For Review Only · 2017. 7. 25. · For Review Only 1 Genomic approaches to characterizing and reducing antimicrobial resistance in beef cattle production systems Cassidy Klima 1,2,

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Genomic approaches to characterizing and reducing

antimicrobial resistance in beef cattle production systems

Journal: Canadian Journal of Animal Science

Manuscript ID CJAS-2016-0208.R1

Manuscript Type: Review

Date Submitted by the Author: 16-Jan-2017

Complete List of Authors: Klima, Cassidy; University of Calgary Cumming School of Medicine Cameron, Andrew; Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre Afzal Javed , Muhammad; University of Calgary Cumming School of Medicine, Infectious Disease Alexander, Trevor; Agriculture and Agri-Food Canada, Lethbridge Research

and Development Centre Zaheer, Rahat; Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre Munns, Krysty; Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre McAllister, Tim; Agriculture and Agri-Food Canada, Lethbridge Research Centre

Keywords: Genomics, Antimicrobial Resistance, Beef Cattle, Metagenomics, CRISPR-Cas

https://mc.manuscriptcentral.com/cjas-pubs

Canadian Journal of Animal Science

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Genomic approaches to characterizing and reducing antimicrobial resistance in beef cattle

production systems

Cassidy Klima1,2, Andrew Cameron1, Muhammad Afzal Javed2, Trevor Alexander1, Rahat

Zaheer1, Krysty Munns1 and Tim A. McAllister1*

1Lethbridge Research and Development Center, Agriculture and Agri-Food Canada, Lethbridge,

AB. Canada, T1J 4B1.

2Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada

T2N 4N1

*Corresponding author.

[email protected]

Short title: Genomic approaches to reducing antimicrobial resistance.

Keywords: antimicrobial resistance, genomics, cattle, beef, metagenomics, CRISPR-Cas.

Abbreviations: ARG-annot, Antibiotic Resistance Gene Annotation; AMR, antimicrobial

resistance; AMU, antimicrobial use; AMPs, antimicrobial peptides; ARGs, antimicrobial

resistant genes; AST, antimicrobial sensitivity testing; BRD, bovine respiratory disease; CARD,

Comprehensive Antimicrobial Resistance Database; CRISPR, clustered regularly-interspaced

short palindromic repeats; DDJP, DNA Data Bank of Japan; EMBL, European Molecular

Biology Laboratory; ESBL, extended spectrum β-lactamases, GC, guanine-cytosine; ICE,

integrative conjugative elements; IncA/C, incompatibility group A/C; INseq, insertion

sequencing; HITS, high-throughput insertion tracking by deep sequencing; LEE, locus of

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enterocyte effacement; mecA/C, methicillin resistance gene A/C; MDR, multidrug resistant;

MGEs, mobile genetic elements; MG-RAST, metagenomics rapid annotation using subsystem

technology; MLST, multi-locus sequence typing; MRSA, methicillin resistant Staphylococcus

aureus; NK, natural killer; NGS, next generation sequencing; NCBI, National Centre for

Biotechnology Information; ND, neighbourhood distance; NDF, neutral detergent fibre; OM,

organic matter; PCR, polymerase chain reaction; PFGE, pulsed-field gel electrophoresis;

PICRUSt, phylogenetic investigation of communities by reconstruction of unobserved states;

SNPs, single nucleotide polymorphisms; TAP, tracheal antimicrobial peptide; TIS, transposon-

insertion sequencing; Tn-seq, transposon sequencing; TraDIS, transposon-directed insertion

site sequencing; T3SS, type III secretion system; WGS, whole genome sequencing.

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Klima, C., Cameron, A., Javed, M. A., Alexander, T., Zaheer, R., Munns, K. and McAllister, T.A.

2016. Genomic approaches to characterizing and reducing antimicrobial resistance in

beef production systems. Can. J. Anim. Sci. XX: YY-ZZ. Antimicrobial resistance (AMR) is a

global health threat and a standstill in the discovery and design of new antibiotics has been

linked to the growing number of human deaths attributed to AMR infections. Intensive beef

production utilizes antimicrobials to promote health and growth efficiency. To understand the

magnitude and risk of AMR in beef production, it is important to assess the prevalence and

diversity of antimicrobial resistant genes (ARGs) within microbial populations. Antimicrobial

resistant bacteria are traditionally identified by isolation and growth in the presence of selective

antibiotics. Whole-genome, metagenomic, and RNA sequencing provide new avenues to detect

and identify novel ARGs in both culturable and unculturable bacterial communities. Some of

these approaches place ARGs within the context of mobile genetic elements, gauging their

likelihood of transfer across genomes. Genomics can also mitigate AMR, contributing to rational

drug design or the development of alternatives to antimicrobials such as vaccines and

probiotics. RNA-seq-based transcriptomics and Tn-seq may provide new ways to examine

mechanisms that promote or prevent AMR. Finally, CRISPR-Cas gene-editing could directly

reduce AMR by killing AMR-resistant bacteria without harming beneficial bacteria. Together,

these technologies may provide new opportunities to identify, quantify, and mitigate AMR while

developing alternatives to antimicrobials for beef production.

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ANTIMICROBIAL RESISTANCE AND USAGE IN BEEF CATTLE

The discovery of antibiotics (antimicrobials) was one of the most significant developments in

contemporary science. Prior to the 1940s, infectious disease was the predominant cause of

human death (Jones et al., 2012). In the current era of antimicrobial therapy, the majority of

bacterial infections in both humans and animals can be successfully treated. Despite this, the

effectiveness of antimicrobial therapy is threatened by the emergence of antimicrobial resistant

(AMR) bacteria, and constrained by the limited number of new and novel antimicrobials in the

discovery pipeline.

Antimicrobial resistance is a global concern for both human and veterinary medicine.

The use of antimicrobials in livestock production is contentious because of the potential for the

genesis and transmission of AMR genes (ARGs) between both veterinary pathogens and

zoonotic pathogens that may threaten humans. Resistance to multiple antimicrobial families is a

serious issue, with highly mobile genetic elements that contain a number of ARGs of particular

concern. Generally, the presence of AMR is best-studied in clinically important microbes from

hospitals, veterinary medicine and food samples. However, ARGs can be carried by non-

pathogenic or environmental bacteria, which may serve as reservoirs of these genes that can be

acquired by pathogens. The total ARG repertoire of any microbial population is termed the

resistome. Knowledge of the resistome may aid in the development of strategies to mitigate

AMR on a broader scale and at higher, managerial levels of animal production.

Globally, ~80% of antimicrobials manufactured are given to livestock (Huttner et al.,

2013). This reflects both the size of animal populations in production systems as well as the

mass of individual animals and the antimicrobial use needed to maintain animal health in large,

confined feeding operations. For beef production, antimicrobials are used for disease

prevention, therapy, and to improve animal growth and feed efficiencies. In-feed antimicrobials

can reduce feed requirements and increase weight gains by 2 - 15%, enhancing on-farm

profitability (Hao et al., 2014). Respiratory pneumonia, foot-rot, hepatic abscesses, and ruminal

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acidosis are some of the most frequent diseases in cattle that necessitate antimicrobial use.

Prevention or resolution of these infections/diseases relies on effective antimicrobials.

Consequently, safeguarding antimicrobial efficacy is critical to sustaining animal health and

profitability in beef production.

Unless there are changes in conventional practises, antimicrobial use in livestock and

poultry production is predicted to rise ~67% by 2030 (Van Boeckel et al., 2015). As a result,

enhanced AMR surveillance will be increasingly important to evaluate specific risks associated

with antimicrobial use (AMU) and to identify intervention strategies that mitigate the

dissemination and long-term enrichment of ARGs. This review highlights recent genomic

advances that have opened new avenues to investigating the abundance and function of ARGs

and also presents ways these technologies can be used to mitigate AMR through the

development of alternatives to antimicrobials for use in beef production.

GENOMICS-BASED TECHNOLOGIES WITH RELEVANCE TO ANTIMICROBIAL

RESISTANCE

Culture-Based Resistance Profiling

Conventional AMR detection employs culture-based, phenotypic assays for antimicrobial

sensitivity testing (AST). These approaches determine the presence and degree of AMR in

culturable bacteria. Culture-based assays are limited in that they do not provide information

about the genetic mechanism of resistance and only through the use of genomic techniques

such as polymerase chain reaction (PCR) and whole genome sequencing (WGS) can the ARGs

conferring resistance be identified and linked to phenotype (Chantziaras et al., 2014). However,

DNA sequence-based gene detection methodologies do not provide information on the level of

resistance as they do not confirm if ARGs are expressed or the functionality of translated

proteins. As bacterial strains can harbour inactive ARGs (Davis et al., 2011), AST will likely

remain an important tool for the characterization of AMR in both agricultural and hospital

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environments. However, developments in high-throughput nucleic acid sequencing

technologies, as described in the following section should enable ARGs in bacteria to be rapidly

characterized in an increasingly cost-effective manner in both chute and bed-side settings. As a

result, we will likely see the widespread adoption of genomics-based AMR detection tools in

both veterinary and medical diagnostics in the near future.

High-Throughput DNA Sequencing

Over the last two decades, there has been a rapid development of high-throughput or

‘next’ or 'second'-generation sequencing (NGS) platforms and associated bioinformatic

software (Köser et al., 2014). Various platforms exist for short-read, single-molecule real-time

long read, and synthetic long read technologies, each varying with regards to cost, error rates

and read structure (Goodwin et al., 2016). Although the requirements of each study will

determine the most relevant platform to use, the Illumina system currently holds the largest

market share of available platforms (Goodwin et al., 2016). The majority of sequence data

published is available for public use via central biotechnology databases including the National

Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov/), the European

Molecular Biology Laboratory (EMBL; http://www.embl.de) and the DNA Data Bank of Japan

(DDBJ; http://www.ddbj.nig.ac.jp).

Advances in sequencing technologies and analysis tools have given rise to the “omics”

science areas including genomics (DNA), transcriptomics (mRNA), proteomics (peptides and

proteins), and inferred metabolomics (metabolites). Together these tools can be used in a high-

throughput manner for comprehensive surveillance and characterization of AMR prevalence and

development and in devising AMR mitigation strategies. Some of the linkages among these

approaches are illustrated in Figure 1, with examples of their applications in the context of AMR

investigations.

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Whole-Genome Sequencing and Comparative Genomics

Whole genome sequencing can be applied to AMR research through the identification of

AMR strains, monitoring the evolution and transmission of AMR bacterial pathogens, facilitating

the development of improved diagnostic screening for ARGs, and through identification of novel

antimicrobials and their targets (Punina et al., 2015). Specialty databases like the Antibiotic

Resistance Genes Database (Liu and Pop, 2009), the Comprehensive Antibiotic Resistance

Database (CARD; McArthur et al., 2013), ResFinder (Zankari et al., 2012), and Antibiotic

Resistance Gene Annotation (ARG-annot; Gupta et al., 2014) are useful tools to screen both

whole- and metagenomic sequence data for previously identified ARGs. In addition,

associations of ARGs with mobile genetic elements (MGEs) including plasmids, transposons,

prophage and integrative conjugative elements (Hegstad et al, 2010) can be characterized

through WGS. Frequently, the lower guanine-cytosine (GC) content of these regions aides in

their identification within host genomes (Sebaihia et al., 2006; Hayek, 2013; Boyd et al., 2001).

Genes required for the mobility and/or integration of mobile elements, including integrases and

transfer genes provide context, potentially indicating sequences that may also contain ARGs

(Sebaihia et al., 2006). Multi-copy plasmids are also observable within sequence information

with the observed read coverage related to plasmid copy number. Recently, WGS was used to

identify 8 novel plasmids carrying ARGs to aminoglycosides, carbapenems, penicillins,

cephalosporins, chloramphenicol, dihydrofolate reductase inhibitors, sulfonamides, and

tetracyclines as well as resistance to heavy metals in Shiga toxin-producing and generic

Escherichia coli strains isolated from animals and humans (Losada et al., 2016). The presence

of plasmids in the strains were identified by the evidence of circularity as determined by mate-

pair read status, difference in read depth, and annotations and sequence similarity associated

with other plasmids. Whole genome sequencing confirmed horizontal gene transfer of the

methicillin resistance gene mecA from Staphylococcus epidermidis to Staphylococcus aureus

(Bloemendaal et al., 2010).

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Comparative genomics can further aid in the identification of previously unknown ARGs

by highlighting differing genomic sequences among resistant and sensitive bacterial isolates.

BLAST-based sequence alignments to the ARG databases described above can potentially

detect previously-identified ARG homologs and variants in strain-specific DNA. This strategy

was used to identify putative ARGs in species of Clostridium difficile, with some of the predicted

genes supported by experimental data, while others were not (Sebaihia et al., 2006). The use of

WGS data to predict resistance phenotype has also been employed with over 95% confidence

in Staphylococcus aureus (MRSA) (Gordon et al., 2014), E. coli, Klebsiella

pneumonia (Stoesser et al., 2013), C. difficile (Eyre et al., 2012), and Pseudomonas

aeruginosa (Kos et al., 2015). Advancements in bioinformatics analyses will likely result in WGS

becoming the standardized tool for the detection of AMR bacterial strains within clinical settings

(Punina et al., 2015). Comparative genomics has also been used to study AMR in bacteria

isolated from beef cattle. For example, MGEs known as integrative and conjugative elements

(ICE) were identified in strains of Mannheimia haemolytica originating from cases of Bovine

Respiratory Disease (BRD) (Klima et al., 2016). Some of the ICE harbored extensive multidrug

resistance cassettes carrying up to 11 AMR genes directly threatening the efficacy of veterinary

antimicrobial therapies used to treat respiratory infections in cattle (Klima et al., 2016). Similarly,

NGS and comparative genomics of four broad-host-range incompatibility group A/C (IncA/C)

plasmids extracted from E. coli demonstrated that this MGE was responsible for encoding

resistance to florfenicol, tetracycline and extended spectrum β-lactam isolates from dairy cows,

pigs and turkeys in the USA and Chile (Fernández-Alarcón et al., 2011).

Whole genome sequencing and comparative genomics are also useful for assessing the

mobility, transfer and dissemination of ARGs within bacteria associated with beef cattle and

their surrounding environment. Previously, pulsed-field gel electrophoresis (PFGE) and multi-

locus sequence typing (MLST) were the primary tools for source tracking of AMR bacteria. The

ever-decreasing cost of sequencing, coupled with the increasing availability of tools to support

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data handling, is leading to WGS becoming a primary tool for studying the epidemiology of

ARGs. For example, WGS has been used to examine the transfer of ARG containing bacteria

between animals and humans. A longitudinal study utilizing WGS showed that MDR

Salmonella enterica subsp. Typhimurium infections in humans were not linked to transmission

from animals and the phylogenetic relationships both within and between the resistance

determinants and host bacteria differed (Mather et al., 2013). However, recent work using MLST

and WGS suggests that livestock-associated methilicin resistant Staphylococcus aureus

(MRSA) CC398 originated from methicillin-susceptible S. aureus in humans (Price et al., 2012),

while a separate study linked MSRA strains in humans carrying mecC to a livestock reservoir

(Harrison et al., 2013). These studies highlight the movement of bacteria and the ARGs they

contain from and amongst human and animal sources and point to the role WGS will likely play

as a future tool to better characterize the transmission of AMR.

Currently, the largest barriers towards the widespread application of WGS in AMR beef

cattle research lies in the cost. However, as sequencing technologies continue to evolve, costs

are expected to continue to plummet, outstripping Moore’s Law to the point where in the next 10

years, analyzing the abundance of sequence data will be the rate limiting step in AMR science.

Research will likely focus on the expansion of the bioinformatic tools that will enable those that

are not specialized in bioinformatics and computer programing to analyze and interpret these

data. However, as sequence databases continue to grow and specialty AMR databases

expand, WGS will become increasingly important to AMR surveillance in both hospital and

livestock production settings.

Metagenomic Sequencing

While WGS applications typically examine individual bacterial strains, metagenomics

examines DNA extracted from an entire microbial community. Metagenomic sequencing, in

combination with data analysis tools such as MG-RAST (Metagenomics Rapid Annotation using

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Subsystem Technology; Meyer et al., 2008) provide information on all known genes within a

sample and allow for the examination of a community of genes based on their function. Three

types of metagenomics studies are commonly conducted (i) single/marker-gene amplicon

sequencing studies (e.g., 16s rRNA genes), (ii) shotgun, or whole metagenomics sequencing,

and (iii) environmental clone libraries (functional metagenomics). Although 16S rRNA gene

sequencing has been widely used to predict the structure of microbial communities using

bioinformatic software packages such as PICRUSt (Phylogenetic Investigation of Communities

by Reconstruction of Unobserved States; Langille et al., 2013), it does not generate information

on the ARGs within a bacterial population. In contrast, whole metagenomics sequencing

provides thorough genetic information of microbial communities, describing their complete

genetic potential, including the prevalence of ARGs and their genetic variation.

Though this technique may be used to identify known ARGs it usually lacks the ability to

link ARGs to specific bacterial species (D’Costa et al., 2006), unless samples are sequenced to

a depth that enables bacterial genomes to be assembled. Despite this, metagenomics is an

efficient way to survey the resistome in animal production environments. Composite samples

collected from the digestive tract, animal tissues, feces, and the surrounding environment are

particularly useful for using metagenomics to detect ARGs throughout livestock production

systems. For example, sequencing the rumen microbial populations in Indian buffalo and the

fecal populations of cattle revealed that only 6.4% and 8.4%, respectively, of the sequence data

coded for virulence traits or ARGs (Durso et al., 2011; Singh et al., 2012). Metagenomic

sequencing and analysis of ARGs in dairy cow feces after therapeutic administration of a third

generation cephalosporin revealed an increase in sequences associated with resistance to β-

lactams and multidrug resistance within three days after treatment (Chambers et al., 2015).

Metagenomics of pig, chicken, and human fecal microbial communities also detected ARGs

conferring tetracycline, erythromycin, and aminoglycoside resistance, indicating that bacteria

from chickens harbored the greatest ARGs and associated MGEs (Ma et al., 2016).

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Metagenomics may also be used to evaluate the risks associated with AMR in the food

chain. For example, the resistome of manure, soil, and wastewater from dairy and beef

production systems was examined by aligning shotgun metagenomic reads to a custom non-

redundant database of ARGs sequences compiled from ARG databases: ARG-ANNOT,

Resfinder and CARD (Noyes et al., 2016). The majority of resistance-associated sequences

coded for tetracycline resistance (Noyes et al., 2016). Samples taken on ranches contained

fewer ARGs than dairy or feedlot samples. Of interest, the resistome associated with dairy

operations differed from beef cattle feedlots (Noyes et al., 2016). A similar type of analysis

applied to samples collected from environments impacted by livestock and municipal waste,

showed that the overall abundance of ARGs in human, cattle and swine waste streams was

similar (Agga et al., 2015). However, human waste discharged from municipal wastewater

treatment plants contained more ARG diversity compared to waste from livestock environments

(Agga et al., 2015). Elsewhere, comparisons of fecal DNA isolated from swine reared with in-

feed antimicrobials (chlortetracycline, sulfamethazine, and penicillin), or without antimicrobials,

showed that antimicrobials selected for AMR microbes (Looft et al., 2012). Furthermore, co-

selection for aminoglycoside resistance was evident, despite the fact that aminoglycoside were

not included in feed (Looft et al., 2012).

Studies examining the effects of antimicrobial use on the abundance and diversity of

ARGs in livestock systems aid in evaluating the risks of AMU in food production. Despite this, it

is important to note that metagenomic analyses do not directly determine if the presence of an

ARG contributes to phenotypic resistance. Metagenomic sequence analysis is almost entirely

limited to the identification of known ARGs or close variants. Thus, identification of novel ARGs

can be difficult, and the genomic context in terms of linkages or association with MGE of those

ARGs identified may not be discernable if assembly scaffolds are not large enough. Recent

advancement in the generation of long sequence reads via the high-throughput Illumina TruSeq

long-read hybrid subassembly technology (White et al., 2016) may be helpful in resolving this

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issue as assemblies using this technology have resulted in more than 10,000 contigs over 10

kbp in length and have successfully assembled complete genomes from complex soil

metagenomes.

Metagenomics is likely to be an important tool for the investigation of AMR in beef cattle

research in the future. As long-read technologies advance, resulting in more robust assemblies

and/or completed genomes from metagenomic samples, it is possible that metagenomics could

replace much of the culture based work used to survey AMR in the feedlot environment. Efforts

will need to be directed towards improving and building on the curated AMR databases used to

identify ARGs from veterinary samples. Currently there is a strong bias in these towards

clinically derived ARGs. As with all of the genomic based technologies, as the cost of

sequencing goes down, there will be the need to develop more efficient and user friendly

analytical pipelines to manage the large datasets produced.

Nucleic Acid Hybridization Based Pull-Down Approaches

Although metagenomic sequencing provides the capacity to generate sequence data

from entire communities, there are some cases were information from only specific types of

sequences (i.e., ARGs) is required and the additional sequence data obtained results in

unnecessary computational burden. In these circumstances a hybridization based bait pull

down approach, often referred to as target enrichment or target capture, can be applied to

selectively screen samples prior to sequencing. In this strategy, custom sequence capture

probes, either in an array or solution target sequences of interest. In the array method,

microarrays containing probes complimentary to the regions of interest are used to bind and

purify DNA (Bodi et al., 2013). In the solution-based method, biotinylated DNA or RNA baits are

used to bind targets that are then purified using streptavidin-labelled magnetic beads (Bodi et

al., 2013). Ultimately, target enrichment provides advantages when sequence data unrelated to

the topic of interest only increases cost and the complexity of bioinformatic analyses.

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As with most hybridization-based assays, pull-down approaches necessitate prior

knowledge of the DNA sequence of interest for probe design. Thus, this strategy cannot be

used to enrich for novel ARGs, but can concentrate known DNA sequences (e.g., low

abundance ARGs and ARG variants). This technique can also purify DNA from samples where

amplification or sequencing is hindered by inhibitors, such as the humates found in soil

(Yankson and Steck, 2009). Pull-down techniques can also be used for studying ARG

expression when mRNA is isolated from the samples. For example, an mRNA solution-based

pull down assay was developed for detection of extended-spectrum β-lactamases (ESBL) blaOXA

transcripts and was able to discriminate between the ESBL blaOXA and carbapenemases (Brandt

et al., 2016). The same assay also enabled semi-quantitative detection of highly expressed

insertion sequences associated with MGEs (Brandt et al., 2016).

These pull-down/enrichment techniques may be advantageous for use in beef cattle

research due to their ability to overcome sequencing inhibitors and concentrate genes of

interest in the presence of high levels of contaminating bovine DNA. Furthermore, their ability to

reduce sequencing costs may make it more palatable to use metagenomics in large scale AMR

surveillance programs. To be successfully applied to beef AMR research, probes will need to

be designed that target ARGs common to agricultural settings, information currently limited in

AMR databases. Enrichment techniques are likely to be more applied in the future as the

increased frequency of sequenced based AMR beef research supports the generation of more

comprehensive AMR databases.

Functional Screening of Metagenomics Libraries

Functional metagenomics is a tool that utilizes metagenomic data to describe gene

function and interactions, or more simply, how those genes in a sample translate into protein

products. The functional analysis of a gene involves the construction and screening of extracted

DNA gene cloning libraries that are expressed in a heterologous host to enable phenotypic

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screening. This metagenome wide approach is higher throughput than traditional methods that

clone individual genes from single isolates.

Functional metagenomics can be used to examine AMR phenotypes conferred by

previously uncharacterized genomic material. For this technique, amplified or native

metagenomic DNA fragments from microbial communities are cloned into plasmids (inserts of

<10 kb), fosmids (25 - 45 kb), cosmids (15 - 40 kb), or bacterial artificial chromosomes (100 -

200 kb inserts), and expressed in a suitable host such as E. coli. Transformants are screened

using phenotypic assays to determine whether an AMR phenotype is conferred by expression

products (Ekkers et al., 2012). Once clones expressing a phenotype of interest are isolated, the

genes responsible can be identified via NGS and in silico searches against ARG databases. If

homology searches are inconclusive, knockout mutagenesis or sub-cloning can be employed to

determine which gene within the DNA fragment is responsible for the AMR phenotype. Thus,

functional metagenomics can facilitate the discovery of novel ARGs within the resistome (Fouhy

et al., 2015).

Although functional metagenomic libraries provide a convenient method to screen for

ARGs, they are influenced by the relative abundance of members within the microbial

community, and it is possible to miss rare members that contribute a miniscule amount of DNA

to library preparation. In addition, some ARGs may not express properly in heterologous hosts

due to codon preference, preventing their identification (Fouhy et al., 2015). For example,

although E. coli can support the expression of genes from several donor genomes, an unknown

quantity cannot be expressed due to differential transcriptional, translational or post-translational

controls (Uchiyama and Miyazaki, 2009). These may include barriers to promoter recognition

and initiation factors, codon usage or ribosomal entry and protein folding. Using in silico

detection of compatible expression signals, it was estimated that roughly 40% of genes within a

subset of 32 taxonomically diverse genomes could be expressed in E. coli with a wide range (7–

73%) in expression among genomes (Gabor et al., 2004). Ralstonia metallidurans has also

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been shown to be a suitable gram-negative host for the heterologous expression of genes

(Craig et al., 2010; Iqbal et al., 2014). Others have designed plasmid-based shuttle vectors to

transfer genes from metagenomic libraries into Gram-positive bacteria such as Bacillus subtilis

(Troeschel et al., 2012). Metagenomic libraries have also been used to identify ARGs in unique

samples. For example, using fosmid libraries, 1,093 AMR-associated genes were detected in

human gut microbial communities (Hu et al., 2013). Elsewhere, using functional genomics,

clinical-level resistance to aminoglycosides, tetracycline, and β-lactams was identified in 5,000

year-old microbial communities from ancient permafrost soil collected from the Canadian Arctic

(Perron et al., 2015).

Although not yet widely employed in beef research, functional screening of

metagenomics libraries are likely to be used to identify novel ARGs in samples from beef cattle

and their surrounding environment, thereby supporting the development of AMR databases that

are more reflective of the production environment. It is also likely that functional metagenomics

will be used to identify novel antimicrobials produced by microorganisms in these settings or to

facilitate the identification of probiotics that maybe employed in this agricultural settings to offset

antimicrobial use.

Single-Cell Sequencing

Single-cell sequencing is the study of the individuality of cells using omics techniques

(Linnarsson and Teichmann, 2016). As improvements in NGS have reduced the amount of

template DNA required for sequencing, it has become feasible to sequence the genome of

single cells (Navin et al., 2011) or apply RNA-seq to measure single-cell gene expression.

Thus, the genome, epigenome, and transcriptome of unculturable or rare microbes can be

sequenced (Blainey, 2013). Technical details of bacterial single cell genome sequencing are

reviewed elsewhere (Blainey, 2013). In brief, individual bacterial cells are isolated from complex

microbial communities on the basis of phenotypic characteristics like cell size, shape, or the

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spectral pattern of native or applied fluorophores. Cells are typically isolated using flow

cytometry/cell sorting, laser capture, or microfluidic devices (Wallner et al., 1997). The genomic

DNA or RNA of the individual cell is then amplified, often via multiple displacement amplification

(Raghunathan et al., 2005) and sequenced.

Typically, clinical research outpaces veterinary when it comes to the application of novel

or newly developed sequencing technologies. As such, single-cell genomics has not yet been

utilized in beef AMR research. However, this technique could overcome the limitations of

metagenomic approaches in terms of directly linking bacterial species to ARGs or be employed

to examine phenomenon like adaptive resistance in microbial populations from livestock. If

coupled successfully with hybridization based labelling technologies, single cell sequencing

could also be used to sort out ARGs containing cells from mixed populations for targeted WGS.

As this technology is less mature than some of the other omics technologies, there are current

limits imposed by the algorithms designed for analysis of single-cell data (Linnarsson and

Teichmann, 2016). However, these are likely to resolve as the technique is applied with

increasing frequency across disciplines.

GENOMICS-BASED RESISTANCE MITIGATION STRATEGIES FOR BEEF PRODUCTION

Genomic analyses can be applied in multiple ways to help combat or mitigate AMR in beef

production. On a basic level, ARG surveillance may contribute to an understanding of the

relationship between AMR and virulence, and provide insight into the epidemiology of ARG

expression in pathogenic and commensal bacterial populations, leading to more effective

utilization or reduced use of antimicrobials. Omics tools may also be employed to identify new

antimicrobials or antimicrobial targets as well as identify potential alternatives to antimicrobials

such as vaccines, probiotic bacteria or bacteriophage. Genomics can also be used to

characterize unique features within microbial genomes that can be targeted or exploited to

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combat the transmission of ARGs such as the application of the rapidly emerging CRISPR-Cas

gene-editing systems.

Rational Antimicrobial Design Using Target Gene Identification

Traditionally, antimicrobials have been identified via inhibition screening of cultures

grown together with fermentation broths or in the presence of organic compounds generated by

microbes (Lewis, 2013). More recently, the majority of new antimicrobials are now synthesized

analogues based on the core scaffold of existing antimicrobial families, with alterations that

often further improve their activity and pharmacokinetic properties (McPhillie et al., 2015).

Despite this, the rapid development of resistance and the high cost of designing, developing,

and bringing a new antimicrobial to market, have stalled traditional development pipelines.

However, genomic approaches are providing new opportunities to study and identify novel

antimicrobials.

For antimicrobials to be effective they must target microbial features essential for cell

survival, lack a mammalian homologue (to avoid host side-effects), and have broad spectrum

activity against multiple bacterial species. Typically, such compounds need to target highly-

conserved cellular components, features that are now easily identified using comparative

genomics. This strategy has been applied to Vibrio cholerae (Chawley et al, 2014),

Mycobacterium ulcerans (Butt et al., 2012), Staphylococcus aureus (Lee et al., 2009),

Mycobacterium tuberculosis and E. coli O157:H7 (White and Kell, 2004; Chalker & Lunsford,

2002; Falconer & Brown, 2009), and is becoming common enough to support the development

of computational pipelines that can assist in the discovery of new antimicrobials (Panjkovich et

al., 2014).

Gene Fitness Profiling By Transposon-Insertion Sequencing

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Mutagenesis based genomics have also been applied to identify essential genes that

can be exploited as drug targets. Transposon-insertion sequencing (TIS) is one such method

used for genome-wide interrogation of gene function, essentiality, fitness and interaction within

functional networks. Transposons are DNA polynucleotides capable of inserting and moving

from one location to another within genomes (Munoz-Lopez and Garcia-Perez, 2010). The

insertion of a transposon into a gene coding sequence usually disrupts function, and may alter

properties of non-coding DNA. Transposon-insertion sequencing comprises several methods, all

utilizing saturated transposon libraries which typically consist of a pooled library of individual

insertional mutants constructed within the bacterial strain of interest. Thus, combined with

massively parallel sequencing, high-throughput insertion site tracking can be used to determine

the location of the transposon within the altered gene. Changes in the frequency of insertion can

elucidate the gene’s function and fitness depending on the condition-of-interest under which the

pooled library was established. Transposon mutagenesis has been used to identify essential

genes in different bacterial species (Gerdes et al., 2002; Roemer & Boone, 2013), including

Haemophilus influenza (Akerley et al., 2002) and Streptococcus pneumoniae (Thanassi et al.,

2002).

In the context of AMR, TIS may be utilized to discover novel antimicrobial targets,

virulence factors and genes essential for colonization and pathology of species carrying ARGs

within the host. Products of these identified genes can then be used as vaccine antigens or as

targets to generate attenuated vaccine strains. Four major types of TIS include transposon

sequencing (Tn-seq), insertion sequencing (INseq), high-throughput insertion tracking by deep

sequencing (HITS), and transposon-directed insertion site sequencing (TraDIS; Barquist et al.,

2013; van Opijnen and Camilli, 2013). Transposon sequencing and INseq both use MmeI-

flanked himar1 Mariner transposon libraries (Lampe et al., 1999; van Opijnen et al., 2009).

MmeI is a Type IIS endonuclease-cleaving outside and to one side of the recognition sequence.

In these methods it creates 16 bp fragments of transposon-flanking genomic DNA, the

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sequence of which can be used to identify the site of transposon insertion. High-throughput

insertion tracking by deep sequencing and TraDIS rely on random shearing of genomic DNA

obtained from transposon libraries. This is followed by adapter attachment and PCR

amplification to enrich transposon-adjacent DNA prior to sequencing. Thus, PCR may introduce

bias into HITS and TraDIS methods, but advantageously these methods can be applied to any

transposon or transposable element. For all methods, transposon mutagenesis may be

complicated for bacterial species that are poorly described, difficult to culture, difficult to

transform with DNA, or for which genetic transposon-delivery systems have not been

developed. Finally, since transposon mutants with fitness defects are not recovered, validation

of TIS discoveries requires the construction of targeted deletion mutants.

Although TIS has not been widely applied in beef cattle research, several studies have

shown its promise in the identification of new antimicrobials and vaccine candidates. In one

study, TraDIS was used to assign genotypes and fitness scores to mini-Tn5 (transposon)

mutants of E. coli O157:H7 strain EDL933 via comparison of inoculation/infection library pools

and mutants recovered from calf feces 5 days post-inoculation (Eckert et al., 2011). Many of the

mutations that attenuated colonization were mapped to the locus of enterocyte effacement

(LEE). This gene complex harbors a type III secretion system (T3SS) and effectors of host

colonization, including eae, intimin, tir (translocated intimin receptor), and z1829, (EspK), all

known to influence persistence of enterohemorrhagic E. coli in calves (Naylor et al., 2005).

Other attenuated mutants included insertions in enterohemolysin, O-antigen, and aromatic

amino acid biosynthesis genes, and an insertion in the catalytic subunit of stx1A Shiga toxin

(Eckert et al., 2011). Thus, TIS identified O157:H7 genes with roles in the intestinal colonization

of cattle, including some genes that code for proteins used to develop anti-E. coli O157:H7

cattle vaccines used in the United States and Canada (Smith, 2014). Transposon-directed

insertion site sequencing has also been used to assess the identity and relative fitness of mini-

Tn5 insertions in Salmonella enterica serovar typhimurium strain ST4/74 in chickens, pigs, and

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cattle, revealing 1,069 unique mutants with attenuation in at least one animal host (Chaudhuri et

al., 2013). These attenuated mutants, refined for use as live vaccines, could help reduce the

transmission of these and other common AMR foodborne pathogens.

In another application, essential genes for the human respiratory pathogens

Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis were identified

by Tn-seq and compared in silico to the human genome and human commensal microbiome

database to exclude genes with potential off-target/disruptive effects (Mobegi et al., 2014). In

total, 249 essential genes were identified, and 67 were targets of 75 Food and Drug

Administration-approved antimicrobials and 35 independently-researched small molecules

(Mobegi et al., 2014). As proof-of-principle, novel compounds, without prior known antimicrobial

activity were tested for their ability to target the predicted function of four essential genes

involved in fatty acid, methionine, vitamin, and isoprenoid biosynthesis. The antimicrobial

activity of some of the tested compounds validated two of the four tested targets (Mobegi et al.,

2014). Thus, similar approaches, expanded to other bacterial species, could provide concrete

leads for new targets and antimicrobials. Antimicrobial tolerance can also be explored with TIS

for example, a Tn-seq screen of E. coli mutants tolerant to gentamicin identified >100 genes in

multiple pathways (Shan et al., 2015). Thus, TIS might also be used to predict/design/develop

synergistic drug potentiators that expand the usefulness of current antimicrobials.

RNA-Seq Transcriptomics

RNA-seq is a technology that allows for the quantitative measurement of global gene

expression via massively parallel sequencing (RNA-seq), and has dramatically advanced

discovery in multiple areas of biology by facilitating the analysis of dynamic transcriptomes in

complex regulatory networks, identifying differentially spliced transcripts and fusions, multiple

post-transcriptional modifications and single nucleotide polymorphisms (SNPs)/mutations in

mRNA (Wilhelm and Landry, 2009). Beyond mRNA, RNA-seq facilitates ribosome profiling and

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the study of small RNAs (Ozsolak and Milos, 2011; Ingolia, 2014). Further advances allow for

simultaneous host-pathogen transcriptomics (dual-RNA-seq) (Westermann et al., 2012). As

RNA-seq is based upon NGS, it can be applied in any biological context from which sufficient

RNA can be isolated. RNA-seq protocols can be modified in many ways to address specific

questions, but the typical transcriptomic approach involves isolation of total RNA, conversion to

double-stranded cDNA, and ligation of adaptors for NGS sequencing. Given that the bulk of

RNA species (>90%) are comprised of rRNA, a number of techniques can be used to deplete

rRNA or enrich mRNA.

RNA-seq has rapidly become one of the most important “omics” technologies, and has

proved useful in elucidating bacterial regulatory hierarchies controlling the expression of ARGs

(Li et al., 2016), and identifying genes that are induced upon antimicrobial exposure (Kjos et al.,

2016). RNA-seq has also been applied in research with relevance to cattle and mitigation of

antimicrobial resistance. In one study, RNA-seq transcriptomics of lung and bronchial node

tissues from cattle challenged with BRD-associated pathogens identified elevated expression of

several antimicrobial peptide (AMP) including natural killer (NK) lysins (Chen et al., 2016). The

bovine genome harbors four NK-lysin genes, and upregulation of NK2A and NK2C was

observed in most pathogen-challenged animals (Chen et al., 2016). The NK2C transcript

expression was >20-fold in two of the experimentally challenged animals (Chen et al., 2016).

This further demonstrates the importance of these AMPs as synthetic peptides based on the

functional components of these gene products exerted antimicrobial effects on P. multocida and

M. haemolytica (Chen et al., 2016). Current therapies for BRD often involve high-frequency

antimicrobial usage and new control strategies could significantly lessen antimicrobial usage

and AMR. For example, selective breeding could further increase the diversity and expand the

repertoire of the NK-lysin genes (Chen et al., 2016), or synthetic peptides could replace current

antimicrobials.

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RNA-seq has also helped understand host determinants of Bacillus Calmette-Guerin

vaccination efficacy in cattle later challenged with Mycobacterium bovis (bovine tuberculosis,

bTB). RNA-seq transcriptomics of peripheral blood mononuclear cells harvested from cattle

(vaccine-protected, vaccine-unprotected, and non-vaccinated) showed that up-regulation of the

gene encoding the host cytokine IL-22 was the dominant predictor of vaccine protection (Bhuju

et al., 2012). Thus, such information could aid in the design and development of a new bTB

vaccine. Beyond bTB, RNA-seq could likewise assist in the development of effective vaccines

for other bovine pathogens that are currently treated with antimicrobials.

Genomic Identification of Vaccine Targets

Vaccines are the most effective and efficient way to prevent bacterial infection and are a

primary means to overcome the current reliance of antimicrobials to treat diseases in beef

production. Vaccines are advantageous in that they are an efficient, cost-effective, and a

sustainable method of disease prevention. Conventional vaccine design relies on the use of

heat-killed, live modified or purified component vaccines typically derived from culturable

bacteria. In silico identification of antigen candidates offers some advantages over less specific

conventional vaccine development methods. For example, in vitro cultivation of the target

organism is not required, eliminating the effects of variable growth expression or antigenic

profiles under different growth conditions. In addition, comparative genomics of unrelated

pathogens that comprise infectious complexes or commensal populations can identify targets

for the development of polyvalent vaccines that are effective against more than one species, or

those that are specific to the infection (Seib et al., 2012). Following identification, antigen

candidates used in subunit vaccines are projected to produce a product for clinical trials within 1

- 2 years vs. the 5 - 15 years commonly required by conventional vaccine development methods

(Johri et al., 2006).

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Genomics-based reverse vaccinology successfully identified pathogen-specific surface-

exposed vaccine candidates for Neisseria meningitides (Pizza et al., 2000), Bacillus anthracis

(Ariel et al., 2002), S. pneumoniae (Wizemann et al., 2001) and Helicobacter pylori (Dutta et al.,

2006). Similarly, comparisons of 8 Group B Streptococcus strains identified 312 surface protein

vaccine candidates, and a combined vaccine of four of these proteins was protective in a murine

disease model (Maione et al., 2005). Comparative analyses of pathogenic and commensal E.

coli revealed pathogen-specific genes encoding proteins, that could be targeted to generate

pathogen-specific vaccines (Bhagwat & Bhagwat, 2008; Rasko et al., 2008). Likewise, DNA

microarrays that compared genomic DNA from M. tuberculosis and M. bovis identified 910 M.

tuberculosis-specific open reading frames that were absent in M. bovis (Behr et al., 1999). The

authors suggested that these missing regions in M. bovis might impair the ability of the BCG

vaccine to stimulate a protective immune response against TB. Thus, genomics tools can be

used not only to design vaccines, but to evaluate the efficacy of existing formulations, potentially

highlighting ways to improve the efficacy of current products.

Vaccination is currently the most effective strategy towards disease management and

indirect prevention of AMR in intensive agricultural systems. Vaccines are commonly used in

cattle production to reduce the incidence of respiratory infections by viral and bacterial agents.

However, the efficacy of many products is limited due to the number of agents involved in

diseases complexes (e.g., BRD), lack of universal protection against all serotypes, and

challenges faced with eliciting strong immune responses. The continued accumulation of WGS

data, coupled with advancement in the bioinformatics tools used to identify conserved surface-

exposed or secreted antigens (Petersen et al., 2011), and predict bovine immune responses

(Lundegaard et al., 2008) will likely help to alleviate some of these issues and improve the

efficacy of future vaccines.

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Probiotic Identification and Validation

According to the World Health Organization, probiotics are defined as live

microorganisms which when administered in adequate amounts, confer a health benefit to the

host. Probiotics are used in beef cattle with the intention to increase feed efficiency, promote

weight gain, support overall health and reduce the shedding of human pathogens such as E.

coli O157:H7 (Chaucheyras-Durand and Durand, 2009). For example, the feeding of live yeast

to cattle has been shown to improve growth (Lesmeister et al., 2004) by increasing the density

of cellulolytic microorganisms (Newbold et al., 1996; Mosoni et al., 2007). These additives can

also stabilize ruminal pH and thereby decrease the risk of acidosis (Chaucheyras-Durand et al.,

2008). Further, the use of Lactobacillus acidophilus in-feed has been shown to reduce the

shedding of E. coli O157:H7 (Younts-Dahl et al., 2005; Chaucheyras-Durand et al., 2006) and

Salmonella enterica (Stephens et al., 2007). Field studies have shown that these probiotics can

reduce E. coli O157:H7 shedding by up to 50% (Brashears et al., 2003) and its presence on

hides by as much as 75% (Younts-Dahl et al., 2005; Stephens et al., 2007). A reduction in the

release of these pathogens into the environment theoretically reduces the risk of humans

acquiring infection (Chaucheyras-Durand and Durand, 2010). The use of probiotics in livestock

may also enhance immune function through the promotion of immunoglobulin production (Musa

et al., 2009).

Given the above, probiotics are potential alternatives to the use of antimicrobials in beef

production, not only as a means to offset the use of growth promotors, but to reduce the

carriage of bacterial pathogens that might otherwise prompt antimicrobial use. There are

multiple avenues whereby genomics could aid in the development of effective probiotics. For

example, probiotics can be selected based on functional genomics so that they provide nutrients

that limit existing beneficial commensal bacterial species or generate antagonisms that prevent

the establishment of pathogens (Callaway et al., 2008). Whole genome sequencing of probiotic

isolates provides information that aids in the elucidation of how they may contribute to the

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microbial community (Kullen and Klaenhammer, 2000). This can include identifying key genes

or features that limit undesirable bacteria through competition for nutrients and physical

attachment sites within the intestinal tract. Other factors could include enhancement of host

immune activity (Callaway et al., 2008). Metagenomics can aid in examining the effects of

probiotics strains on the composition of microbial communities as well examine the ability of

probiotic strains to establish and persist within microbiomes. Transcriptomics of mRNA from the

host as well as from microbiome can be used to identify synergistic relationship between the

animal and its microflora. Lastly, functional screening of metagenomic libraries for gene

products or metabolites with effective antimicrobial activity may also help to identify prebiotics or

important processes in probiotic bacteria. Although genomic tools have not been widely applied

to probiotics used in beef cattle production they have potential to improve the efficacy of these

potential alternatives to antimicrobials.

Bacteriophage Therapy

Bacteriophage are viruses that specifically infect bacterial hosts. They are divided into

two classes: lytic and temperate. Lytic bacteriophage are bactericidal and exploit host molecular

machinery to propagate new bacteriophage particles that lyse the host cell, releasing viral

progeny into the environment. Temperate bacteriophage integrate into the host bacterial

chromosome or may replicate extra-chromosomally within an infected bacterial cell. The

genome of temperate baceriophage is passed to daughter bacterial cells during cell division. Of

the two forms, lytic bacteriophage have the potential to be exploited as tools to target and kill

specific bacteria (Pires et al., 2015). The efficacy of bacteriophage therapy has been reported

against many bacterial pathogens, including MDR P. aeruginosa (Khairnar et al., 2013; Pires et

al., 2015), M. tuberculosis (Hatfull, 2010), and methicillin-resistant S. aureus (Takemura-

Uchiyama et al., 2014). Bacteriophage therapy has also been shown to be effective in vivo,

reducing Salmonella Typhimurium and C. jejuni colonization in chickens (Atterbury et al., 2007;

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Loc Carrillo et al., 2005; Waseh et al., 2010) and Salmonella and E. coli O157:H7 in cattle

(Johnson et al., 2008).

In addition to the targeted bactericidal effects of lytic bacteriophage, engineered phage

may also be used to deliver targeted genes to favorably alter bacterial populations (Karimi et al.,

2016). This method has been used to deliver DNA encoding bactericidal proteins in an E. coli

model (Westwater et al., 2003). It has also been used to transform a streptomycin-resistant E.

coli with a plasmid that contained a copy of the wild-type susceptibility gene rpsL, rendering the

resistant strain ~10-fold more sensitive to this antimicrobial (Edgar et al., 2011). The potential

for engineered bacteriophage to deliver targeted antimicrobials to specific bacterial populations

may reduce the total volume of antimicrobials required to for therapy, thereby reducing AMU.

In addition to bacteriophage strategies, phage-derived proteins called endolysins

possess antimicrobial activity, and in conjunction with other phage proteins (e.g., holins) can

degrade the bacterial cell membrane and peptidoglycan layer. Endolysins lyse Gram-positive

bacterial cell walls when applied extracellularly (Loessner, 2005) with the recombinant Ply511

endolysin from Listeria monocytogenes phage A511 being active against MRSA (Turner et al.,

2007). Other endolysins have also been used successfully against Streptococci in vitro (Nelson

et al., 2001) and against S. pneumoniae, S. agalactiae, S. pyogenes and B. anthracis in animal

disease models (Schmelcher & Loessner, 2016). Although previously presumed otherwise, the

antimicrobial activity of bacteriophage proteins is not limited to Gram-positive bacteria as a

recombinant phage carbohydrate-binding protein (Gp047) from the C. jejuni phage NCTC 12673

was active against Campylobacter jejuni NCTC 11168 (Javed et al., 2015). Additionally, the

lysin, PlyF307 present in bacteriophage induced from Acinetobacter baumannii strain 2198,

was able to kill multiple MDR A. baumannii strains in vitro and reduce mortality in mice

challenged with this bacterium (Lood et al., 2015). The potential to exploit these compounds as

antimicrobials in veterinary settings may help alleviate the current lack of novel antimicrobials

within pharmaceutical pipelines. It is also important to note that many bacteriophage proteins

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may also be used to enhance the effectiveness of existing antimicrobials as some, like

Pseudomonas putida phage AF proteins can degrade biofilms (Cornelissen et al., 2012).

Although not a true antimicrobial, use of these proteins to reduce the protective effects of

biofilms against antimicrobials may help increase the efficacy of antimicrobials that are already

used in beef production.

Genomics may aid in the identification of novel lytic bacteriophage for use as therapies

in beef production systems through WGS of bacteria or through metagenomic sequencing of

viromes collected from both beef cattle and their environment. Sequence analysis of

bacteriophage genomes as well as transcriptomic and functional metagenomics may all be

employed to help identify viral peptides that can be exploited as antimicrobials or reduce the

robustness of biofilms. As more information is gathered about bacteriophage diversity within this

environment, this novel mitigation method may prove useful for the control and eradication of

MDR pathogens in beef production systems.

Biofilm Inhibition and Dispersal

Bacteria grown in vitro typically display planktonic growth that maximizes their

susceptibility to antimicrobial agents. However, within natural environments, bacteria often form

complex communities that are densely integrated into a biofilm bound by extracellular polymeric

substances. Biofilm bacteria have increased resistance to antimicrobials agents, due to

exclusion of the antimicrobial, slow bacterial growth rates that impede antimicrobial activity and

expression of secreted antimicrobial degrading products by some bacterial species within the

community (Gabrani et al., 2015). Therapeutic doses of antimicrobials that are based on

susceptibility levels obtained in vitro may not be effective against microbes encased within

biofilms, resulting in treatment failure.

Inhibition and or dispersal of microbial biofilms could lead to improved antimicrobial

efficacy. Genomic technologies augment biofilm research via improved understanding of biofilm

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complexity and community composition (Sauer, 2003), and can assist in the identification of

therapeutic agents that degrade (Gökçen et al., 2014) or inhibit the formation of biofilms. One

strategy to mitigate biofilms has been to utilize antibodies raised against sequence-identified

proteins involved in biofilm formation. These include the accumulation-associated protein

produced by S. epidermidis (Sun et al., 2005) and the P. aeruginosa biofilm protein PsI

(DiGiandomenico et al., 2012). Additional sequencing may identify more of these target proteins

in biofilm producing species. Wildtype and engineered bacteriophage have also been used as a

tool to effectively disperse P. aeruginosa and E. coli biofilms through their bactericidal effects

(Alemayehu et al., 2012). Using genomics tools to identify more targets for bacteriophage

induced biofilm dispersal will maximize the utility of such strategies. Lastly, there are many

proteins that have been shown to have anti-biofilm activity including DNase I/DNase 1L2

(Eckhart et al., 2007), human antimicrobial peptide cathelicidin LL-37, and some synthetic

peptides (Dean et al., 2011; Overhage et al., 2008). Thus, functional genomics can be applied

to environmental samples to identify molecules that can degrade or disperse biofilms, resulting

in products that could reduce AMU through improved antimicrobial efficacy.

Antimicrobial Peptides

Antimicrobial peptides (AMPs) are short positively charged amphipathic peptides,

produced by many organisms that play a role in innate immunity (Boman, 1995). These

peptides bind to and neutralize negatively charged cell surfaces (lipoteichoic acid and

lipopolysaccharide) of a wide range of Gram-positive and Gram-negative bacteria, mainly

through perturbations in the cell envelope (Jenssen et al., 2006). Antimicrobial peptides have

also been shown to traverse the cell membrane and interact with DNA or DNA polymerase to

effect cell death (Powers & Hancock, 2003). Bacteriocins are a class of AMP produced by

numerous bacterial species (Galvez et al., 2008), some of which share homology with

bacteriophage genes, suggesting a possible common origin. Bacteriocins kill bacteria by a

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number of mechanisms. For example, R-type pyocins form pores in the bacterial membrane

while a novel pyocin from P. aeruginosa JJ692 hydrolyzed peptidoglycan (Barreteau et al.,

2009; Scholl and Martin, 2008). As these AMPs are produced in a wide array of bacteria, WGS

and comparative genomics can be used to screen microbial genomes for novel bacteriocins that

could be used as antimicrobials.

In addition to bacterial sources, AMPs can also be produced by mammalian hosts as a

first line defence against microbial pathogens. An example of this is a cysteine-rich AMP formed

in the tracheal mucosa of cattle, named tracheal antimicrobial peptide (TAP). This defensin

displays broad-spectrum activity against respiratory pathogens in vivo (Yarus et al., 1996).

Sequence analysis of the coding region of the TAP gene identified a non-synonymous SNP that

corresponded to varied bactericidal activity of the peptide (Taha-Abdelaziz et al, 2013). This

finding highlighted the important role genomic tools have in providing a mechanistic

understanding of the function of AMPs. The design and synthesis of potent antimicrobial

peptides may offer a valuable alternative to antimicrobials.

CRISPR –Cas Gene-Editing

The CRISPR (clustered regularly-interspaced short palindromic repeats) -Cas9 system

is a technology that is increasingly exploited for genome editing, and has recently been

successfully used to target and eliminate populations of AMR bacteria. CRISPR-Cas systems

are used by bacteria to resist invasion by foreign nucleic acids, which include bacteriophage

genomes and plasmids (Makarova et al., 2011). They are genomic features that carry repetitive

sequences interspersed with short spacer segments. The spacers are complementary to the

foreign nucleic acids from which they were derived. CRISPR-associated (Cas) genes encode

proteins with nuclease (e.g., Cas9) or helicase activity. Mechanistically, in the case of CRISPR-

Cas9, the spacer DNA nucleotide sequence is transcribed into crRNA, which guides the Cas9

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nuclease to the re-infecting foreign DNA, which is then degraded (Marraffini and Sontheimer,

2010).

Self-targeting CRISPR-Cas are cytotoxic and were originally proposed by Marraffini et

and Sontheimer (2008) as a means of limiting the spread of AMR in pathogenic bacteria. Two

groups, Bikard et al., (2014) and Citorik et al., (2014), applied this idea and engineered plasmids

with phage packaging signals, called phagemids, which contained all genetic components

necessary for CRISPR-Cas activity in a form that was contained and deliverable by

bacteriophage. In both cases, the packaged phagemids were used to target chromosomal

material as well as antimicrobial-resistant plasmids, leading to successful plasmid removal from

the bacterial population. In the study by Bikard et al., (2014), an anti-aph CRISPR-Cas

construct rendered S. aureus cells immune to future acquisition of the aph gene and its

associated kanamycin resistance, providing proof of principal that CRISPR-Cas systems can

prevent the spread of plasmid-borne resistance genes. In the study by Citorik et al. (2014), the

phagemid was used to target the chromosomally located DNA gyrase gene conferring resistant

to quinolone antimicrobials as well as to simultaneously target two different β-lactamases,

encoded by blaNDM-1 and blaSHV-18, highlighting the versatility of the technology and its potential to

target multiple ARGs at once.

The application of CRISPR-Cas systems to mitigate AMR in beef cattle is possible but

requires the development of a product that is both effective and economically appealing to

producers. Both S. aureus and E. coli are clinically relevant microbes and the bacteriophages

associated with these bacteria have been well characterized. Phage can be highly specific,

attaching to surface receptors that are often unique to a particular bacterial species (Drulis-

Kawa et al., 2012). To be able to apply a similar phagemid based approach to control AMR in

beef production, more information will be required about the specific bacterial species/ strains

that contribute to the ARG reservoir along with their associated bacteriophage. Conjugation has

been examined as an alternative delivery route for CRISPR-Cas systems, but transfer

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efficiencies were too low for effective application (Citorik et al., 2014). However, further

research into the use of conjugation based delivery of CRISPR-Cas systems could yield a less

discriminant and more widely applicable product than phage delivery based approaches.

Regardless of the route to development, the application of a well-designed CRISPR-Cas based

system holds the potential for AMR mitigation in the feedlot environment and will likely be a

subject of future investigation.

CONCLUSION

As the global demand for animal protein continues to increase, so does the drive towards more

intensive agriculture that currently relies on AMU to control infectious diseases. Maintaining the

efficacy of antimicrobials as a resource in beef production is of critical importance. Genomic

technologies have already yielded significant advancements in our understanding of the

transmission of AMR human pathogens, the development of novel antimicrobials, and the

development of novel AMR mitigation strategies. Future efforts to combat AMR via effective

surveillance in human and animal health settings, vaccine development, and the identification

and formulation of alternatives to antimicrobials will rely heavily on genomic approaches. Taken

together, these efforts may help minimize AMU and the impact of AMR bacteria and the

infectious diseases they cause on beef cattle production.

ACKNOWLEDGMENTS

The authors acknowledge the financial support of the Beef Cattle Research Council –

Agriculture and Agri-Food Canada Beef Cluster AMR program that enabled the preparation of

this review article.

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REFERENCES

Agga, G. E., Arthur, T. M., Durso, L. M., Harhay, D. M. and Schmidt, J. W. 2015. Antimicrobial-

resistant bacterial populations and antimicrobial resistance genes obtained from

environments impacted by livestock and municipal waste. PLoS ONE. 10: 1–19.

http://doi.org/10.1371/journal.pone.0132586

Akerley, B. J., Rubin, E. J., Novick, V. L., Amaya, K., Judson, N. and Mekalanos, J. J. 2002. A

genome-scale analysis for identification of genes required for growth or survival of

Haemophilus influenzae. Proc. Natl. Acad. Sci. USA 99: 966–971.

http://doi.org/10.1073/pnas.012602299

Alemayehu, D., Casey, P. G., McAuliffe, O., Guinane, C. M., Martin, J. G., Shanahan, F. et al.

2012. Bacteriophages phiMR299-2 and phiNH-4 can eliminate Pseudomonas aeruginosa

in the murine lung and on cystic fibrosis lung airway cells. MBio. 3: e00029-12.

http://doi.org/10.1128/mBio.00029-12

Ariel, N., Zvi, A., Grosfeld, H., Gat, O., Inbar, Y., Velan, B. et al. 2002. Search for potential

vaccine candidate open reading frames in the Bacillus anthracis virulence plasmid pXO1:

In silico and in vitro screening. Infect.Immun. 70: 6817–6827.

http://doi.org/10.1128/IAI.70.12.6817-6827.2002

Atterbury, R. J., Van Bergen, M. A. P., Ortiz, F., Lovell, M. A., Harris, J. A., De Boer, A. et al.

2007. Bacteriophage therapy to reduce Salmonella colonization of broiler chickens. Appl.

Environ. Microbiol. 73: 4543–4549. http://doi.org/10.1128/AEM.00049-07

Barquist, L., Boinett, C.J. and Cain A.K. 2013. Approaches to querying bacterial genomes with

transposon-insertion sequencing. RNA Biol. 10: 1161-9. doi: 10.4161/rna.24765.

Barreteau, H., Bouhss, A., Fourgeaud, M., Mainardi, J. L., Touzé, T., Gérard, F. et al. 2009.

Human- and plant-pathogenic Pseudomonas species produce bacteriocins exhibiting

colicin M-like hydrolase activity towards peptidoglycan precursors. J. Bacteriol. 191: 3657–

Page 32 of 52



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nly

33

3664. http://doi.org/10.1128/JB.01824-08

Behr, M. A, Wilson, M. A, Gill, W. P., Salamon, H., Schoolnik, G. K., Rane, S. and Small, P. M.

1999. Comparative genomics of BCG vaccines by whole-genome DNA microarray.

Science 284: 1520–1523. http://doi.org/10.1126/science.284.5419.1520

Bhagwat, A. A. and Bhagwat, M. 2008. Methods and tools for comparative genomics of

foodborne pathogens. Foodborne Path.Dis. 5: 487–97.

http://doi.org/10.1089/fpd.2008.0117

Bhuju, S., Aranday-Cortes, E., Villarreal-Ramos, B., Xing, Z., Singh, M. and Vordermeier, H. M.

2012. Global Gene Transcriptome Analysis in Vaccinated Cattle Revealed a Dominant

Role of IL-22 for Protection against Bovine Tuberculosis. PLoS Path. 8: e1003077.

http://doi.org/10.1371/journal.ppat.1003077

Bikard, D., Euler, C. W., Jiang, W., Nussenzweig, P. M., Goldberg, G. W., Duportet, X. et al.

2014. Exploiting CRISPR-Cas nucleases to produce sequence-specific antimicrobials. Nat.

Biotech. 32: 1146–50. http://doi.org/10.1038/nbt.3043

Blainey, P. C. 2013. The future is now: Single-cell genomics of bacteria and archaea. FEMS

Microbiol. Rev. 37: 407-427 http://doi.org/10.1111/1574-6976.12015

Bloemendaal, A. L. A., Brouwer, E. C. and Fluit, A. C. 2010. Methicillin resistance transfer from

Staphylocccus epidermidis to methicillin-susceptible Staphylococcus aureus in a patient

during antibiotic therapy. PLoS ONE. 5: e11841. doi:10.1371/journal.pone.0011841.

Bodi, K., Perera, A. G., Adams, P. S., Bintzler, D., Dewar, K., Grove, D. S. et al. 2013.

Comparison of commercially available target enrichment methods for next-generation

sequencing. J Biomol Tech. 24: 73–86. http://doi.org/10.7171/jbt.13-2402-002

Boman, H. G. 1995. Peptide antibiotics and their role in innate immunity. Annu.Rev. Immun., 13:

61–92. http://doi.org/10.1146/annurev.iy.13.040195.000425

Boyd, D., Peters, G. A., Cloeckaert, A., Boumedine, K. S., Chaslus-Dancla, E., Imberechts, H.

and Mulvey, M. R. 2001. Complete nucleotide sequence of a 43-kilobase genomic island

Page 33 of 52



For Review O

nly

34

associated with the multidrug resistance region of Salmonella enterica serovar

Typhimurium DT104 and its identification in phage type DT120 and serovar Agona. J.

Bacteriol. 183: 5725–32. http://doi.org/10.1128/JB.183.19.5725-5732.2001

Brandt, C., Zander, E., Pfeifer, Y., Braun, S. D., Ehricht, R., Makarewicz, O. and Pletz, M. W.

2016. Development of a rapid diagnostic assay based on magnetic bead purification of

OXA-β-lactamase mRNA. Future Microbiol.11: 617–629. http://doi.org/10.2217/fmb.16.13

Brashears, M. M., Galyean, M. L., Loneragan, G. H., Mann, J. E. and Killinger-Mann, K. 2003.

Prevalence of Escherichia coli O157:H7 and performance by beef feedlot cattle given

Lactobacillus direct-fed microbials. J Food Prot. 66: 748–754.

Butt, A. M., Nasrullah, I., Tahir, S. and Tong, Y. 2012. Comparative genomics analysis of

mycobacterium ulcerans for the identification of putative essential genes and therapeutic

candidates. PLoS ONE. 7: e43080. http://doi.org/10.1371/journal.pone.0043080

Callaway, T. R., Edrington, T. S. Anderson, R. C., Harvey, R. B., Genovese, K. J., Kennedy, C.

N. et al. 2008. Probiotics, prebiotics and competitive exclusion for prophylaxis against

bacterial disease. Anim.Health Res.Rev.9: 217–225.

http://doi.org/10.1017/S1466252308001540

Chalker, A. F. and Lunsford, R. D. 2002. Rational identification of new antibacterial drug targets

that are essential for viability using a genomics-based approach. Pharmacol. Ther. 95: 1-20

http://doi.org/10.1016/S0163-7258(02)00222-X

Chambers L, Yang Y, Littier H, Ray P, Zhang T, Pruden A, Strickland M. and Knowlton K. 2015.

Metagenomic Analysis of Antibiotic Resistance Genes in Dairy Cow Feces following

Therapeutic Administration of Third Generation Cephalosporin. PLoS One. 10: e0133764.

doi: 10.1371/journal.pone.0133764. eCollection

Chantziaras, I., Boyen, F., Callens, B. and Dewulf, J. 2014. Correlation between veterinary

antimicrobial use and antimicrobial resistance in food-producing animals: a report on seven

countries. J. Antimicrob. Chemother. 69: 827–34. http://doi.org/10.1093/jac/dkt443

Page 34 of 52



For Review O

nly

35

Chaudhuri, R. R., Morgan, E., Peters, S. E., Pleasance, S. J., Hudson, D. L., Davies, H. M. et al.

2013. Comprehensive Assignment of Roles for Salmonella Typhimurium Genes in

Intestinal Colonization of Food-Producing Animals. PLoS Gen. 9: e1003456.

http://doi.org/10.1371/journal.pgen.1003456

Chaucheyras-Durand, F. and Durand, H. 2010. Probiotics in animal nutrition and health. Benef.

Microbes. 1: 3–9. http://doi.org/10.3920/BM2008.1002

Chaucheyras-Durand, F., Madic, J., Doudin, F. and Martin, C. 2006. Biotic and abiotic factors

influencing in vitro growth of Escherichia coli O157:H7 in ruminant digestive contents. Appl.


Chaucheyras-Durand F., Walker, N.D. and Bach, A., 2008. Effects of active dry yeasts on the

rumen microbial ecosystem: past, present and future. Anim.Feed Sci. Technol.145: 5-26.

Chaucheyras-Durand, F. and Ossa, F. 2014. Review: The rumen microbiome: Composition,

abundance, diversity, and new investigative tools. Prof. Anim. Sci. 30: 1–12.

http://doi.org/10.15232/S1080-7446(15)30076-0

Chawley, P., Samal, H. B., Prava, J., Suar, M. and Mahapatra, R. K. 2014. Comparative

genomics study for identification of drug and vaccine targets in Vibrio cholerae: MurA

ligase as a case study. Genomics.103: 83–93. http://doi.org/10.1016/j.ygeno.2013.12.002

Chen, J., Yang, C., Tizioto, P. C., Huang, H., Lee, M. O. K., Payne, H. R. et al. 2016.

Expression of the Bovine NK-Lysin Gene Family and Activity against Respiratory

Pathogens. PLoS ONE. 11: e0158882. http://doi.org/10.1371/journal.pone.0158882

Citorik, R. J., Mimee, M. and Lu, T. K. 2014. Sequence-specific antimicrobials using efficiently

delivered RNA-guided nucleases. Nat. Biotechnol. 32: 1141–1145.

http://doi.org/10.1038/nbt.3011

Cornelissen, A., Ceyssens, P.-J., Krylov, V. N., Noben, J.-P., Volckaert, G. and Lavigne, R.

2012. Identification of EPS-degrading activity within the tail spikes of the novel

Pseudomonas putida phage AF. Virology. 434: 251–256.

Page 35 of 52



For Review O

nly

36

http://doi.org/10.1016/j.virol.2012.09.030

D’Costa, V. M., McGrann, K. M., Hughes, D. W. and Wright, G. D. 2006. Sampling the antibiotic

resistome. Science. 311: 374–377. http://doi.org/10.1126/science.1120800

Craig, J.W., Chang, F.Y., Kim, J.H., Obiajulu, S.C. and Brady, S.F.. 2010. Expanding small-

molecule functional metagenomics through parallel screening of broad-host-range cosmid

environmental DNA libraries in diverse proteobacteria. Appl. Environ. Microbio.l 76: 1633–

1641.

Davis, M. A., Besser, T. E., Orfe, L. H., Baker, K. N. K., Lanier, A. S., Broschat, S. L.et al. 2011.

Genotypic-phenotypic discrepancies between antibiotic resistance characteristics of

Escherichia coli from calves in high and low antibiotic use management settings. Appl.

Environ. Microbiol. 77: 3293-3299 http://doi.org/10.1128/AEM.02588-10

Dean, S. N., Bishop, B. M. and van Hoek, M. L. 2011. Natural and synthetic cathelicidin

peptides with anti-microbial and anti-biofilm activity against Staphylococcus aureus. BMC

Microbiol. 11: 114. http://doi.org/10.1186/1471-2180-11-114

DiGiandomenico, A., Warrener, P., Hamilton, M., Guillard, S., Ravn, P., Minter, R. et al. 2012.

Identification of broadly protective human antibodies to Pseudomonas aeruginosa

exopolysaccharide Psl by phenotypic screening. J. Exp. Med. 209: 1273–1287.

http://doi.org/10.1084/jem.20120033

Drulis-Kawa, Z., Majkowska-Skrobek, G., Maciejewska, B., Delattre, A.-S., & Lavigne, R. 2012.

Learning from Bacteriophages - Advantages and limitations of phage and phage-encoded

protein applications. Curr Protein Pept Sci. 13(8), 699–722.

http://doi.org/10.2174/138920312804871193

Durso, L. M., Harhay, G. P., Bono, J. L. and Smith, T. P. L. 2011. Virulence-associated and

antibiotic resistance genes of microbial populations in cattle feces analyzed using a

metagenomic approach. J. Microbiol. Methods 84: 278–282.

http://doi.org/10.1016/j.mimet.2010.12.008

Page 36 of 52



For Review O

nly

37

Dutta, A., Singh, S. K., Ghosh, P., Mukherjee, R., Mitter, S. and Bandyopadhyay, D. 2006. In

silico identification of potential therapeutic targets in the human pathogen Helicobacter

pylori. In Silico Biol. 6: 43–47. Retrieved from

http://www.ncbi.nlm.nih.gov/pubmed/16789912

Eckert, S.E., Dziva, F., Chaudhuri, R.R., Langridge, G.C., Turner, D.J., Pickard, D.J., Maskell,

D.J., Thomson, N.R. and Stevens, M.P. 2011. Retrospective application of transposon-

directed insertion site sequencing to a library of signature-tagged mini-Tn5Km2 mutants of

Escherichia coli O157:H7 screened in cattle. J. Bacteriol. 193:1771-1776. doi:

10.1128/JB.01292-10.

Eckhart, L., Fischer, H., Barken, K. B., Tolker-Nielsen, T. and Tschachler, E. 2007. DNase1L2

suppresses biofilm formation by Pseudomonas aeruginosa and Staphylococcus aureus. Br.

J. Dermatol 156: 1342–1345. http://doi.org/10.1111/j.1365-2133.2007.07886.x

Edgar, R., Friedman, N., Molshanski-Mor, S. and Qimron, U. 2012. Reversing bacterial

resistance to antibiotics by phage-mediated delivery of dominant sensitive genes. Appl.


Ekkers, D. M., Cretoiu, M. S., Kielak, A. M. and Van Elsas, J. D. 2012. The great screen

anomaly-a new frontier in product discovery through functional metagenomics. Appl.

Microbiol. Biotechnol. 93: 1005-1020. http://doi.org/10.1007/s00253-011-3804-3

Eyre, D. W., Golubchik, T., Gordon, N. C., Bowden, R., Piazza, P., Batty, E. M. et al. 2012. A

pilot study of rapid benchtop sequencing of Staphylococcus aureus and Clostridium difficile

for outbreak detection and surveillance. BMJ Open. 2: e001124–e001124.

http://doi.org/10.1136/bmjopen-2012-001124

Falconer, S. B. and Brown, E. D. 2009. New screens and targets in antibacterial drug discovery.

Curr. Opin.Microbiol. 12: 497-450 http://doi.org/10.1016/j.mib.2009.07.001

Fernández-Alarcón, C., Singer, R. S. and Johnson, T. J. 2011. Comparative genomics of

multidrug resistance-encoding IncA/C plasmids from commensal and pathogenic

Page 37 of 52



For Review O

nly

38

Escherichia coli from multiple animal sources. PloS One. 6: e23415.


Fouhy, F., Stanton, C., Cotter, P. D., Hill, C. and Walsh, F. 2015. Proteomics as the final step in

the functional metagenomics study of antimicrobial resistance. Front. Microbiol. 6: 172.

http://doi.org/10.3389/fmicb.2015.00172

Gabor, E.M., Alkema, W.B.L. and Janssen, D.B. 2004. Quantifying the accessibility of the

metagenome by random expression cloning techniques. Environ. Microbiol. 6: 879-886.

Gabrani, R., Sharma, G., Dang, S. and Gupta, S. 2015. Interplay among bacterial resistance,

biofilm formation and oxidative stress for nosocomial infections. In Free Radicals in Human

Health and Disease (pp. 369–379). http://doi.org/10.1007/978-81-322-2035-0_23

Galvez, A., Lopez, R. L., Abriouel, H., Valdivia, E. and Omar, N. Ben. 2008. Application of

bacteriocins in the control of foodborne pathogenic and spoilage bacteria. Crit. Rev.

Biotechnol., 28: 125–152. http://doi.org/10.1080/07388550802107202

Gerdes, S. Y., Scholle, M. D., Souza, M. D., Baev, M. V, Farrell, M., Kurnasov, O. V. et al. 2002.

From Genetic Footprinting to Antimicrobial Drug Targets : Examples in Cofactor

Biosynthetic Pathways From Genetic Footprinting to Antimicrobial Drug Targets : Examples

in Cofactor Biosynthetic Pathways. J. Bacteriol. 184:4555–4572.

http://doi.org/10.1128/JB.184.16.4555

Gökçen, A., Vilcinskas, A. and Wiesner, J. 2014. Biofilm-degrading enzymes from Lysobacter

gummosus. Virulence. 5: 378–387. http://doi.org/10.4161/viru.27919

Goodwin, S., McPherson, J. D. and McCombie, W. R. 2016. Coming of age: ten years of next-

generation sequencing technologies. Nat. Rev. Genet.17: 333–351.

http://doi.org/10.1038/nrg.2016.49

Gordon, N. C., Price, J. R., Cole, K., Everitt, R., Morgan, M., Finney, J. et al. 2014. Prediction of

staphylococcus aureus antimicrobial resistance by whole-genome sequencing. J. Clin.

Microbiol. 52: 1182–1191. http://doi.org/10.1128/JCM.03117-13

Page 38 of 52



For Review O

nly

39

Gupta, S. K., Padmanabhan, B. R., Diene, S. M., Lopez-Rojas, R., Kempf, M., Landraud, L. and

Rolain, J. M. 2014. ARG-annot, a new bioinformatic tool to discover antibiotic resistance

genes in bacterial genomes. Antimicrob. Agents Chemother. 58: 212–220.

http://doi.org/10.1128/AAC.01310-13

Harrison, E. M., Paterson, G. K., Holden, M. T. G., Larsen, J., Stegger, M., Larsen, A. R., ]

Holmes, M. A. 2013. Whole genome sequencing identifies zoonotic transmission of MRSA

isolates with the novel mecA homologue mecC. EMBO Mol. Med. 5: 509–515.

http://doi.org/10.1002/emmm.201202413

Hao, H., Cheng, G., Iqbal, Z., Ai, X., Hussain, H. I., Huang, L.Dai, M., Wang, Y., Liu, Z. and

Yuan, Z. 2014. Benefits and risks of antimicrobial use in food-producing animals.

Front.Microbiol. 5: 288. doi:10.3389/fmicb.2014.00288

Hatfull, G. F. 2010. Mycobacteriophages: genes and genomes. Ann. Rev. Microbio. 64: 331–

356. http://doi.org/10.1146/annurev.micro.112408.134233

Hayek, N. 2013. Lateral transfer and GC content of bacterial resistance genes. Front. Microbiol.

4: 1–3. http://doi.org/10.3389/fmicb.2013.00041

Hegstad, K., Mikalsen, T., Coque, T. M., Werner, G. and Sundsfjord, A. 2010. Mobile genetic

elements and their contribution to the emergence of antimicrobial resistant Enterococcus

faecalis and Enterococcus faecium. Clin. Microbiol. Infect. 16: 541–554.

http://doi.org/10.1111/j.1469-0691.2010.03226.x

Hu, Y., Yang, X., Qin, J., Lu, N., Cheng, G., Wu, N. et al. 2013. Metagenome-wide analysis of

antibiotic resistance genes in a large cohort of human gut microbiota. Nat. Commun. 4:

2151. http://doi.org/10.1038/ncomms3151

Huttner, A., Harbarth, S., Carlet, J., Cosgrove, S., Goossens, H., Holmes, A. et al. 2013.

Antimicrobial resistance: a global view from the 2013 World Healthcare-Associated

Infections Forum. Antimicrob. Res. Infect. Con. 2: 31 doi:10.1186/2047-2994-2-31

Ingolia, N. T. 2014. Ribosome profiling: new views of translation, from single codons to genome

Page 39 of 52



For Review O

nly

40

scale. Nature Rev. Genet. 15:205–213.

Iqbal, H.A., Craig, J.W. and Brady, S.F. 2014. Antibacterial enzymes from the functional

screening of metagenomic libraries hosted in Ralstonia metallidurans. FEMS Microbiol.

Lett. 354: 19-26

Javed, M. A., Sacher, J. C., van Alphen, L. B., Patry, R. T. and Szymanski, C. M. 2015. A

Flagellar Glycan-Specific Protein Encoded by Campylobacter Phages Inhibits Host Cell

Growth. Viruses 7: 6661–74. http://doi.org/10.3390/v7122964

Jenssen, H., Hamill, P. and Hancock, R. E. W. 2006. Peptide antimicrobial agents. Clin.

Microbiol. Rev.. 19: 491-511. http://doi.org/10.1128/CMR.00056-05

Ji, Y., Zhang, B., Van, S. F., Horn, Warren, P., Woodnutt, G. et al. 2001. Identification of Critical

Staphylococcal Genes Using Conditional Phenotypes Generated by Antisense RNA.

Science. 293: 2266-2269.

Johnson, R.P., Gyles, C.L., Huff, W.E., Ojha, S., Huff, G.R., Rath, N.C. and Donoghue, A.M.

2008. Bacteriophages for prophylaxis and therapy in cattle, poultry and pigs. Anim. Health

Res. Rev. 9: 201-215. doi: 10.1017/S1466252308001576.

Johri, A. K., Paoletti, L. C., Glaser, P., Dua, M., Sharma, P. K., Grandi, G. and Rappuoli, R.

2006. Group B Streptococcus: global incidence and vaccine development. Nat. Rev.

Microbiol. 4: 932–942. http://doi.org/10.1038/nrmicro1552

Jones, D. S., Podolsky, S. H. and Greene, J. a. 2012. The Burden of Disease and the Changing

Task of Medicine. New Eng. J. Med. 366: 2333–2338.

http://doi.org/10.1056/NEJMp1113569

Karimi, M., Mirshekari, H., Moosavi Basri, S. M., Bahrami, S., Moghoofei, M. and Hamblin, M. R.

2016. Bacteriophages and phage-inspired nanocarriers for targeted delivery of therapeutic

cargos. Adv. Drug Deliv. Rev. 106: 45–62. http://doi.org/10.1016/j.addr.2016.03.003

Khairnar, K., Raut, M. P., Chandekar, R. H., Sanmukh, S. G. and Paunikar, W. N. 2013. Novel

bacteriophage therapy for controlling metallo-beta-lactamase producing Pseudomonas

Page 40 of 52



For Review O

nly

41

aeruginosa infection in catfish. BMC Vet. Res. 9: 264. http://doi.org/10.1186/1746-6148-9-

264

Kjos, M., Miller, E., Slager, J., Lake, F. B., Gericke, O., Roberts, I. S., Rozen, D.E., Veening, J.-

W. 2016. Expression of Streptococcus pneumoniae Bacteriocins Is Induced by Antibiotics

via Regulatory Interplay with the Competence System. PLoS Path. 12: e1005422.

http://doi.org/10.1371/journal.ppat.1005422

Klima, C. L., Cook, S. R., Zaheer, R., Laing, C., Gannon, V. P., Xu, Y. et al. 2016. Comparative

genomic analysis of mannheimia haemolytica from bovine sources. PloS One. 11: 1–23.


Kos, V. N., Déraspe, M., McLaughlin, R. E., Whiteaker, J. D., Roy, P. H., Alm, R. A. et al. 2015.

The resistome of Pseudomonas seudomonas aeruginosa in relationship to phenotypic

susceptibility. Antimicrob. Agents Chemother. 59: 427–436.

http://doi.org/10.1128/AAC.03954-14

Köser, C. U., Ellington, M. J. and Peacock, S. J. 2014. Whole-genome sequencing to control

antimicrobial resistance. Trends Genet. 30: 401-407.

http://doi.org/10.1016/j.tig.2014.07.003

Kullen, M. J. and Klaenhammer, T. R. 2000. Genetic Modification of Intestinal Lactobacilli and

Bifidobacteria. Curr. Issues Mol. Biol. 2: 41–50.

Lampe, D. J., Akerley, B. J., Rubin, E. J., Mekalanos, J. J. and Robertson, H. M. 1999.

Hyperactive transposase mutants of the Himar1 mariner transposon. Proc. Natl. Acad. Sci.

USA 96:11428-11433.

Langille, M. G.I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., A Reyes, J.,

Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., Beiko, R. G, and

Huttenhower, C. 2013. Predictive functional profiling of microbial communities using 16S

rRNA marker gene sequences. Nat. Biotechnol. 31: 814–821 doi:10.1038/nbt.2676

Lee, D. S., Burd, H., Liu, J., Almaas, E., Wiest, O., Barabasi, A. L. et al. 2009. Comparative

Page 41 of 52



For Review O

nly

42

genome-scale metabolic reconstruction and flux balance analysis of multiple

Staphylococcus aureus genomes identify novel antimicrobial drug targets. J. Bacteriol. 191:

4015–4024. http://doi.org/10.1128/JB.01743-08

Lesmeister, K.E., Heinrichs, A.J. and Gabler, M.T. 2004. Effects of supplemental yeast

(Saccharomyces cerevisiae) culture on rumen development, growth characteristics, and

blood parameters in neonatal dairy calves. J. Dairy Sci. 87: 1832-1839.

Lewis, K. 2013. Platforms for antibiotic discovery. Nat. Rev. Drug Dis.12: 371–387.

http://doi.org/10.1038/nrd3975

Li, L., Wang, Q., Zhang, H., Yang, M., Khan, M.I. and Zhou, X. 2016. Sensor histidine kinase is

a beta-lactam receptor and induces resistance to beta-lactam antibiotics. Proc. Natl. Acad.

Sci. USA 113: 1648-1653.

Linnarsson, S. and Teichmann, S. A. 2016. Single-cell genomics: coming of age. Genome Biol.

17: 1–3.Liu, B. and Pop, M. 2009. ARDB--Antibiotic Resistance Genes Database. Nucleic

Acids Res. 37: D443-7. http://doi.org/10.1093/nar/gkn656

Loc Carrillo, C., Atterbury, R. J., El-Shibiny, A., Connerton, P. L., Dillon, E., Scott, A. and

Connerton, I. F. 2005. Bacteriophage therapy to reduce Campylobacter jejuni colonization

of broiler chickens. Appl. Environ. Microbiol. 71: 6554–6563.

http://doi.org/10.1128/AEM.71.11.6554-6563.2005

Loessner, M. J. 2005. Bacteriophage endolysins--current state of research and applications.

Curr. Opin. Microbiol. 8: 480–487. http://doi.org/10.1016/j.mib.2005.06.002

Lood, R., Winer, B. Y., Pelzek, A. J., Diez-Martinez, R., Thandar, M., Euler, C. W. et al. 2015.

Novel phage lysin capable of killing the multidrug-resistant gram-negative bacterium

Acinetobacter baumannii in a mouse bacteremia model. Antimicrob. Agents Chemother.

59: 1983–1991. http://doi.org/10.1128/AAC.04641-14

Looft, T., Johnson, T. A., Allen, H. K., Bayles, D. O., Alt, D. P., Stedtfeld, R. D. et al. 2012. In-

feed antibiotic effects on the swine intestinal microbiome. Proc. Natl. Acad. Sci. USA. 109:

Page 42 of 52



For Review O

nly

43

1691–1696. http://doi.org/10.1073/pnas.1120238109

Losada, L., DebRoy, C., Radune, D., Kim, M., Sanka, R., Brinkac, L. et al. 2016. Whole genome

sequencing of diverse Shiga toxin-producing and non-producing Escherichia coli strains

reveals a variety of virulence and novel antibiotic resistance plasmids. Plasmid. 83: 8–11.

http://doi.org/10.1016/j.plasmid.2015.12.001

Lundegaard, C., Lamberth, K., Harndahl, M., Buus, S., Lund, O. and Nielsen, M. 2008.

NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC

class I affinities for peptides of length 8-11. Nucleic Acids Res. 36: 509–512.

http://doi.org/10.1093/nar/gkn202

Ma, L., Xia, Y., Li, B., Yang, Y., Li, L. G., Tiedje, J. M. and Zhang, T. 2016. Metagenomic

Assembly Reveals Hosts of Antibiotic Resistance Genes and the Shared Resistome in Pig,

Chicken, and Human Feces. Environ. Sci. Technol. 50: 420–427.

http://doi.org/10.1021/acs.est.5b03522

Maione, D., Margarit, I., Rinaudo, C. D., Masignani, V., Mora, M., Scarselli, M. et al. 2005.

Identification of a universal Group B streptococcus vaccine by multiple genome screen.

Science. 309: 148–150. http://doi.org/10.1542/peds.2006-0900JJJJ

Makarova, K. S., Haft, D. H., Barrangou, R., Brouns, S. J. J., Charpentier, E., Horvath, P. et al.

2011. Evolution and classification of the CRISPR–Cas systems. Nat. Rev. Microbiol. 9:

467–477. http://doi.org/10.1038/nrmicro2577

Marraffini, L. A. and Sontheimer, E. J. 2010. CRISPR interference: RNA-directed adaptive

immunity in bacteria and archaea. Nat. Rev. Genet. 11: 181–190.

http://doi.org/10.1038/nrg2749

Marraffini, L. A. and Sontheimer, E. J. 2008. CRISPR interference limits horizontal gene transfer

in Staphylococci by targeting DNA. Science 322: 1843–1845.

http://doi:10.1126/science,1165771

Mather, A. E., Reid, S. W. J., Maskell, D. J., Parkhill, J., Fookes, M. C., Harris, S. R. et al. 2013.

Page 43 of 52



For Review O

nly

44

Distinguishable Epidemics of Multidrug-Resistant Salmonella Typhimurium DT104 in

Different Hosts. Science. 341: 1514-1517. doi: 10.1126/science.1240578

Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, EM, et al. 2008. The Metagenomics

RAST server - A public resource for the automatic phylogenetic and functional analysis of

metagenomes. BMC Bioinformatics 9: 386. doi: 10.1186/1471-2105-9-386

McArthur, A. G., Waglechner, N., Nizam, F., Yan, A., Azad, M. A., Baylay, A. J. et al. 2013. The

comprehensive antibiotic resistance database. Antimicrob. Agents Chemother. 57: 3348–

3357. http://doi.org/10.1128/AAC.00419-13

McPhillie, M. J., Cain, R. M., Narramore, S., Fishwick, C. W. G. and Simmons, K. J. 2015.

Computational Methods to Identify New Antibacterial Targets. Chem. Biol. Drug Des. 85:

22–29. http://doi.org/10.1111/cbdd.12385

Mobegi F. M., van Hijum S. A., Burghout P., Bootsma H. J., de Vries S. P., van der Gaast-de

Jongh C. E., et al. 2014. From microbial gene essentiality to novel antimicrobial drug

targets. BMC Genomics 15: 958 10.1186/1471-2164-15-95

Mosoni, P., Chaucheyras-Durand, F., Béra-Maillet, C. and Forano, E., 2007. Quantification by

real-time PCR of cellulolytic bacteria in the rumen of sheep after supplementation of a

forage diet with readily fermentable carbohydrates: effect of a yeast additive. J. Appl.

Microbiol. 103: 2676-2685.

Munoz-Lopez, M. and J. L. Garcia-Perez. 2010. DNA transposons: nature and applications in

genomics. Curr. Genomics 11: 115-128.

Musa, H., Wu, S., Zhu, C. and Zhu, G. 2009. The Potentialbenefits of probiotics in animal

production and health. J. Anim. Vet. Adv. 8: 313–321.

Navin, N., Kendall, J., Troge, J. Andrews, P., Rodgers, L., McIndoo, J. et al. 2011. Tumour

evolution inferred by single-cell sequencing. Nature 472: 90–94.

http://doi.org/10.1038/nature09807

Naylor, S. W. et al. 2005. Escherichia coli O157:H7 forms attaching and effacing lesions at the

Page 44 of 52



For Review O

nly

45

terminal rectum of cattle and colonization requires the LEE4 operon. Microbiology 151:

2773-2781.

Nelson, D., Loomis, L. and Fischetti, V. A. 2001. Prevention and elimination of upper respiratory

colonization of mice by group A streptococci by using a bacteriophage lytic enzyme. Proc.

Natl. Acad. Sci. USA, 98: 4107–12. http://doi.org/10.1073/pnas.061038398

Newbold, C.J., Wallace, R.J. and McIntosh, F.M., 1996. Mode of action of the yeast

Saccharomyces cerevisiae as a feed additive for ruminants. Br.J. Nut. 76: 249-261.

Noyes, N. R., Yang, X., Linke, L. M., Magnuson, R. J., Cook, S. R., Zaheer, R. et al. 2016.

Characterization of the resistome in manure, soil and wastewater from dairy and beef

production systems. Sci. Rep., 6: 24645. http://doi.org/10.1038/srep24645

O’Neill J. 2014. 2014. The Review on Antimicrobial Resistance. Antimicrobial Resistance:

Tackling a crisis for the health and wealth of nations. HM Government and Wellcome trust,

London.

Overhage, J., Campisano, A., Bains, M., Torfs, E. C. W., Rehm, B. H. A. and Hancock, R. E. W.

2008. Human host defense peptide LL-37 prevents bacterial biofilm formation. Infect.

Immun. 76: 4176–4182. http://doi.org/10.1128/IAI.00318-08

Ozsolak, F. and P. M. Milos. 2011. RNA sequencing: advances, challenges and opportunities.

Nat. Rev. Genet. 12: 87-98.

Panjkovich, A., Gibert, I., Daura, X., Arias, C., Murray, B., Baquero, F. et al. 2014. antibacTR:

dynamic antibacterial-drug-target ranking integrating comparative genomics, structural

analysis and experimental annotation. BMC Genomics. 15: 36. http://doi.org/10.1186/1471-

2164-15-36

Perron, G. G., Whyte, L., Turnbaugh, P. J., Goordial, J., Hanage, W. P., Dantas, G. and Desai,

M. M. 2015. Functional characterization of bacteria isolated from ancient arctic soil

exposes diverse resistance mechanisms to modern antibiotics. PloS One. 10: e0069533.


Page 45 of 52



For Review O

nly

46

Petersen, T. N., Brunak, S., von Heijne, G. and Nielsen, H. 2011. SignalP 4.0: discriminating

signal peptides from transmembrane regions. Nat. Meth. 8: 785–786.

http://doi.org/10.1038/nmeth.1701

Pires, D. P., Vilas Boas, D., Sillankorva, S. and Azeredo, J. 2015. Phage therapy: a step

forward in the treatment of Pseudomonas aeruginosa infections. J. Virol. 89: JVI.00385-15.

http://doi.org/10.1128/JVI.00385-15

Pizza, M., Scarlato, V., Masignani, V., Giuliani, M. M., Aricò, B., Comanducci, M. et al. 2000.

Identification of vaccine candidates against serogroup B meningococcus by whole-genome

sequencing. Science. 287: 1816–1820. http://doi.org/10.1126/science.287.5459.1816

Powers, J. P. S. and Hancock, R. E. W. 2003. The relationship between peptide structure and

antibacterial activity. Peptides. 24: 1681-1691.

http://doi.org/10.1016/j.peptides.2003.08.023

Price, L. B., Stegger, M., Hasman, H., Aziz, M., Larsen, J., Andersen, S. and Pearson, T. 2012.

Adaptation and emergence of Staphylococcus aureus CC39: Host adaptation and

emergence of methicillin resistance in livestock. mBio. 3: 1–6.

http://doi.org/10.1128/mBio.00305-11.Editor

Punina, N. V, Makridakis, N. M., Remnev, M. A. and Topunov, A. F. 2015. Whole-genome

sequencing targets drug-resistant bacterial infections. Hum. Genom. 9: 19.

http://doi.org/10.1186/s40246-015-0037-z

Raghunathan, A., Ferguson, H. R., Bornarth, C. J., Song, W., Driscoll, M. and Lasken, R. S.

005. Genomic DNA amplification from a single bacterium. Appl. Environ. Microbiol., 71:

3342–3347. http://doi.org/10.1128/AEM.71.6.3342-3347.2005

Rasko, D. a., Rosovitz, M. J., Myers, G. S. a., Mongodin, E. F., Fricke, W. F., Gajer, P. et al.

2008. The Pangenome Structure of Escherichia coli: Comparative Genomic Analysis of E.

coli Commensal and Pathogenic Isolates. J. Bacteriol. 190: 6881–6893.

http://doi.org/10.1128/JB.00619-08

Page 46 of 52



For Review O

nly

47

Roemer, T. and Boone, C. 2013. Systems-level antimicrobial drug and drug synergy discovery.

Nat. Chem. Biol. 9: 222–231. http://doi.org/10.1038/nchembio.1205

Sauer, K. 200). The genomics and proteomics of biofilm formation. Genome Biol. 4: 219.

http://doi.org/10.1186/gb-2003-4-6-219

Schmelcher, M. and Loessner, M. J. 2016. Bacteriophage endolysins: Applications for food

safety. Curr. Opin, Biotechnol. 37: 76 87. http://doi.org/10.1016/j.copbio.2015.10.005

Scholl, D. and Martin, D. W. 2008. Antibacterial efficacy of R-type pyocins towards

Pseudomonas aeruginosa in a murine peritonitis model. Antimicrob. Agents Chemother.

52: 1647–1652. http://doi.org/10.1128/AAC.01479-07

Sebaihia, M., Wren, B. W., Mullany, P., Fairweather, N. F., Minton, N., Stabler, R. et al. 2006.

The multidrug-resistant human pathogen Clostridium difficile has a highly mobile, mosaic

genome. Nat. Gen. 38: 779–786. http://doi.org/10.1038/ng1830

Seib, K. L., Zhao, X. and Rappuoli, R. 2012. Developing vaccines in the era of genomics: A

decade of reverse vaccinology. Clin. Microbiol. Infect. 18: 109–116.

http://doi.org/10.1111/j.1469-0691.2012.03939.x

Shan, Y., Lazinski, D., Rowe, S., Camilli. A. and Lewis, K. 2015. Genetic basis of persister

tolerance to aminoglycosides in Escherichia coli. mBio. 6: pii: e00078-15.

Singh, K. M., Jakhesara, S. J., Koringa, P. G., Rank, D. N. and Joshi, C. G. 2012. Metagenomic

analysis of virulence-associated and antibiotic resistance genes of microbes in rumen of

Indian buffalo (Bubalus bubalis). Gene. 507: 146–151.

http://doi.org/10.1016/j.gene.2012.07.037

Smith, D. R. 2014. Vaccination of Cattle against Escherichia coli O157:H7. Microbiol Spectr 2

doi: 10.1128/microbiolspec.EHEC-0006-2013.

Stephens, T. P., Loneragan, G. H., Karunasena, E. and Brashears, M. M. 2007. Reduction of

Escherichia coli O157 and Salmonella in feces and on hides of feedlot cattle using various

doses of a direct-fed microbial. J Food Prot. 70: 2386–2391.

Page 47 of 52



For Review O

nly

48

Stoesser, N., Batty, E. M., Eyre, D. W., Morgan, M., Wyllie, D. H., Del Ojo Elias, C.,et al. 2013.

Predicting antimicrobial susceptibilities for Escherichia coli and Klebsiella pneumoniae

isolates using whole genomic sequence data. J. Antimicrob. Chemother. 68: 2234–2244.

http://doi.org/10.1093/jac/dkt180

Sun, D., Accavitti, M. A and Bryers, J. D. 2005. Inhibition of biofilm formation by monoclonal

antibodies against Staphylococcus epidermidis RP62A accumulation-associated protein.

Diagn. Clin. Immunol.. 12: 93–100. http://doi.org/10.1128/CDLI.12.1.93-100.2005

Taha-Abdelaziz, K., Perez-Casal, J., Schott, C., Hsiao, J., Attah-Poku, S., Slavić, D. and

Caswell, J. L. 2013. Bactericidal activity of tracheal antimicrobial peptide against

respiratory pathogens of cattle. Vet. Immunol. Immunopathol. 152: 289–294.

http://doi.org/10.1016/j.vetimm.2012.12.016

Takemura-Uchiyama, I., Uchiyama, J., Osanai, M., Morimoto, N., Asagiri, T., Ujihara, T. et al.

2014. Experimental phage therapy against lethal lung-derived septicemia caused by

Staphylococcus aureus in mice. Microbes and Infection / Institut Pasteur. 16: 512–7.

http://doi.org/10.1016/j.micinf.2014.02.011

Thanassi, J. A., Hartman-Neumann, S. L., Dougherty, T. J., Dougherty, B. A. and Pucci, M. J.

2002. Identification of 113 conserved essential genes using a high-throughput gene

disruption system in Streptococcus pneumoniae. Nucleic Acids Res. 30: 3152–3162.

http://doi.org/10.1093/nar/gkf418

Troeschel, S.C., Thies, S., Link, O., Real, C.I., Knops, K., Wilhelm, S., Rosenau, F. and Jaeger,

K.E. 2012. Novel broad host range shuttle vectors for expression in Escherichia coli,

Bacillus subtilis and Pseudomonas putida. J. Biotechnol. 161:71-79

Turner, M. S., Waldherr, F., Loessner, M. J. and Giffard, P. M. 2007. Antimicrobial activity of

lysostaphin and a Listeria monocytogenes bacteriophage endolysin produced and secreted

by lactic acid bacteria. Syst. App.l Microbiol.. 30: 58–67.

http://doi.org/10.1016/j.syapm.2006.01.013

Page 48 of 52



For Review O

nly

49

Uchiyama, T. and Miyazaki, K. 2009. Functional metagenomics for enzyme discovery:

challenges to efficient screening. Curr. Opin. Biotechnol. 20: 616-622.

van Opijnen, T. and A. Camilli. 2013. Transposon insertion sequencing: a new tool for systems-

level analysis of microorganisms. Nat. Rev. Microbiol. 11: 435-442.

van Opijnen, T., Bodi, K. L. and Camilli, A. 2009. Tn-seq: high-throughput parallel sequencing

for fitness and genetic interaction studies in microorganisms. Nat. Meth. 6: 767-772.

Wilhelm, B.T. and Landry, J.R 2009. RNA-Seq-quantitative measurement of expression through

massively parallel RNA-sequencing. Methods 48: 249–257

Wallner, G., Fuchs, B., Spring, S., Beisker, W. and Amann, R. 1997. Flow sorting of

microorganisms for molecular analysis. Appl. Environ. Microbiol. 63: 4223–4231.

Waseh, S., Hanifi-Moghaddam, P., Coleman, R., Masotti, M., Ryan, S., Foss, M. et al. 2010.

Orally administered P22 phage tailspike protein reduces Salmonella colonization in

chickens: Prospects of a novel therapy against bacterial infections. PloS One. 5:. e13904


Westermann, A.J., Gorski, S.A. and Vogel, J. 2012. Dual RNA-seq of pathogen and host. Nat.

Rev. Microbiol. 10: 618-30. doi: 10.1038/nrmicro2852.

Westwater, C., Kasman, L. M., Schofield, D. A., Werner, P. A., Dolan, J. W., Schmidt, M. G. and

Norris, J. S. 2003. Use of genetically engineered phage to deliver antimicrobial agents to

bacteria: an alternative therapy for treatment of bacterial infections. Antimicrob. Agents

Chemother. 47: 1301–1307. http://doi.org/10.1128/AAC.47.4.1301-1307.2003

White, R.A., Bottos, E.M., Roy C.T., Zucker, J.D., Brislawn, C.J., Nicora, C.D., Fansler, S.J.,

Glaesemann, K.R. et al. 2016. Moleculo Long-Read Sequencing Facilitates Assembly and

Genomic Binning from Complex Soil Metagenomes. doi:10.1128/mSystems. mSystems 1:

e00045-16

White, T. A. and Kell, D. B. 2004. Comparative Genomic Assessment of Novel Broad-Spectrum

Targets for Antibacterial Drugs. Comp. Funct. Gen. 5: 304–327.

Page 49 of 52



For Review O

nly

50

http://doi.org/10.1002/cfg.411

Wizemann, T. M., Heinrichs, J. H., Adamou, J. E., Erwin, A. L., Kunsch, C., Choi, G. H.et al.

2001. Use of a whole genome approach to identify vaccine molecules affording protection

against Streptococcus pneumoniae infection. Infect. Immun. 69: 1593–1598.

http://doi.org/10.1128/IAI.69.3.1593-1598.2001

Yankson, K. K. and Steck, T. R. 2009. Strategy for extracting DNA from clay soil and detecting a

specific target sequence via selective enrichment and real-time (quantitative) PCR

amplification. Appl. Environ. Microbiol. 75: 6017–6021. http://doi.org/10.1128/AEM.00211-

09

Yarus, S., Rosen, J. M., Cole, A. M. and Diamond, G. 1996. Production of active bovine tracheal

antimicrobial peptide in milk of transgenic mice (␤-defensin͞antibiotic͞whey acidic protein).

Microbiology. 93: 14118–14121.

You, Y. and Silbergeld, E. K. 2014. Learning from agriculture: Understanding low-dose

antimicrobials as drivers of resistome expansion. Front. Microbiol. 5: 1–10.

http://doi.org/10.3389/fmicb.2014.00284

Younts-Dahl, S. M., Osborn, G. D., Galyean, M. L., Rivera, J. D., Loneragan, G. H. and

Brashears, M. M. 2005. Reduction of Escherichia coli O157 in finishing beef cattle by

various doses of Lactobacillus acidophilus in direct-fed microbials. J. Food Prot. 68: 6–10.

Zankari, E., Hasman, H., Cosentino, S., Vestergaard, M., Rasmussen, S., Lund, O.et al. 2012.

Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67:

2640–2644. http://doi.org/10.1093/jac/dks261

Zhu, Y.-G., Johnson, T. A., Su, J.-Q., Qiao, M., Guo, G.-X., Stedtfeld, R. D. et al. 2013. Diverse

and abundant antibiotic resistance genes in Chinese swine farms. Proc. Natl. Acad. Sci.

USA 110: 3435–40. http://doi.org/10.1073/pnas.1222743110

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Figure 1. Genomic approaches to characterizing and mitigating antimicrobial resistance (AMR). A modern

arsenal of complementary sequence-based technologies enable precision omics-based analyses of both

cultured and uncultured (metagenomic) bacterial samples. Genomics-based analyses have a multitude of

uses, including: (I) the detection, identification, and characterization of antimicrobial resistance genes

(ARGs), mobile genetic elements (MGE) and drug-resistant pathogens, (II) the design of diagnostics,

including for AMR surveillance and for monitoring the transmission of AMR pathogens and ARGs, and (III)

discovery of new antimicrobials and non-antimicrobial approaches to pathogen control.

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For Review Only · 2017. 7. 25. · For Review Only 1 Genomic approaches to characterizing and reducing antimicrobial resistance in beef cattle production systems Cassidy Klima 1,2,

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