278944EZ0Z.R 28 56.83319855

RH.C71071517C5A436Z0Z.R 26 56.80519227 RH.F8D83016F8A04E2Z0Z.R 26 58.22455253

RH.8C9B390A063945EZ0Z.F 25 59.66260023 RH.4BA3BCA48F4442CZ0Z.F 25 60.56360796

RH.30ACDA691714460Z0Z.F 28 57.17976614 RH.03D35237A0504D9Z0Z.F 23 60.4427397

RH.FA056A38E31148BZ0Z.F 26 56.61469161 RH.796E5A9051D241CZ0Z.F 21 62.35458067

RH.5F82CBF89C13494Z0Z.F 30 57.79125568 RH.353AFBAC8C954D3Z0Z.F 27 59.15796255

RH.F2E22BAB32234C4Z0Z.R 22 63.51347952 RH.3C85B83F28294F8Z0Z.R 29 57.82664458

RH.9333799A9CBB470Z0Z.R 23 60.52418576 RH.45A924B407F444FZ0Z.R 23 61.24121781

RH.0F3DA9EBB9BB48FZ0Z.R 25 58.91524679 RH.9EBB37B076EC4BFZ0Z.R 23 60.21446043

Table 11. Primers Tested In Vivo, Set Two Sequence Name Bases Tm (50mM NaCl) C

Lastly, it should be understood that while the present disclosure has been provided in detail with respect to certain illustrative and specific aspects thereof, it should not be considered limited to such, as numerous modifications are possible without departing from the broad spirit and scope of the present disclosure as defined in the appended claims. It will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the invention. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the methods disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims. ^REFERENCES Dobosy, J. R. et al. 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Claims

CLAIMS What is claimed is: 1. A method of detecting two or more antimicrobial resistance (AMR) gene targets in a biological sample, the method comprising: (a) providing a reaction mixture comprising (i) two or more oligonucleotide primer pairs, wherein each primer pair is specific for an AMR gene target, and further wherein each primer has a cleavage domain positioned 5' of a blocking group and 3' of a position of hybridization with a target nucleic acid, wherein the blocking group is linked at or near the end of the 3 '-end of the oligonucleotide primer, wherein the blocking group prevents primer extension and/or inhibits the primer from serving as a template for DNA synthesis, (ii) a biological sample comprising two or more target nucleic acids, (iii) a cleaving enzyme, and (iv) a polymerase; (b) exposing the two or more oligonucleotide primer pairs to the biological sample, wherein if one or more target nucleic acids are present in the sample, at least one primer and at least one target form a hybridized target/primer substrate; (c) cleaving the one or more primers of the hybridized primer/substrate of step b) with the cleaving enzyme at a point within or adjacent to the cleavage domain to remove the blocking group from the primer; (d) extending the primer with the polymerase, thereby obtaining amplified target nucleic acid; (e) barcoding each amplified target nucleic acid with a unique barcoded adapter to produce barcoded samples; and (f) sequencing the barcoded samples of step (e), thereby detecting AMR gene targets.

2. The method of claim 1, wherein an additional step (g) can be performed, wherein the detected AMR gene is compared to a sequence database of microbes, thereby determining which microbe is the source of the AMR gene.

3. The method of claim 1 or 2, wherein Nanopore sequencing is used.

4. The method of any one of claims 1-3, wherein the biological sample is obtained from a microbiome sample.

5. The method of claim 4, wherein the microbiome sample is from a plant, animal or the environment.

6. The method of any one of claims 1-5, wherein the reaction mixture comprises ten or more primer pairs.

7. The method of claim 6, wherein the reaction mixture comprises twenty or more primer pairs.

8. The method of claim 7, wherein the reaction mixture comprises one hundred or more primer pairs.

9. The method of any one of claims 1-8, wherein the cleaving enzyme is a hot start cleaving enzyme which is thermostable and has reduced activity at lower temperatures.

10. The method of any one of claims 1-9, wherein the cleavage domain is comprised of one or more 2' -modified nucleosides, and the cleavage enzyme cleaves between the position complementary to the variation and the one or more modified nucleosides.

11. The method of any of claims 1-10, wherein the antimicrobial resistance gene is from a prokaryotic organism.

12. The method of claim 11, wherein the prokaryotic organism comprises a bacterial organism, fungal organism, archaeal organism, or combinations thereof.

13. A set of primers useful for carrying out the method of claim 1.

14. A method of treating a subject with an antibiotic resistant gene, the method comprising determining which antibiotic resistance genes the subject has by using the method of claim 1, and treating the subject accordingly.

15. A kit comprising two or more primers for detecting antibiotic resistance genes, wherein the two or more primers are designed to work with the method of claim 1.

16. The kit of claim 15, wherein the kit further comprises other reagents for amplification and/or sequencing.

17. The kit of claim 16, wherein all reagents are available as shelf-stable lyophilized mix.

18. The kit of claim 17, wherein the reagents are stable at room temperature.