Mitigation of Antimicrobial Drug Resistance and Changes in the Microbiome of Feedlot Cattle Supplemented with Lactobacillus salivarius L28 as an Alternative to Sub-Therapeutic Antibiotics
by
Andrea R. English, M.S.
A Dissertation
In
Animal Science
Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY
Approved
Dr. Marcos X. Sánchez-Plata Co-Chair of Committee
Dr. Mindy M. Brashears Co-Chair of Committee
Dr. Kendra K. Nightingale
Dr. Alejandro Echeverry
Dr. Jessie Vipham
Dr. Mark F. Miller
Mark Sheridan Dean of the Graduate School
December, 2019
Copyright 2019, Andrea English
Texas Tech University, Andrea English, December 2019
ACKNOWLEDGMENTS
I would like to first thank my advisor Dr. Mindy Brashears you took a chance on me and I am forever grateful. I am so thankful that I was able to learn and grow as a scientist under your guidance. You have provided me with experiences I will always remember. Thank you for everything you have done for me while at Texas Tech.
Thank you to my committee, Dr. Sanchez you have been an incredible mentor and I have truly valued absorbing as much knowledge as I possibly could from you. Dr.
Nightingale thank you for investing in me and this project. You pushed me to be a better scientist which I very much appreciate. Dr. Echeverry and Dr. Vipham I cannot say thank you enough for the support you have provided me. You each have always made time for me and that is invaluable. Dr. Miller thank you for continuously providing me with opportunities to grow, learn and dabble in meat science while here at Texas Tech.
A very special thank you is warranted to Dr. Handley from Washington
University School of Medicine your support and assistance with data analysis and interpretation of the microbiome work is greatly appreciated.
Thank you to the graduate students that helped with the completion of this project specifically Keelyn Hanlon, Hector Garnica you both were there for nearly every fecal collection, streaked plate, sensititre run and swab collection. I could not have done it without you both. Diana Ayala your guidance, friendship and assistance during my time in Lubbock is unmeasurable (the cytokine analysis would not have been done without you). I am extremely thankful for you each.
ii Texas Tech University, Andrea English, December 2019
A huge thank you to my family. Mom and Dad you both have been my main support system. You have never questioned my goals and have only encouraged me to reach even further. Most importantly thank you for your unconditional love and always reminding me that “I can do all things through Christ who strengthens me”
Philippians 4:13.
iii Texas Tech University, Andrea English, December 2019
TABLE OF CONTENTS ACKNOWLEDGMENTS ...... ii
ABSTRACT ...... vi
LIST OF TABLES ...... viii
LIST OF FIGURES ...... x
1. REVIEW OF LITERATURE ...... 1
Introduction ...... 1 Antimicrobial Drugs Mode of Action ...... 3 Cell Wall synthesis ...... 3 Protein synthesis ...... 4 Nucleic Acid metabolism ...... 5 Membrane structure ...... 6 Antimicrobial Resistant Bacteria ...... 6 Enterococcus spp...... 10 Salmonella and Escherichia coli ...... 19 Antibiotic Use and Resistance in Beef Production ...... 26 Background ...... 26 Antimicrobial Resistance in Beef Production ...... 29 Microbial Diversity in Feedlot Cattle Feces ...... 37 Alternatives to Antimicrobial Growth Promoters ...... 39 Direct-fed Microbials ...... 42 Background ...... 42 Lactic Acid Bacteria ...... 44 Direct-fed Microbials in Cattle and Controlling Pathogen Shedding ...... 45 Direct-fed Microbials and Cattle Health ...... 48 Conclusion and Objectives ...... 52 2. DETECTION OF ESCHERICHIA COLI O157 AND SALMONELLA FROM FEEDLOT CATTLE FECES AND FROM CARCASSES SUPPLEMENTED WITH LACTOBACILLUS SALIVARIUS L28 AS A DIRECT-FED MICROBIAL ...... 54
Abstract ...... 54 Highlights ...... 56 Introduction ...... 57 Material and Methods ...... 59 iv Texas Tech University, Andrea English, December 2019
Results and Discussion ...... 65 E. coli O157 Detection...... 65 Salmonella Detection...... 67 Postharvest...... 70 Conclusion ...... 71 3. ANTIMICROBIAL RESISTANCE PATTERNS AND MICROBIOME CHANGES IN FEEDLOT CATTLE SUPPLEMENTED WITH LACTOBACILLUS SALIVARIUS L28 ...... 77
Abstract ...... 77 Highlights ...... 79 Introduction ...... 80 Material and Methods ...... 84 Results and Discussion ...... 92 Generic E. coli ...... 92 Enterococcus ...... 96 E. coli O157 ...... 100 Salmonella ...... 102 Microbiome ...... 104 Conclusion ...... 107 REFERENCES ...... 135
APPENDIX A ...... 154
Sensitire™ Plate schematics ...... 154
v Texas Tech University, Andrea English, December 2019
ABSTRACT
The objectives of this study were to evaluate the efficacy of utilizing L. salivarius L28 as a direct-fed microbial in feedlot cattle to 1) reduce the frequency of pathogen shedding, 2) determine the AMR in commensal and pathogenic bacteria and investigate the microbiome and cytokine changes. A total of three dietary treatments
(control, base and monpro) based on conventional high concentrate diets were fed to finish cattle (n=144) for harvest. Fecal samples were collected every 28 days until harvest at 140 days. Traditional culture methods were used to isolate bacteria.
Antibiotic susceptibility testing was done using the micro-broth dilution (Sensititre™) susceptibility minimum inhibitory concentration plates, following the National
Antimicrobial Resistance Monitoring System protocol. For microbiome analysis
DNA was extracted from fecal samples and 16s-metageomics was performed on an
Illumina Miseq. Blood samples were collected for cytokine and chemokine analysis on day 140 from 1 animal per pen.
The base treatment group shed significantly less (10.4%; P = 0.04) E. coli
O157 compared the control (19.4%) and monpro (12.0%) groups. No significant differences were detected across treatment for the presence of Salmonella over time.
At slaughter, one carcass tested positive for E. coli O145 at pre-evisceration. Overall resistance for generic E. coli to at least one antimicrobial for each treatment group was
39.8%, 44.2% and 18.3% for treatments base, control and monpro, respectively.
Enterococcus resistance was much higher overall with base, control and monpro having 87.8%, 93%, and 83.3% of isolates with resistance, respectively. Enterococcus isolates were most commonly resistant to lincomycin, tetracycline, tylosin tartrate, vi Texas Tech University, Andrea English, December 2019 daptomycin and erythromycin. Relative abundance of bacteria in the phyla
Bacteroidetes increase over time with the overall reduction in relative abundance of all other bacteria. There were no changes based on treatment following application of either control or monpro regiments. For the base treatment group there was the greatest changes over time. No differences across treatments were detected for cytokine and chemokine quantification.
Cattle supplemented with sub-therapeutic antimicrobials had a higher rate of E. coli O157 present in the feces. While feeding L28 or tylosin had no effect on the presence of Salmonella. Supplementation of cattle with L28 was the most effective at mitigating the AMR of generic E. coli. However, marginal effects on the AMR of
Enterococcus isolates collected. Supplementation had no apparent negative effects on the microbiome or cytokine levels of cattle during the feeding trial.
vii Texas Tech University, Andrea English, December 2019
LIST OF TABLES 1. Treatment description and formulation with all diets based on a traditional finishing diet...... 60 2. Overall odds ratios (OR), relative risk (RR) for potential positives, 95% confidence intervals (CI) for the RR and P values for the likelihood of fecal shedding of E. coli O157 and Salmonella in control and monpro treatments compared to the base treatment from October 2016 to March 2017...... 75 3. Visual representation of samples testing positive for E. coli O157 and/or Salmonella by pen number and sampling time. Significant difference was set at (P < 0.10) comparing north and south pen presence for E. coli O157 (E), Salmonella (S), both Salmonella and E. coli O157 (S/E)...... 76 4. Antimicrobial class, antimicrobial agent, abbreviations and minimum inhibitory concentrations (µg/ml) for Salmonella and E. coli...... 88 5. Antimicrobial class, antimicrobial agent, abbreviations and minimum inhibitory concentrations (µg/ml) for Enterococcus...... 89 6. Distribution of generic E. coli AMR frequency considering percentage within each treatment, collection and number of antimicrobials with resistance...... 112 7. Minimum inhibitory concentrations and number of generic E. coli isolates with resistance to specific antimicrobials tested over the entire feeding trial...... 112 8. Patterns of AMR for generic E. coli isolates from feedlot cattle...... 114 9. Distribution of Enterococcus AMR frequency considering percentage within each treatment, collection and number of antimicrobials with resistance...... 117 10. Minimum inhibitory concentrations and number of Enterococcus isolates with resistance to specific antimicrobials tested over the entire feeding trial...... 118 11. Antibiotic resistance patterns of Enterococcus isolates from feedlot cattle...... 120 12. Antimicrobial resistance genes and the drug classes they confer resistance to present in E. coli O157 isolates collected from feedlot cattle...... 121 13. Antimicrobial resistance genes and phenotypic resistance present in Salmonella isolates collected from feedlot cattle...... 122 14. Standard curves for cytokine and chemokines analyzed...... 132 viii Texas Tech University, Andrea English, December 2019
15. Cytokines mean concentrations (pg/ml), standard deviation, 95% confidence limit and p-values (P<0.1) for each treatment at the end of the feeding period...... 133 16. Chemokine mean concentrations (pg/ml), standard deviation, 95% confidence limit and P-values (P<0.1) for each treatment at the end of the feeding period...... 134
ix Texas Tech University, Andrea English, December 2019
LIST OF FIGURES 1. Overall detection of E. coli O157 and Salmonella from fecal samples collected from October 2016 to March 2017...... 72 2. Detection of E. coli O157 from fecal samples collected from October 2016 to March 2017...... 73 3. Detection of Salmonella from fecal samples collected from October 2016 to March 2017...... 74 4. Total percent of generic E. coli isolates with resistance to one or more antimicrobials tested over the entire feeding trial. Columns with different letters represent significant differences (P < 0.10)...... 110 5. Percent of generic E. coli isolates resistant to one or more antimicrobials tested over time. Columns with different letters represent significant differences (P < 0.10)...... 110 6. Percent of generic E. coli isolates with resistance to up to 3 antimicrobials collected during the feeding trial...... 111 7. Number of AMR generic E. coli to specific antimicrobial classes by treatment...... 113 8. Total percentage of Enterococcus isolates with resistance to one or more antimicrobial tested over the entire feeding trial. Columns with different letters represent significant differences (P < 0.10)...... 115 9. Percent of Enterococcus isolates resistant to one or more antimicrobials tested over time. Columns with different letters represent significant differences (P < 0.10)...... 115 10. Percent of Enterococcus isolates with resistance to up to 3 antimicrobials collected during the feeding trial...... 116 11. Number of AMR Enterococcus to specific antimicrobial classes...... 119 12. The number of unique sequences identified for each treatment. Boxplots depict the median and quartile range...... 123 13. Bacterial phylum for each treatment at the beginning of feeding trail and at the end of feeding...... 124 14. Comparisons of the relative abundances of the bacterial phyla at day 0 and 140 for each treatment. Bacteroidetes increased over time with the overall reduction in relative abundances of all other bacteria. Treatment changes were not observed following application of either control or monpro diets. Significance was determined using the Wilcoxon signed-rank test...... 125
x Texas Tech University, Andrea English, December 2019
15. Alpha diversity (Richness (number of bacterial taxa) and Shannon-diversity) of treatments on days 0 and 140. There were no significant differences on day 0 or 140. Significance was determined using the Wilcoxon signed-rank test...... 126 16. Alpha diversity comparisons between treatments for day 0 and 140...... 127 17. Principle coordinate analysis of weighted UniFrac (wUniFrac) distances showing the beta diversity comparing treatments and days 0 and 140...... 128 18. Bacterial taxa differentially abundant by treatments. Fold- change (x-axis, log2) of differentially abundant taxa for each treatment...... 129 19. Individual abundance plots for all taxa from Figure 18 comparing day 0 and 140 for each treatment. Y-axis represents rlog normalized abundance and each plot represents one bacterial family with genera encoded using color...... 130 20. (A) Bacterial taxa differentially abundant between control and monpro on day 140. Taxa to the left represent control treatments and to the right monpro. (B) Individual plots of each of the statistically significant bacterial taxa from figure (A). Y-axis is rlog normalized abundance with each plot representing one bacterial family with genera encoded using color...... 131
xi Texas Tech University, Andrea English, December 2019
CHAPTER 1
1. REVIEW OF LITERATURE
Introduction
The use of antibiotics has revolutionized both human and veterinary medicine and has significantly helped fight disease and infection, although shortly after the beginning use of antibiotics, bacteria began to adapt and develop resistance (Tenover,
2006). Antimicrobial resistance (AMR) is a global concern and has a significant public health threat as infections with AMR bacteria increase mortality, morbidity and has a substantial economic impact. It is estimated that by 2050, 10 million deaths will occur per year with an economic burden of 100 trillion United States (U. S.) dollars globally
(Tang et al., 2017). Currently in the U. S., 2 million people are infected with antimicrobial resistant bacteria resulting in 23,000 deaths annually (Centers for
Disease Control and Prevention (CDC), 2018a). Antimicrobial resistance occurs when bacteria are able to survive and multiply when they are in the presence of antimicrobial agents that would otherwise inhibit or kill the bacteria (Unknown, 2011;
CDC, 2018b). Antimicrobial resistance is not novel and resistance genes to ß-lactams, tetracycline, and glycopeptide antibiotics have been identified on 30,000-year-old
Beringian permafrost sediments (D’Costa et al., 2011).
Overlapping use of antimicrobials in animal production, aquaculture, and agriculture is suspected to be a contributor to the increase in bacteria resisting antimicrobials commonly used in human medicine (Tang et al., 2017). This may occur if AMR bacteria from food producing animals survive through the food production chain and ultimately infecting consumers. Human contact with animals that 1 Texas Tech University, Andrea English, December 2019 harbor these bacteria, or shared environmental sources such as water are other routes of acquiring these bacteria (Tang et al., 2017).
Another contributing factor for the spread of AMR is improper prescribing by healthcare providers. The CDC estimates that 30% of antibiotics prescribed each year are for infections that do not require antibiotics (CDC, 2019a). In 2016, 270.2 million prescriptions were written for antibiotics in the U.S. even though prescribing has decreased by 5% from 2011 (CDC, 2019a). There are significant benefits possible by continuing to reduce the improper and unnecessary prescribing of antibiotics.
Furthermore, the World Health Organization (WHO) has ranked antimicrobials based on their importance in human medicine and they are categorized as follows; critically important [e. g. aminoglycosides, ansamycins, carbapenems, third and fourth generation cephalosporins, glycopeptides, glycylcyclines, lipopeptides, macrolides, penicillins, quinolones], highly important [e. g. tetracyclines, streptogramins, sulfonamides, lincosamides, amphenicols], important [e. g. aminocyclitols, cyclic polypeptides, nitrofurantoins, nitroimidazoles] (Collignon et al.,
2016). Two criteria (C1 and C2) are used to set the above classifications for the antimicrobial classes used in human medicine. For C1 the antimicrobial class is the only or one of few options for treatment of serious bacterial infections in people, and
C2 the antimicrobial class is used to treat infections of bacteria that may be transmitted to humans from non-human sources or bacteria that could acquire resistance genes from non-human sources (WHO, 2018). To control the spread of
AMR bacteria, antimicrobials must be used judiciously not only in human medicine but veterinary medicine as well. Utilizing the WHO list of critically important
2 Texas Tech University, Andrea English, December 2019 antimicrobials is a useful tool to avoid overuse of certain drugs with cross over in both human and veterinary medicine (Collignon et al., 2016). The WHO list can help preserve the antimicrobials that are still effective at treating bacterial infections.
Antimicrobial Drugs Mode of Action
Antibiotics are classified by their cellular components or the system they affect, and their mode of action, if they are bactericidal (induce cell death) or bacteriostatic (inhibit cell growth). Most commonly used antibiotics are bactericidal, or they stop cell growth by affecting the cell wall examples include ß-lactams and glycopeptides, some target protein synthesis (e.g., macrolides, chloramphenicol, tetracyclines, linezolid, and aminoglycosides). Also, antibiotics such as fluoroquinolones and rifampin interfere with nucleic acid metabolism. Some disrupt the metabolic pathways (e.g., sulphonamides and folic acid) or disrupt the bacteria’s membrane structure (e.g., polymyxins, daptomycin) (Kohanski et al., 2010; Tenover,
2006; Sengupta et al., 2013).
Cell Wall synthesis
The bacterial cell is surrounded by a peptidoglycan layer that provides protection for the cell allowing it to withstand harsh environmental conditions and can alter osmotic pressures. The cross-linking corresponds with the overall structural integrity of the cell (Kohanski et al., 2010). ß-lactams including penicillin, ampicillin and amoxicillin along with glycopeptides (e.g., vancomycin) are antibiotics that are designed to target and interfere with cell wall biosynthesis. Specifically, ß-lactams block the cross-linking of peptidoglycan by inhibiting the peptide bond formation
3 Texas Tech University, Andrea English, December 2019 reaction that is catalyzed by penicillin-binding proteins (PBPs). Glycopeptides such as vancomycin inhibit peptidoglycan synthesis by binding to the peptidoglycan unit D- alanyl-D-alanine dipeptide and block transglycosylase and PBP activity resulting in the failure to make peptidoglycan crosslinks which causes a weak cell wall layer
(Walsh, 2000; Kohanski et al., 2010).
Protein synthesis
Several antimicrobial classes utilize protein synthesis as the mechanism of action against bacteria some of those drugs include aminoglycosides, tetracyclines, chloramphenicol, macrolides and lincosamides, just to name a few. Before discussing the mode of action of antimicrobials further understanding how protein synthesis occurs is warranted. Protein synthesis takes place in three phases; initiation, elongation and termination (Kohanski et al., 2010). This process involves the ribosomes consisting of large ribonucleoproteins that enact the translation process and are made up of a 50S and 30S subunits. Initiation occurs when the 30S ribosomal subunit binds to the start codon of the messenger ribonucleic acid (mRNA) and transfers ribonucleic acid (tRNA) then binds to the 30S mRNA complex at the peptidyl site (P site), the 50S subunit binds to form the 70S ribosomal complex. Furthermore, a tRNA binds to the aminoacyl site (A site) then a transpeptidation reaction links the amino acids resulting in a growing peptide chain. As the chain grows it moves to the P site allowing the next tRNA to enter the A site. This continues until a translational stop codon enters the A site and is terminated and the peptide is released (McDermott et al., 2003).
4 Texas Tech University, Andrea English, December 2019
Macrolides, lincosamides, amphenicols and oxazolidinones target the 50S large ribosome subunit. These antimicrobials either block initiation of translation or translocation of peptidyl tRNAs which then inhibits the process of peptidyltransferase reaction that elongates the peptide chain (Kohanski et al., 2010). Tetracyclines and aminocyclitols are inhibitors of the 30S ribosomal subunit; tetracyclines specifically block the binding of aminoacyl tRNAs to the ribosome. Aminocyclitols which are made up of spectinomycin and aminoglycosides bind to the 16S rRNA element of the
30S ribosome subunit (Kohanski et al., 2010).
Nucleic Acid metabolism
Antimicrobials target DNA and RNA synthesis by two main ways either by interfering with nucleotide or nucleic acid biosynthesis in the cell (McDermott et al.,
2003). Antimicrobials that target nucleotide synthesis are sulfonamides, while quinolones and rifamycins affect nucleic acid synthesis. Sulfonamides structurally are similar to para aminobenzoic acid (PABA) which is a precursor and a major part of the folic acid structure. Sulfonamides function by competing for the enzyme dihydropteroate synthase active site thus blocking the formation of nucleotide precursors (Bhattacharjee, 2016).
Fluoroquinolones are synthetic antimicrobials that target two related enzymes,
DNA topoisomerase Ⅱ and DNA topoisomerase Ⅳ, and ultimately trap the enzymes at the DNA cleavage stage and prevent the strand from rejoining. Quinolones are effective at treating gram-positive and gram-negative bacteria infections; they target
5 Texas Tech University, Andrea English, December 2019 the topoisomerase Ⅳ in gram-positive bacteria where in gram-negative bacteria the target is topoisomerase Ⅱ (Kohanski et al., 2010).
Membrane structure
Daptomycin for example is a lipopeptide that is utilized to target hard to treat gram-positive bacteria such as vancomycin resistant Enterococci and methicillin resistant Staphylococcus aureus (Walsh and Wencewicz, 2016). It was approved by the U.S. Food and Drug Administration (FDA) in 2003 for the treatment of skin and soft tissue infections caused by the previously mentioned pathogens. It has no effect against gram-negative organisms because its structural make up cannot penetrate the outer membrane barrier (Walsh and Wencewicz, 2016). It induces cell death by disrupting the cell membrane integrity, daptomycin inserts the lipophilic tail into the bacterial cell membrane which then causes membrane depolarization and potassium ion efflux, resulting in DNA/RNA arrest and protein synthesis causing bacterial cell death (Steenbergen et al., 2005).
There are several antibiotics on the market being used to treat numerous bacterial infections. However, Walsh in 2000 stated that “development of resistance is not a matter of if but when” as bacteria have developed multiple ways of resisting almost all antibiotics in use.
Antimicrobial Resistant Bacteria
Bacteria can have intrinsic resistance to antibiotics and they can acquire resistance by chromosomal mutations or horizontal gene transfer of new genetic material. Intrinsic resistance meaning the organism has the inherent properties or
6 Texas Tech University, Andrea English, December 2019 characteristics to limit the action of antibiotics (Blair et al., 2015; Fernandez and
Hancock, 2012). An example of intrinsic resistance would be the use of daptomycin to treat gram-positive bacteria which is not an effective treatment for gram-negative organisms. This is due to the differences in the cytoplasmic membrane thus gram- negative bacteria have a lower proportion of anionic phospholipids in the cytoplasmic membrane which limits the Ca2+ mediated insertion of daptomycin (Blair et al., 2015).
A common method of acquired resistance includes horizontal gene transfer through conjugation, transduction and transformation. Conjugation is when physical contact through a conjugation pilus occurs between a donor and a recipient cell, genetic material then is transferred. Transduction simply put is the infection of bacteria by phages or bacterial viruses. The phage DNA can insert and then remove from the bacterial DNA. Finally, transformation which is the uptake of DNA from the environment (Soucy et al., 2015; Walsh and Wencewicz, 2016). Resistance also differs for gram-negative organism as their additional outer membrane is ultimately an extra layer of protection, restricts the permeability and diffusion through porins making them commonly more resistant to antibiotics compared to single cell gram- positive organisms (Walsh and Wencewicz, 2016).
The way bacteria acquire resistance to certain antimicrobials typically fall within the following methods: first by reducing the intracellular concentrations of the antibiotic by poor penetration into the bacteria or by antibiotic efflux. Secondly, by modifying the antibiotic target by genetic mutation or post translational modification of the target. Lastly, the inactivation of the antibiotic through hydrolysis or modification (Blair et al., 2015).
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An example of how bacteria reduce cell permeability is through modifying porins. Gram-negative bacteria and their outer membrane are intrinsically less permeable to antibiotics compared to gram-positive bacteria. Enterobacteriaceae for instance specifically E. coli have outer membrane porins OmpF and OmpC. OMP’s are iron-regulated outer membrane proteins, that can be down regulated which reduces the entry of antibiotics into the cell (Fernandez and Hancock, 2012; Blair et al., 2015).
Bacterial efflux pumps are another mechanism that bacteria use to push antibiotics out of the cell and are a main contributor to intrinsic resistance in gram- negative bacteria (Blair et al., 2015). There are efflux pumps that have a specific task in the case of tetracycline pump, however there are also pumps which can transport multiple substrates out of the cell known as multidrug resistance (MDR) efflux pumps.
Bacteria commonly have the genes that code for MDR efflux on their chromosome however genes have also moved to plasmids which can be transferred between bacteria (Fernandez and Hancock, 2012; Blair et al., 2015).
Antibiotics can also resist antibiotics by modifying the antibiotics target.
Antibiotics have specific targets that they bind to which disrupts the activity of that target, thus if bacteria can prevent the binding of those specific targets while still maintaining cell function, but can effectively resist the antibiotic (Blair et al., 2015).
For example, fluoroquinolones which target the topoisomerase genes thus resulting in halting DNA replication. Resistance to fluoroquinolones occurs typically by point mutation resulting in an amino acid substitution at the topoisomerase subunits GyrA,
GyrB, ParC, or ParE. Most common mutations occur in the N-terminus of the GyrA and GyrB proteins located in a region called the quinolone resistance-determining
8 Texas Tech University, Andrea English, December 2019 region (QRDR) resulting in reduced susceptibility to quinolones (Hopkins et al.,
2005).
A common method of resisting antimicrobials is through modification or destruction of the antibiotic has been around since the first use of antibiotics. This can occur by inactivation of the antibiotic through hydrolysis, or by the transfer of a chemical group. There have been thousands of enzymes identified that can modify different classes of antibiotics such as ß-lactams, aminoglycosides, phenicols and macrolides (Blair et al., 2015). For example, ß-lactamases are quite possibly have one of the most researched enzymes for their role in inactivation of ß-lactam antibiotics
(Bush, 2018). In Salmonella specifically the most commonly identified ß-lactamase genes include blaTEM, blaSHV, blaCTX-M, blaOXA, blaPER, blaPSE and blaCMY (Zhao et al.,
2009). With the modification of antibiotics to improve their functionality against ß- lactamases the hydrolytic enzymes have also improved their functionality resulting in extended-spectrum ß-lactamases (ESBLs) which can resist penicillins (first-, second-, and third-generation cephalosporins) and aztreonam by hydrolysis (Blair et al., 2015;
Zhao et al., 2009). Some of the most frequently isolated ESBLs enzymes around the world are CTX-M14 and CTX-M15 (Blair et al., 2015).
Bacteria can also inactivate antibiotics by the transfer of a chemical group.
Bacterial enzymes can cause antibiotic resistance by the addition of a chemical group binding to the antibiotic which prevents the antibiotic binding to its original target site.
An example would be aminoglycoside antibiotics as they are larger molecules with exposed hydroxyl and amide groups. There are three main classes of aminoglycoside-
9 Texas Tech University, Andrea English, December 2019 modifying enzymes (acetyltransferases, phosphotransferases and nucleotidyltransferases (Blair et al., 2015).
Enterococcus spp.
The genus Enterococcus are lactic acid bacteria that are gram positive, facultative anaerobes, catalase-negative, non-spore-forming and can occur either in single-cocci form or in chains (Fisher and Phillips, 2009). Enterococcus is also capable of growing in very diverse environments and are found ubiquitously meaning it is nearly everywhere that humans and animals interact. It can grow at temperatures ranging from 10-45°C, with high salt concentrations and in a broad range of pH
(Facklam, Carvalho and Teixeira, 2002). Thus, making it even more difficult to control the spread of the organism but on the other hand can also make it favorable to include in different food products. With enterococci’s capabilities to withstand variable pH values, high salt concentration and extreme temperatures it makes them favorable for certain food matrixes (Hanchi et al., 2018). With those characteristics in combination with some strains able to carry the genes and produce bacteriocin
Enterococci can out compete other pathogenic or spoilage organisms in those food products. Currently some Enterococcus strains are used as probiotics, or as starter cultures in fermented foods, like meat, dairy and vegetables (Hanchi et al., 2018;
Klein, 2003). However, this is a commonly disputed practice due to the high rate at which enterococci can transfer AMR genes to other organisms, not just other enterococci.
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The Food and Drug Administration (FDA) began the National Antimicrobial
Resistant Monitoring System (NARMS) in 1996 to test Salmonella in human clinical settings. NARMS has grown today to include data from humans in all 50 states along with samples during slaughter, cecal at slaughter and retail meats to monitor the AMR patterns of Salmonella, Campylobacter, generic E. coli and Enterococcus.
Enterococcus AMR is monitored in NAMRS to understand the resistance patterns of gram-positive bacteria. In the NARMS 2015 integrated report, from retail meats samples collected > 98% were E. faecalis and >84% E. faecium and were resistant to at least one antimicrobial class. In cecal isolates >78% were E. faecalis and >99% E. faecium were also resistant to at least one antimicrobial class. E. faecalis resistance to penicillin was ≤1% in 2015, while high-level gentamicin resistance was found in 24% of retail chicken and 34% of retail turkey samples and only 1% in retail swine and 0% in cattle. Less than 2% of E. faecalis isolates from both retail and animal sources were resistant to vancomycin, daptomycin, and linezolid. While no vancomycin resistance was detected in any retail meats in 2015.
Enterococci are commonly a commensal organism found in the gastrointestinal tract of both humans and animals. E. faecalis and E. faecium are the most prevalent nosocomial pathogens worldwide (ECDC, 2011). However, Enterococcus spp. have been a growing cause of concern as the leading cause of nosocomial infections, second only to Staphylococcus as a cause of gram-positive nosocomial infections (Hidron et al., 2008). In the last several years Vancomycin-resistant Enterococcus (VRE), in the health care system, has been on the rise leaving few treatment options for patients that acquire the infection (CDC, 2013). Vancomycin-resistant Enterococcus are one of the
11 Texas Tech University, Andrea English, December 2019 leading causes of hospital acquired infections. Furthermore, the CDC estimates that
Enterococcus causes 66,000 infections each year in the U.S., while VRE accounts for
20,000 of those infections, resulting in 1,300 deaths (CDC, 2013). Enterococcus spp. are of particular concern when discussing antibiotic resistance. Enterococcus spp. have the capabilities to acquire and transfer resistant determinants to other enterococci and even other gram-positive bacteria and are commonly referred to as “gene traffickers” (Wener et al., 2013). Enterococcus spp. are intrinsically resistant, which is based on the chromosomal genes, to multiple antibiotics and in recent years have been able to acquire different mechanisms of resistance, which is normally plasmid encoded or on transposons, to multiple different types of antibiotics that make it even more difficult to treat (Werner et al., 2013; Kak and Chow, 2002, Huycke et al., 1998).
ß-lactams are still commonly used to target and inhibit the cell wall biosynthesis specifically ampicillin and penicillin are the most commonly used antibiotic against Enterococci to inhibit the synthesis of the peptidoglycan (Miller et al., 2015; Zapun et al., 2007). However, Enterococci have intrinsic resistance to ß- lactams because of the low affinity to penicillin-binding proteins (PBPs).
Enterococcus commonly have 5 PBPs and in some cases up to nine PBPs (Kak and
Chow, 2002). Specifically, for E. faecium has PBP5 and PBP4 in E. faecalis binds weakly to ß-lactams antibiotics resulting in a higher minimum inhibitor concentration
(MIC) (8-16µg/ml and 2-8 µg/ml respectively) for penicillin compared to streptococci or other gram-positive organisms that do not have the low affinity for the species- specific chromosomal PBP genes (Miller et al., 2015: Hollenbeck and Rice, 2012).
Furthermore, cephalosporins are less effective at treating Enterococcus infections
12 Texas Tech University, Andrea English, December 2019 because the increased intrinsic resistance, and the decreased binding affinity to the enterococci PBP’s, mainly PBP5 (Kak and Chow, 2002; Miller et al., 2015). In a study conducted in Greece, 55 E. faecalis and 21 E. faecium isolates were collected from a hospital ward and from those isolates 7% (E. faecalis) and 76% (E. faecium) were resistant to ampicillin (Papaparaskevas et al., 2000). Ampicillin resistant enterococci are a public health concern and are a serious concern worldwide, as it makes treating enterococci infections even more difficult to treat.
Enterococci also have an intrinsic resistance to aminoglycoside by reducing the ability of the drugs to cross the cell membrane, aminoglycosides act by interrupting protein synthesis and binding to the 16S rRNA of the 30S ribosomal subunit (Kak and
Chow, 2002; Ramirez and Tolmasky, 2010). Aminoglycosides are not commonly used independently to treat an Enterococcus infection. However; they can be more effective when used in combination with a penicillin or glycopeptide such as vancomycin (Kak and Chow, 2002). High levels of Enterococcus resistance to aminoglycosides is common through acquisition of genes specifically for aminoglycoside-modifying enzymes (AME), such as phosphotransferases (APHs), acetyltransferases (AACs) and nucleotidyltransferases (ANTs) (Kak and Chow, 2002; Campo et al., 2000). In a study conducted in Spain by del Campo (2000) showed that the most frequent AMEs found in clinical isolates of Enterococcus with high levels of resistance to both gentamicin and streptomycin were AAC(6’)-APH(2”)+APH(3’). The second most common
AMEs present included AAC(6’)-APH(2”)+APH(3’)+ANT(6) with 12 out of 68 clinical isolates present for the AMEs (del Campo et al., 2000). In a study by Jackson et al. (2005) enterococci were collected from swine fecal samples around the U.S., 444
13 Texas Tech University, Andrea English, December 2019 isolates were collected and 187 of those were resistant to high levels of gentamicin, kanamycin (MIC ≥500 µg/ml), and streptomycin (MIC ≥1000 µg/ml). Also, 10 AME genes were examined with 8 genes identified [ant(6)-Ia, ant(9)-Ia, ant(4’)-Ia, aph(3’)-
IIIa, aac(6’)-Ie-aph(2”)-Ia, aph(2”)-Id, and aac(6’)Ii] (Jackson et al., 2005).
Suggesting from the above two studies that enterococci isolates from human and animals can carry a diverse range of AME genes. Furthermore, in Greece a study conducted by Papaparaskevas et al. (2000) found high level gentamicin resistance in
22% of E. faecalis and 0% of E. faecium isolates. The AME gene aac(6’)+aph(2”) was found in 10 out of the 12 resistant E. faecalis isolates and ant(6)-I was detected in
22 streptomycin resistant E. faecalis isolates and all of the E. faecium samples
(Papaparaskevas et al., 2000). Donabedian et al. (2003) investigated the spread of gentamicin resistant genes in enterococci from humans, farm animals and in food from around the U.S. to understand the possibility of spreading resistant genes from animals to humans. From the 360 enterococci isolates analyzed, 258 had HLGR (MIC ≥2,000
µg/ml) and 102 with an MIC of 128-1,024 µg/ml, the most common AME genes present was aac(6’)-Ie-aph (2”)-Ia in 72% of isolates, and 18% had aph(2”)-Ic with
9% containing the aph(2”)-Id gene (Donabedian et al., 2003). Isolates of E. faecalis containing the aac(6’)-Ie-aph(2”)-Ia gene had indistinguishable PFGE patterns and were found in human stool specimens, along with chicken and pork from different grocery stores in the same state. These data from Donabedian et al. (2003) suggest the possibility of gentamicin resistant enterococci could be passing from animals to humans through the food supply. This is of concern for the treatment of clinically
14 Texas Tech University, Andrea English, December 2019 important enterococci infections and the further spread of antimicrobial resistant determinant to other pathogens along the food supply chain.
Glycopeptides are used to treat gram positive organisms by inhibiting cell wall formation by binding with high affinity to the D-alanine-D-alanine (D-Ala-D-Ala) C- terminal pentapeptide precursors this action reduces the cell wall integrity resulting in cell death (Kak and Chow, 2002). Gram-negative organisms are insensitive to glycopeptides because the outer membrane blocks the glycopeptides from reaching the cell wall (Kak and Chow, 2002). The first reported case of vancomycin resistant E. faecium was reported in France in 1986 in a patient with leukemia and another patient in England with renal failure (Uttley et al., 1989; Kak and Chow, 2002), with a continued increase in vancomycin resistant infections in hospitals and limited options in treatment worldwide (Cetinkaya, Falk, and Mayhall, 2000). There are 9 different types of glycopeptide resistance including: vanA, vanB, vanC, vanD, vanE, vanG, vanL, vanM, and vanN with vanA, B and C being the most common variants. Acquired vancomycin resistance operons include vanA, B, D, E, G, L, N and intrinsic vanC in E. gallinarum and E. casseliflavus (Hegstad et al., 2010). VanA strains have high levels of resistance to both vancomycin and teicoplanin and are acquired through Tn1546 transposon or a related family member Tn3 transposons. VanB glycopeptide resistance is similar to vanA, vanB like vanA terminates pentadepsipeptide production in D-Ala-
D-Lac. VanB resistance is often on the chromosome and spread by conjugative transposons (Kak and Chow, 2002; Cetinkaya, Falk, and Mayhall, 2000). VanD has resistance to vancomycin and teicoplanin and is also located on the chromosome with similar function as VanA and B (Cetinkaya, Falk, Mayhall, 2000) but differs from by
15 Texas Tech University, Andrea English, December 2019 the presence of a VanY carboxypeptidase that is sensitive to penicillin G (Kak and
Chow, 2002). Glycopeptides such as vancomycin are commonly treatments of last resort for Enterococcus and other gram-positive organism infections thus the transfer of vancomycin resistant genes to other more virulent pathogens is a significant public health concern.
Macrolide and lincosamide have similar modes of action, and macrolides are made up of ≥2 amino or neutral sugars that are attached to a lactone ring however lincosamides differ as they do not have the lactone ring (Leclercq, 2002). Commonly acquired resistance for enterococci to macrolides and lincosamide antibiotics is through target site modification by methylation of an adenine residue in the 23S ribosomal RNA of the 50S ribosomal subunit. Typically achieved by the erythromycin ribosome methylase genes more specifically erm(B) gene and sometimes by erm(A) gene and the phenotype for those bacteria then is macrolide-lincosamide- streptogramin B or MLSB (Leclercq, 2002; Kak and Chow, 2002). Furthermore, bacteria can resist macrolides and lincosamides by efflux of the antibiotic and drug inactivation. Genes mefA, mefE, msrA and mreA are commonly responsible for the efflux of macrolides in gram-positive bacteria. A study by Schmitz et al in 2000, investigated macrolide-resistance genes in Staphylococcus aureus and Enterococcus faecium isolates from 24 university hospitals in Europe. For the S. aureus isolates ermA was the most prevalent at 67%, then ermC (23%), msrA/msrB (6%) and at 0.6% ermB and ereB genes, while ereA was not detected. In erythromycin-resistant E. faecium isolates ermB was found in 93% of isolates, while ermA only was detected in
4% of samples and ermA and ermB were found in 3% of isolates (Schmitz et al.,
16 Texas Tech University, Andrea English, December 2019
2000). Quinupristin-dalfopristin is a streptogramin and is an important antibiotic used typically for the treatment of severe infections caused specifically by gram-positive organisms, such as vancomycin-resistant Enterococcus faecium (Manzella, 2001).
Quinupristin-dalfopristin is composed of type B streptogramin and type A streptogramin at a 30:70 ratio (Manzella, 2001), with a specific target of the bacterial
50S ribosome which then inhibits protein synthesis (Kak and Chow, 2002).
Tetracyclines are used in both gram-positive and -negative bacterial infections.
Resistance in Enterococcus spp. is present in approximately 60-65% of clinical isolates even though tetracycline isn’t commonly used for treatment of Enterococcus infections (Roberts, 2005). Tetracyclines mode of action consists of inhibiting protein synthesis by binding to the 30S ribosomal subunit to the ribosome (Kak and Chow,
2002). Bacteria can resist tetracyclines by active efflux across the cell membrane and ribosomal protection. According to Roberts (2005) there are 38 tet and otr genes and
23 of those code for efflux proteins, 11 genes code for ribosomal protection proteins, 3 genes code for inactivating enzyme and 1 unknown mechanism.
A study conducted by Cauwerts et al. in 2007 looked at tetracycline resistance
Enterococcus isolates collected from 26 broilers in Belgium. From the broilers 93
Enterococcus isolates were collected with 73 isolates making up 8 different species.
Eighty-nine percent of Enterococci with the erm(B) gene also were positive for tet genes. The most frequently detected tet gene was tet(M), this gene is found on the bacterial chromosome and can be carried by conjugative transposons of the
Tn916/Tn1545 family. From this study there were high rates of genotypic and phenotypic resistance in the broilers positive for the erm(B) gene. Thus, with wide use
17 Texas Tech University, Andrea English, December 2019 of tetracyclines in poultry production there is a possibility of resistance to MLS antibiotics (Cauwerts et al., 2007). Furthermore, Huys et al. in 2003 investigated tetracycline resistance in Enterococcus isolates collected from different food sources in Belgium, largely European cheeses, other foods included; milk, meat, fish, vegetables, and fermented milk. From the 187 isolates collected 45 displayed phenotypic resistance to tetracycline (MIC range 16-256 µg/ml). Multidrug resistance of tetracycline, erythromycin and chloramphenicol was found in 8 of those isolates.
For isolates (n=15) with the MIC of 256 µg/ml both tet(L) and tet(M) were detected, and the isolates containing the tet(M) gene also carried a member of the
Tn916/Tn1545 conjugative transposon family, showing similar results to the previously mentioned study by Cauwers et al., (2007) (Huys et al., 2003).
A relatively new class of antimicrobial agent is oxazolidinone specifically developed to treat multidrug-resistant gram-positive bacteria. Specifically, in 2000 linezolid was the first oxazolidinone approved for clinical treatment (Kak and Chow,
2002; Wang et al., 2015). Linezolid acts by binding to the 23S rRNA and prevents aminoacyl-tRNA attachment to the A site of the ribosome thus inhibiting protein synthesis (Miller et al., 2014).
Enterococcus are extremely important nosocomial pathogen and as previously mentioned they are a leading cause of hospital acquired infections worldwide (Hidron et al., 2008). They also are quite adaptable to the environments they survive in and
Enterococcus can acquire and spread resistance determinants to not only other
Enterococcus spp. but other pathogenic organisms like Staphylococcus. This makes them even more difficult to treat and control (Arias et al., 2010). Thus, continued
18 Texas Tech University, Andrea English, December 2019 investigation into how Enterococcus spp. adapt, spread and acquire resistance determinants not only in clinical settings but in animal studies is warranted along with prudent use of current antimicrobials in human and veterinary medicine.
Salmonella and Escherichia coli
Nontyphoidal Salmonella is estimated to cause 1.2 million illnesses, with
23,000 resulting in hospitalizations, and 450 deaths each year in the U.S. (CDC,
2019). Specifically, S. typhi causes 21,700,000 illness worldwide, and in the U. S. specifically, 620 hospitalizations each year (CDC, 2013). Approximately, 1 million nontyphoidal Salmonella illnesses are from consumption of contaminated food such as eggs, meat, poultry, milk, fruits and vegetables which have been contaminated through feces. There have also been cases of Salmonella infection through contact with pets like hedgehogs, and backyard poultry (CDC, 2019b). Symptoms can include diarrhea, fever, and abdominal cramps, in more severe cases it can transfer to the blood and other parts of the body and become more life threatening resulting in death unless treated with antibiotics. The medical costs associated with Salmonella infections each year is substantial, estimated to be $365 million (American’s Health Rankings, 2016).
Furthermore, drug-resistant nontyphoidal Salmonella results in about 100,000 infections with 40 deaths each year in the U. S. drug resistant S. typhi causes an estimated 3,800 infections each year (CDC, 2013). Fluoroquinolones are a common antimicrobial utilized to treat severe salmonellosis (Tyson et al., 2017).
In 2011, Salmonella Heidelberg was in the top ten sources of human salmonellosis in the U.S. and is commonly found in poultry and ground turkey (Routh
19 Texas Tech University, Andrea English, December 2019 et al., 2015). An outbreak occurred of multidrug-resistant S. Heidelberg isolated from frozen and fresh ground turkey which caused a 36-million-pound recall of ground turkey, and resulted in 136 illness cases across 34 states, from February 27th to
October 17th, 2011. From the isolates collected during the investigation 19 of isolates were resistant to ampicillin, gentamicin, streptomycin and tetracycline (Routh et al.,
2015). According to the CDC from 2017 to 2019 raw turkey products were associated with multidrug resistant S. Reading, a total of 358 people were infected, with 133 hospitalizations, and 1 death, across 42 states. Whole genome sequencing (WGS) was performed on 314 isolates and resistant genes were detected suggesting decreased susceptibility or possible resistance to ampicillin, streptomycin, sulfamethoxazole, tetracycline, kanamycin, gentamicin, nalidixic acid, ciprofloxacin, trimethoprim- sulfamethoxazole and fosfomycin (CDC, 2019c). Also, in 2018 the CDC reported a multidrug-resistant S. Infantis outbreak associated with raw chicken products causing
129 illness, 25 hospitalizations and 1 death. The multidrug strain was isolated from raw chicken products, raw chicken pet food and live chickens (CDC, 2019d). Isolates from infected people (97) and food/environmental samples (139) were subject to WGS and resistance or reduced resistance was found in the following antibiotics; ampicillin, ceftriaxone, chloramphenicol, ciprofloxacin, fosfomycin, gentamicin, hygromycin, kanamycin, nalidixic acid, streptomycin, sulfamethoxazole, tetracycline, and trimethoprim-sulfamethoxazole (CDC, 2019d).
In the NARMS 2015 report, Salmonella was found in 6.1% of retail chicken samples the lowest recorded presence in 14 years of NARMS testing. Furthermore,
76% of the Salmonella isolates from humans were susceptible to all 14 antimicrobials
20 Texas Tech University, Andrea English, December 2019 tested while 57% of retail ground turkey had resistance to at least one antimicrobial tested. A common combination pattern of resistance for Salmonella includes the antibiotics ampicillin, chloramphenicol, streptomycin, sulfonamides and tetracycline commonly referred to as ACSSuT (NARMS, 2015). MDR Salmonella isolates from humans in 2015 was approximately 12%, while Salmonella Typhimurium designated
4,[5],12:i: had resistance to ACSSuT in 60% of human isolates in 2015 and 65% in swine cecal samples (NARMS, 2015). In 2014, the Food Safety Inspection Service
(FSIS) National Antimicrobial Resistance Monitoring System cecal sampling program found Salmonella in 1,077 of 5,001 of cecal samples collected from swine, cattle and poultry, and 66% of those positive samples were pan-susceptible. While 13% were
MDR, specific serotypes with increased MDR were S. 4,[5],12:i: and Typhimurium with 68.4% and 51.6% MDR respectively.
A study conducted in Mexico investigated the multidrug-resistant S. enterica from two different states and from multiple different sources (ground beef, human clinical samples, swine, poultry and bovine carcasses) (Aguilar-Montes de Oca et al.,
2017). The most common identified serovar was S. Typhimurium (17.7%), followed by S. Anatum (7%), S. Agona (5.8%), S. Infantis (5.8%), S. London (5.8%) and S.
Group B (5.3%). Samples were most commonly recovered from ground beef at 56.7%, then swine (16.5%), cattle (3.6%), and poultry at 1.5% (Aguilar-Montes de Oca et al.,
2017). Furthermore, 243 isolates were examined for AMR and the most common resistance was to tetracycline at 41.6%, then ampicillin (24.7%), nalidixic acid
(21.8%), gentamicin (9%), cefotaxime, ceftazidime, and cefoxitin all at 6.6%, while
16 isolates had multidrug-resistance with the most frequent resistant pattern was
21 Texas Tech University, Andrea English, December 2019 cefotaxime-ceftazidime-cefoxitin-ampicillin-nalidixic acid-gentamicin-tetracycline
(n=3) (Aguilar-Montes de Oca et al., 2017). However, the resistance rates of
“critically important antibiotics” such as cephalosporins was less than 10%.
Fluoroquinolones act by inhibiting the bacterial DNA gyrase and topoisomerase thus causing DNA damage resulting in cell death (Oliphant and Green,
2002). Fluoroquinolones are also used not only in human medicine but for veterinary medicine as well to treat a wide range of diseases and pathogens (Bearson and
Brunelle, 2015). Resistance to fluoroquinolones is commonly a chromosomal mutation although it can be passed by a conjugative plasmid encoded qnr gene (Mammeri et al.,
2005). Bearson and Brunelle (2015) investigated if fluoroquinolones specifically, ciprofloxacin, enrofloxacin and danofloxacin, could induce generalized transducing prophages in multidrug-resistant S. Typhimurium DT120 and DR104. The data show that both strains of S. Typhimurium DT120 and DR104, stimulated generalized transduction of the kanamycin resistance plasmid into BBS 243 (kanamycin-sensitive recipient), thus suggesting that fluoroquinolones can induce horizontal gene transfer from MDR S. Typhimurium to susceptible bacteria (Bearson and Brunelle, 2015).
Providing even further evidence of the importance of prudent usage of antimicrobials.
Furthermore, NARMS investigated Salmonella isolates from swine cecal samples and retail pork chops that expressed decreased resistance to ciprofloxacin. In
2013, 2.7% swine cecal samples were nonsusceptible and in 2014, it was 3.8% and for retail pork chops 3 of the 39 isolates were nonsusceptible to ciprofloxacin (Tyson et al., 2017). Further investigation into these isolates was conducted and analyzed for mutations in the quinolone-resistance determining regions (QRDR) of gyrA, gyrB,
22 Texas Tech University, Andrea English, December 2019 parC, and parE along with the plasmid-mediated quinolone resistance (PMQR) gene including qnr genes aac(6’)-lb-cr, qepA, and oqxAB. Qnr genes can increase the MICs of fluoroquinolones by 4-8 fold and can cause resistance to nalidixic acid (Mammeri et al., 2005). Thirty Salmonella isolates were sequenced and from those 27 had PMQR genes and 24 with qnrB19 genes on ColE-type plasmids. Also, 2 isolates had the qnrS2 gene (first reported presence in Salmonella in the U. S.) (Tyson et al., 2017). A study conducted by Saenz in 2003, investigated quinolone resistance in isolates collected from animals and food to study the mutations in gyrA, parC and gyrB in nalidixic acid resistant E. coli. Nalidixic acid susceptible E. coli strains (n=13) were used and 80 nalidixic acid resistant E. coli strains were further investigated for mutations. No amino acid changes were found in the susceptible nalidixic acid strains in gyrA or pacC. In 62 nalidixic acid resistant strains a single amino acid change occurred in the gyrA protein, while double changes occurred in 18 of the nalidixic acid resistance strains also in gyrA. For the parC protein 26 nalidixic acid resistant strains had single amino acid changes (Saenz et al., 2003). Furthermore, three amino acid changes 2 in gyrA and 1 in parC had moderate to high level ciprofloxacin resistance at
8-32 mg/L, and 4 amino acid changes 2 in both gyrA and parC had the highest reported MIC to ciprofloxacin at 64 mg/L (Saenz et al., 2003). Fluoroquinolones are important for human and animal medicine, which makes it even more important to continue to investigate the possible ways of transmitting these resistance genes, organism to organism, but also through the food supply.
Escherichia coli (E. coli) are commonly found in the gastrointestinal tract
(GIT) of humans and animals and typically begin colonization in the GIT a few hours
23 Texas Tech University, Andrea English, December 2019 after birth (Kaper et al., 2004; CDC, 2014). The majority of E. coli are commensal strains; however, some E. coli do cause serious illness such as diarrhea, urinary tract infections and sepsis/meningitis (Kaper et al., 2004). There are 6 main pathotypes that can result in diarrhea with include; shiga toxin-producing E. coli (STEC), enterotoxigenic (ETEC), enteropathogenic (EPEC), enteroaggregative (EAEC), enteroinvasive (EIEC), and diffusely adherent E. coli (DAEC) (CDC, 2014). The CDC estimates that STECs are responsible for approximately 265,000 infections each year in the U. S. and E. coli O157 is responsible for 36% of those illnesses. However, for these infections’ antimicrobials are not recommended it is still important to monitor the AMR of E. coli strains because of their ability to transfer resistance and virulent determinants to other pathogenic gram-negative organisms. Furthermore, uropathogenic E. coli (UPEC) the most common cause of urinary tract infections
(UTI) in the U. S. and a common line of treatment for infections is trimethoprim- sulfamethoxazole (SXT). Although, with increased use of SXT, increased rates of E. coli with resistance to SXT has occurred, in fact, SXT resistant E. coli isolates from
UTIs in the U. S. range from 18-25%, and ampicillin resistant E. coli isolates are approximately 40% in the U. S. (Sahm et al., 2001).
NARMS monitors E. coli as an indicator organism for AMR for other pathogenic bacteria such as Salmonella. Antibiotic resistant E. coli in 2015 according to NARMS was most common in turkey samples (85%) with isolates resistant to at least one antimicrobial, while retail ground beef had 20% resistance to at least one antimicrobial and 41% in beef cow ceca, and 24% in dairy cow ceca. Multidrug resistance (resistance to 3 or more drug classes) E. coli was detected in 57% of retail
24 Texas Tech University, Andrea English, December 2019 ground turkey samples, 31% from retail chicken, 12% in pork chops, and 7% in ground beef (NARMS, 2015).
A monitoring system was implemented in Germany in 2009 to track the AMR of Salmonella, Campylobacter, verotoxigenic E. coli and generic E. coli isolated from poultry production (laying hens, broilers, chicken meat, turkey meat), dairy cattle, veal calves, and pork (Kaesbohrer et al., 2011). E. coli specifically was isolated from 202 broiler samples, 312 laying hens, 194 broiler meats, 203 turkey meats, 93 dairy cattle,
361 veal calves, 51 veal, and 46 pork samples and the positive isolates were tested against 13 antimicrobials. For dairy cattle, veal calves, veal and pork, 83.9, 27.1, 37.3,
56.5% respectively were susceptible to all antimicrobials tested. In poultry production,
85-93% of isolates were resistance to at least antimicrobial and 73-84% had resistance to several antimicrobials (Kaesbohrer et al., 2011). While in veal calves, veal and pork
43-73% of isolates had resistance to at least one antimicrobial. Chicken and turkey meat showed high rates of resistance to fluoroquinolones, while it was less common in cattle and pig production. Cephalosporin resistance was detected in 5.9% of broilers and 6.2% of chicken meat samples. Thus, resistant fluoroquinolones and cephalosporin E. coli is critical to monitor as both antimicrobials are critically important antimicrobials for treatment in human medicine (Kaesbohrer et al., 2011).
Like previously mentioned Enterococcus, Salmonella and generic E. coli have the capabilities to carry and transfer antimicrobial resistant genes. Concerningly, the rate of resistance continues to increase making AMR a significant public health concern. It is imperative to continue to utilize a multidisciplinary approach to monitor the use of antibiotics along with the spread and dissemination of AMR organisms.
25 Texas Tech University, Andrea English, December 2019
Antibiotic Use and Resistance in Beef Production
Background
The U.S. is the largest producer of beef in the world at 12,086,000 metric tons followed by Brazil (9,500,000), European Union (7,875,000) and China (7,070,000) totaling 59% of the world’s beef supply (Cook, 2019). In the U.S. specifically 26.3 billion pounds of beef were produced in 2017 (North American Meat Institute
(NAMI), 2017). Beef production is complex and takes approximately 2-3 years to take beef from farm to fork (National Cattleman’s Beef Association (NCBA), 2019). Cattle are moved through different production systems from cow-calf operations to post- weaning and lastly finishing in a feedlot commonly on a high-energy grained based diet (Cameron and McAllister, 2016). During any of these phases’ illness, disease or production losses can occur, thus antimicrobials can be given either therapeutically or sub-therapeutically to cattle during any of these phases of operation, especially in feedlots where infections can spread rapidly (Cameron and McAllister, 2016).
Antimicrobials have made significant leaps and bounds for public and veterinary medicine and have been used in animal medicine for over 50 years.
Antimicrobials in animal production are typically used for three purposes: [1] prophylactic to prevent disease, [2] metaphylaxis typically means treating large cohorts or entire herds to provide treatment of infected animals or potentially susceptible animals and [3] sub-therapeutic or growth promoters to improve animal performance and increase feed efficiency (Laxminarayan et al., 2015; Cameron and
McAllister, 2016). Sub-therapeutic treatment is typically a low dose of antimicrobial
26 Texas Tech University, Andrea English, December 2019 in feed over an extended period of time and is a dosage below 200 grams per ton of feed (Mathews, 2001). One of the most common sub-therapeutically fed antimicrobials is tylosin a macrolide that is utilized widely to control liver abscesses in feedlot cattle (Amachawadi and Nagaraja, 2016). A study done by Wileman et al. in
2009 showed that feedlot cattle fed tylosin had a reduced risk of liver abscesses from
30% to only 8%. Sub-therapeutic antimicrobials have had great success in livestock production, they have been proven to increase daily weight gain by approximately 6% and improve feed efficiency by 5% thus lowering production costs (Mathews, 2001).
Furthermore, an antimicrobial of non-medical importance is monensin which is an ionophore, it is used widely in beef production to improve feed efficiency and also prevents/controls coccidiosis (Elanco, 2019). Monensin has been approved for use in the beef industry since the 70’s (McGuffey et al., 2001). The addition of Rumensin® in feedlot cattle diets have shown to increase feed efficiency by 4% resulting in a net return of $23.13 per head (Elanco, 2019). Ionophores are critical for beef production to maintain animal performance and health so the beef industry can continue to meet the demand for beef, because beef cattle take approximately 7 additional weeks if raised without antimicrobials to reach their market weight (Vikram, et al., 2017).
When antimicrobials are used appropriately, they are extremely effective at treating infections and disease caused from a wide range of bacteria, however when misused bacteria can quickly become resistant (Unknown, 2018). Supplementing food producing animals with low doses of antimicrobials in feed has been speculated to be a contributing factor to the growing crisis and spread of antimicrobial resistant bacteria and the potential transfer to humans. It can also place greater selection
27 Texas Tech University, Andrea English, December 2019 pressure for resistant strains to emerge and evolve (Van Boeckel et al., 2015; Marshall and Levy, 2011). Thus, due to changes in the veterinary feed directive (VFD) the sub- therapeutic treatment with medically important antibiotics (antimicrobial drugs that are important for therapeutic use in humans) as of 2017 are no longer permitted by the
FDA in the U.S. (FDA, 2012). The FDA has stated in the Guidance for Industry #209 that addresses medically important antimicrobial drugs used in food producing animals that the use of antimicrobials for “increased rate of weight gain” or “improved feed efficiency” are not an appropriate use for medically important antimicrobials
(FDA, 2012). However, antimicrobials can be used for the treatment, control and prevention of specific diseases as they ensure the health of the animals. The current list from the FDA of medically important antimicrobial drug classes includes aminoglycosides, lincosamides, macrolides, penicillins, streptogramins, sulfonamides and tetracyclines (FDA, 2013). The European Union began banning the use of antimicrobials as growth promoters starting in 1997, and in 1999 banned the use of tylosin, spiramycin, bacitracin, along with other antimicrobials, and finally fully banned growth promoters in 2006 (Maron et al., 2013). Controlling the use of antimicrobials and ensuring their judicial use is important to maintain the efficacy of antimicrobials for treatment of human and animal diseases and infections. From the consumer perspective there has been a more recent demand for meat products to be
“raised without antibiotics” as the claim suggests to the consumer that fewer bacteria with AMR are present. However, there is a need for further studies that show the claim
“raised without antibiotics” does actually reduce the presence of AMR bacteria in final
28 Texas Tech University, Andrea English, December 2019 product and can help reduce the emergence of AMR in both human and veterinary medicine.
Antimicrobial Resistance in Beef Production
Antimicrobial resistance of Enterococcus, E. coli, Salmonella and
Campylobacter has been extensively studied especially in food producing animals.
Studying these bacteria can provide insight into the impact of sub-therapeutic antimicrobials not only for potentially human foodborne pathogens but also the commensal bacteria of food animals (Marshall and Levy, 2011). Continued monitoring of the impact of antimicrobial use in animal production is important to fully understand the spread and dissemination of AMR organism to organism, through contaminated food, contact with infected animals and potential persistence in the environment. A study conducted by Agga et al. (2019) investigated the persistence and distribution of antimicrobial resistance genes (ARGs) (tetracyclines (tetO, tetW, tetQ), sulfonamides (sul1 and sul2) and erythromycin (ermB and ermF)) and bacteria after destocking in beef cattle pasture-feedlot environments. Where the cattle were held consisted of a covered barn with outdoor feeding, watering and grazing. Cattle were feed intensively for seven years in this location before stopping in 2010. Samples were collected in 12 locations in the feeding area and 14 where the cattle grazed during
2010 while the cattle were still in the pens, along with 2011 and 2012 after the cattle had been removed. Total bacteria, Enterococcus spp., class 1 integrons and ARGs all had similar trends in that concentrations were higher around the feeding area and along the fence compared to grazing areas and after two years the concentration
29 Texas Tech University, Andrea English, December 2019 significantly decreased in concentration (Agga et al., 2019). There were still higher concentrations of ARGs around the feeding area compared to the grazing area after the two-year period suggesting a persistence of ARGs in the soil (Agga et al., 2019).
Enterococcus
Enterococcus spp. is certainly not a concerning pathogen from a foodborne illness perspective; however, they are important to continue to monitor as they have the capability to transfer AMR to other species and pathogens and are a leading cause of nosocomial infections worldwide (ECDC, 2011; Hidron et al., 2008). They are also ubiquitous in the environment and are found in both the animal and human gastrointestinal tract so they can provide insight into how other gram-positive organisms could potentially be affected by the use of antimicrobials (Cameron and
McAllister, 2016).
A study conducted by Fluckey et al. (2008) explored the AMR patterns of enterococci collected from feedlot cattle and also determined if there were genetic similarities in isolates from the feedlot to isolates collected at harvest. At the feedlot
53.9% of fecal samples were positive for enterococci, while hide samples at the feedlot had 77.8% positives, and the presence continued to increase at harvest as hide samples were 96.1% positive, pre-evisceration were 58.3% positive and finally in the cooler only 5 of the 20 carcasses were positive for Enterococcus (Fluckey et al.,
2008). All 279 confirmed enterococci isolates were resistant to at least one antimicrobial and 179 were resistant to at least 6 antimicrobials tested. All isolates were resistant to chloramphenicol, followed by flavomycin (90.3%), lincomycin
(87.8%), tylosin (78.5%), erythromycin (76.3%), tetracycline (58.9%), synercid or
30 Texas Tech University, Andrea English, December 2019 quinupristin-dalfopristin (47.7%), bacitracin (17.9%), streptomycin (9.0%), ciprofloxacin (1.4%), linezolid (0.7%) and finally salinomycin (0.4%). The most common identified species was E. durans (169) followed by 103 E. faecium and 7 E. faecalis isolates. Utilizing pulsed-field gel electrophoresis they also discovered indistinguishable electrophoresis patterns in isolates from the feedlot and in the plant.
Showing that isolates can be transferred from the feedlot to the harvest processing plant through contamination however very few isolates were recovered from the cooler (Fluckey et al., 2010). However, a study by Aslam et al. (2010) looked at what happens to the carcasses once they enter fabrication thus, they recovered Enterococcus isolates from different conveyers in a beef harvest plant at the start of operation and 2 hours after operation then from ground beef. For susceptibility testing, resistance gene detection and genetic analysis there were 39 isolates collected from before operation,
84 isolates two hours after operation, and 76 isolates from ground beef. E. faecalis was the most commonly isolates species at 87% from all three sources, followed by E. faecium (10.6%). In E. faecium isolates collected after two hours of operation 42% has resistance to quinupristin-dalfopristin, and for E. faecalis regardless of location more than 90% of isolates were resistant to lincomycin. Tetracycline resistance was also higher at two hours post operation with 52% of isolates resistant (Aslam et al., 2010).
Also, from ground beef the ermB gene was identified in 50% of the E. faecium isolates and regardless of location more than 12% of E. faecalis isolates had the ermB gene
(Aslam et al., 2010). This study shows that even if carcasses have a low concentration of Enterococcus spp. the prevalence can increase once further processed and this can
31 Texas Tech University, Andrea English, December 2019 be concerning from their ability to transfer resistance genes to other organisms and survive harsh environments.
Work by Beukers et al. (2015) investigated the AMR of enterococci isolated from feedlot steers fed tylosin phosphate compared to cattle fed no antimicrobial.
There were no differences detected for cattle fed tylosin compared to control cattle on days 0, 14, 49, and 84 for erythromycin resistance and no differences for tylosin resistance on days 0, 14, and 49. On days 113, 141, 169, and 197 tylosin fed cattle had higher rates of erythromycin resistant enterococci while tylosin resistance was significantly higher in tylosin fed cattle on days 84, 113, 141, 169, and 197.
Interestingly after withdrawing tylosin from the diet 28 days prior to harvest there were no differences between treatment groups for erythromycin and tylosin resistant enterococci (Beukers et al., 2015). During the same study they utilized PCR to further confirmed and identify the enterococci isolates and 95.2% were confirmed as enterococci. The most prevalent species were E. hirae (n=431) which is not commonly identified in human infections, along with E. villorum (n=32), and E. faecium (n=21).
In 125 enterococci isolates resistance determinants were detected erm(B) was found in
106 of the isolates, while no isolates had tetB or tetC genes however 10 isolates had tetM and 9 had tetL (Beukers et al., 2015).
A study conducted in 2007 investigating the AMR of enterococci isolated from dairy cattle in the U.S. and 118 of the 122 operations had at least one dairy cow positive for enterococci (Jackson et al., 2010). The most prevalent species of enterococci which was similar to the previously mentioned study was E. hirae (49.2%) followed by E. faecalis (14.2%), E. faecium (13.4%), E. casseliflavus (10.4%), E.
32 Texas Tech University, Andrea English, December 2019 species (3.6%), E. durans (3.5%), E. mundtii (3.1%), E. gallinarum (2.0%), E. avium
(0.5%), and finally E. flavescens (0.2%) (Jackson et al., 2010). Isolates had the highest rates of resistance to lincomycin (587 of 636), then flavomycin (457 of 636) and tetracycline (156 of 636) (Jackson et al., 2010).
Furthermore, a study done by Jacob et al. (2008) looked at the effects of feeding wet distiller grains with and without monensin and tylosin on the presence and susceptibility of E. coli O157, Salmonella, commensal E. coli and Enterococcus spp. in the feces of feedlot cattle. For Enterococcus isolates 57.3% were resistant to macrolides, and 75% of the isolates from cattle fed monensin and tylosin had resistance to tylosin and erythromycin, while cattle only fed monensin had 66% of isolates resistant to the same drugs. Also, tetM was found in all fecal samples and ermB was detected in 94 of the 108 fecal samples. Tetracycline and erythromycin resistance is highly correlated with the presence of tetM and ermB genes respectively.
Salmonella and E. coli
Cattle are common reservoirs of Salmonella and E. coli which both can cause serious human infection. Both organisms can be transferred to humans through contaminated foods, water, or direct contact with the animal fecal matter (Pfizer, 2011;
APHIS, 2014; Xu, et al., 2014; CDC, 2015; CDC, 2019b). The prevalence of these organisms varies across feedlots and across seasons. A study conducted in 2011 looked at the prevalence of Salmonella in feedlots across the U.S. and 60.3% of the 68 feedlots had at least one positive pen (APHIS, 2014). Barkocy-Gallagher et al. (2003) reported a higher prevalence of Salmonella in feedlot cattle feces during the summer months, as well as a higher presence on hides during the summer and fall and lowest
33 Texas Tech University, Andrea English, December 2019 during the winter. The frequency of E. coli O157:H7 has been reported to range from
0.3% to 19% in feedlot cattle and at harvest 0.2% to 27.8% (Pfizer, 2011). E. coli
O157 during the summer reported by Ekong et al. (2015) had an estimated prevalence of 10.6% while during the winter months it was 9% in the U.S. These pathogens can be harmful enough when they enter the food supply however, they become significantly more dangerous when resistant to antimicrobials that could be used to potentially treat them. A speculated cause of the growing AMR of bacteria as previously mentioned is the use of sub-therapeutic antimicrobials in animal production. When considering this idea, it is important to review current research investigating AMR of pathogens from the production system and address gaps where further research is needed. Fluckey et al. (2006) studied the AMR of Salmonella and
E. coli from feedlot cattle feces, on hides (before shipping and after arrival at the plant) and carcasses. Salmonella was present on 33.9%, 37.3%, 84.2%, 8.3% of fecal samples, on hides before shipping, hides after arrival at the plant and at pre- evisceration, respectively. While no Salmonella was detected on final carcasses. E. coli was present in 98.3% of fecal samples and ranged from 100% to 50% on the hides at the feedlot, and 90% to 100% of hides at the plant and finally E. coli presence at pre-evisceration was 40.0% ± 26%. From the Salmonella isolates 97% were resistant to at least one antimicrobial with the most common resistance to sulfamethoxazole
(96%) then streptomycin (17.6%). E. coli isolates were most commonly resistant also to sulfamethoxazole (79%), then to trimethoprim-sulfamethoxazole (29.9%), tetracycline (13.4%), cephalothin (5.6%), streptomycin (5.6%), kanamycin (4.8%) and ampicillin (1.12%) (Fluckey et al., 2007). In another study conducted by Benedict et
34 Texas Tech University, Andrea English, December 2019 al. (2015) looked at the susceptibility of two sets of E. coli isolates (first sample set
2,379 isolates and second sample set 2,725 isolates) collected from feedlot cattle feces to better understand the AMR of E. coli isolated from cattle supplemented with antimicrobials. During the first sample set testing 11.2%, 4.0% and 3.7% of isolates were resistant to 1, 2 and 3 antimicrobials tested while the second sample set had
41.8%, 18.8% and 10.2% with resistance to 1, 2 and 3 antimicrobials. Showing an increase in resistance as the cattle were on feed longer at the feedlot. The most common drugs isolates were resistant to included tetracycline, sulfisoxazole and streptomycin, across all sampling periods (Benedict et al., 2015). However, the resistance to critically important antimicrobials for human medicine remained low during the study. While treating cattle with tetracycline can result in increased detection of tetracycline resistant E. coli but treatment with macrolides like tulathromycin did not increase the presence of resistant E. coli (Benedict et al., 2015).
Furthermore, a study by Schmidt et al. (2015) determined the AMR of E. coli and
Salmonella in beef cattle production and found generic E. coli, third-generation cephalosporin-resistant (3GC) E. coli and trimethoprim-sulfamethoxazole-resistant
(COT) E. coli in 100%, 82.6%, and 98.4% of feedlot cattle feces. While Salmonella, third-generation cephalosporin-resistant (3GC) Salmonella, and nalidixic acid-resistant
(NAL) Salmonella was detected in 5.4%, 0.5%, and 0% of the cattle fecal samples at the feedlot. The presence on hides at processing for generic E. coli, 3GC, COT E. coli was 100%. Generic Salmonella, 3GC Salmonella, and NAL Salmonella was 99.5%,
7.6% and 0.5% positive on hides at processing, respectively. Prevalence at pre- evisceration for generic E. coli was 90.2%, 3GC E. coli at 2.7% and COT E. coli with
35 Texas Tech University, Andrea English, December 2019
32.6% positive. Generic Salmonella, 3GC Salmonella, and NAL Salmonella was present at 2.2%, 0% and 0% respectively. Generic E. coli was the most prevalent on final carcasses at 49.5% followed by 3GC E. coli and COT E. coli both at 0.5%, no
Salmonella was present on final carcasses. These data strongly support the effectiveness of food safety interventions and controlling the spread of these pathogens into the food supply. Afema et al. (2014) analyzed 5,124 human Salmonella isolates and 2,255 cattle isolates. The most prevalent serovars in both human and cattle were Typhimurium (ST), Newport (SN) and Montevideo (SM). Ninety-four percent of the ST isolates from cattle were resistant to at least one antimicrobial compared to
54.4% in the human isolates. For SN 70% of the cattle isolates were resistant to one antimicrobial compared to the humans at only 44.9%. Finally, SM isolates from cattle only 2.2% were resistant to one antimicrobial while 75.2% of human isolates were resistant to at least one antimicrobial (Afema et al., 2014). Further analysis looked into the AMR diversity and found an increase in AMR diversity from human isolates compared to cattle (Afema et al., 2014). Furthermore, a study investigated the effect of feeding dairy cow ceftiofur, which is a third-generation cephalosporin and is a critically important class of antibiotic defined by the WHO, and the antimicrobial resistant genes from the fecal microbiome (Chambers et al., 2015). Chambers et al.
(2015) found that the treatment with ceftiofur increased the ß-lactam and multidrug resistant genes found in the fecal samples compared to control cows.
There is extensive research showing that bacteria, both pathogenic and commensal, isolated from food producing animals specifically beef harbor AMR.
36 Texas Tech University, Andrea English, December 2019
However, as previously mentioned control measures in processing plants and proper hygiene/interventions can help control the spread and dissemination of AMR bacteria.
Microbial Diversity in Feedlot Cattle Feces
The rumen microbiome is diverse and complex but increased and more widely available molecular methodologies has heightened the understanding of the true diversity in the rumen independent of culturing methods which has been shown that only approximately 1% of the bacteria in the gut can be identified via that method
(Dowd et al., 2008; Nocker et al., 2007). The microbial community in the rumen plays a critical role in overall health and productivity of the animal. The rumen microbiome is responsible for breaking down low-quality feedstuffs and turning it into approximately 70% of the hosts energy (Clemmons et al., 2018). Thus, research has tried to determine what a “healthy rumen microbiome” would be to maximize productivity and overall health especially with the removal of growth promoters in feedlot cattle. Studies have shown that the lower intestinal tract of cattle is predominantly strict anaerobes like Bacteroides spp., Clostridium spp., and
Bifidobacterium spp. A study by Dowd et al. (2008) found similar results with the addition of the following genera Porphyromonas, Ruminococcus, Alistipes,
Lachnospiraceae, Prevotella, Lachnospira, Bacteroidales, Akkermansia, and
Enterococcus spp. in cattle samples (Dowd et al., 2008). While the two most dominant phyla in cattle are Firmicutes, in forage-based diets, and Bacteroidetes in concentrate diets (Clemmons et al., 2018). A study looking at the microbial community from different geographical areas found seven dominating bacterial groups in all their cattle
37 Texas Tech University, Andrea English, December 2019 samples regardless of location were Prevotella, Butyrivibrio, Ruminococcus,
Lachnospiraceae, Ruminococcaceae, Bacteroidales and Clostridiales (Henderson et al., 2015). There has also been more recent work looking at the effects of feed additives, antibiotics and probiotics and the changes they potentially cause on the microbiome (Clemmons et al., 2018). Thomas et al. (2017) utilized shotgun metagenomics sequencing of samples collected from the rumen, cecum and colon of cattle at slaughter to investigate the effects of feeding antimicrobials. The most prevalent phyla included Bacteroidetes, Firmicutes and Proteobacteria regardless of treatment. Bacteroidetes had a higher abundance in the rumen compared to other locations and Firmicutes were the lowest in the rumen compared to the colon and cecum regardless of treatment (Thomas et al., 2017). Although, there was a shift at the genus level as the abundance of gram-positive organisms decreased in the cattle fed antimicrobials. Furthermore, Thomas et al., (2017) also looked at the presence of
AMR genes present and if the supplementation of antimicrobials like tylosin and monensin increases the overall resistome in the rumen, cecum and colon. They reported presence of AMR genes distributed across both treatment groups and in all three sampling locations and found no correlation with the presence of AMR genes and the supplementation of antibiotics (Thomas et al., 2017). Another interesting study conducted carried out by Noyes et al. (2016) also used shotgun metagenomics to trace the AMR from the feedlot environment, the cattle as they enter and leave, through slaughter and final product. They found that current practices and interventions used at slaughter can help reduced the occurrence of AMR determinants entering the food supply. However, at the feedlot level AMR determinants could be passed between
38 Texas Tech University, Andrea English, December 2019 pens of cattle (Noyes et al, 2016). The authors suggest that AMR determinants could potentially be entering the human environments through run-off, manure application on crops, or windborne matter (Noyes et al., 2016).
Alternatives to Antimicrobial Growth Promoters
With the growing concerns regarding the use of antimicrobials, specifically as growth promoters, in food producing animals and the changes to the VFD there is a need to investigate alternative solutions to help producers maintain animal performance and reduce disease. Some alternatives investigated include bacteriophages, phytochemicals, vaccines, antimicrobial peptides, extensive work has been done on feeding direct-fed microbials. Promising alternatives would need to provide the same beneficial effects of antimicrobial growth promoters (AGP) while remaining cost efficient for the producer.
Bacteriophages which are viruses that infect and multiple in bacteria, could be an alternative to antimicrobials (Joerger, 2003). However, they do not work on multiple pathogenic bacteria at once. Specifically, high concentrations of the targeted bacteria is needed for the greatest effect (Allen et al., 2013). This could be a promising intervention when used appropriately to control the spread of foodborne pathogens such as Salmonella, Campylobacter and E. coli O157 in food producing animals such as cattle, chickens, lamb and pork. Although, there are mixed results of the efficacy of bacteriophages. Further investigation and research are needed to determine appropriate and most effective means of application and treatment. For example, a study conducted by Wall et al. (2009) showed a reduction of Salmonella colonization of
39 Texas Tech University, Andrea English, December 2019
99.0% in the ileum, 99.9% in the tonsils, and cecum by anti-Salmonella phage therapy. Thus, if the appropriate strain of bacteriophage is used and at the appropriate time during the production chain there is a possibility of phage therapy reducing the presence of foodborne pathogens entering the food supply.
Another example of a possible alternative to antimicrobial growth promoters are phytochemicals, which are secondary plant metabolites that are included in animal feed to improve productivity (Valenzuela-Grijalva et al., 2017 and Lillehoj et al.,
2018). Phytochemicals are more difficult to utilize in ruminant diets as disruption of rumen fermentation can negatively affect animal health and performance (Lillehoj et al., 2018). There are some phytochemicals identified that could be a potential for rumen manipulation and improved animal production including saponins, essential oils, tannins, and flavonoids (Patra and Saxena, 2009). Although, in poultry they could be a more effective alternative a study conducted by Salaheen et al. in 2017 investigated the effects of blueberry and blackberry pomaces on the performance and gut microbiota of broilers. Two trials of 100 broiler chicks divided into four treatment groups including negative (A) and positive included AGP (B) controls, and two treatment groups (C – tap water supplemented with 0.1 g gallic acid equivalent
(GAE)/L of berry pomace extract (BPE) and D – tap water supplemented with 0.1 g
GAE/L of BPE then increased to 1 g of GAE/L during the last 72 hours before harvest). Not surprisingly, treatment group B supplemented with AGP gained 9.5 % more body weight than the negative control, however treatment group C gained 5.8% more body weight than the negative control treatment. Treatment group D which was supplemented with increased BPE for the last 72 hours had >12% increase in the
40 Texas Tech University, Andrea English, December 2019 cecum length. Also, treatments including AGP and BPE had greater Firmicutes to
Bacteroidetes (Salaheen et al., 2017). These data provide promising results in utilizing phytochemicals in replacement of AGP, at least in broilers, as treatments with the blueberry and blackberry pomaces had similar results as the chicks supplemented with the AGP.
Vaccines, specifically antibacterial vaccines, could be another effective option to limit the use of antimicrobials, currently focused research has been in poultry and controlling the dissemination and colonization of Salmonella in birds with variable findings. Salmonella vaccines have included, and most commonly, live strains of
Salmonella or killed vitro-grown culture vaccines which have been shown to be a safer option compared to the live Salmonella vaccines (Cheng et al., 2014). There are a few commercially available live vaccines which are used to control Salmonella infection in poultry, specifically short-lived birds, in Australia. Vaxsafe® ST (STM-1) is used by oral and coarse-spray applications to control S. Typhimurium (Sharma et al., 2018). In the U.S., there is a commercially available modified live vaccine that helps reduce the load of multiple different serotypes of Salmonella in poultry production (POULVAC®
ST; Zoetis Services LLC, U. S.) (Zoetis, 2019). Sharma et al (2018) investigated the effect of STM-1 in a commercial egg laying flock that were naturally infected with S.
Typhimurium. However, no significant differences were detected in the presence of
Salmonella for both vaccinated and unvaccinated groups in the feces or in the egg belt in early layers (Sharma et al., 2018). Sadly, the wide use and development of vaccines could be an impractical and an expensive replacement of antimicrobials in animal production.
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Continued investigation in alternatives to antibiotics is important research to not necessarily fully replace the use of antimicrobials, but provide an alternative to utilize fewer less frequent antimicrobials. Potential alternatives can help maintain animal health and productivity. Which can assist in minimizing the potential loss in performance from withdrawing sub-therapeutic antimicrobials, that are of human medicine concern, in food producing animals. Another important and perhaps the most promising alternative are probiotics or direct fed microbials which will be discussed in much more detail below.
Direct-fed Microbials
Background
Probiotic is derived from the Greek meaning ‘for life’ which has had reported beneficial health effects for hundreds of years. Elie Metchnikoff known as the father of natural immunity noticed in the early 1900’s a longer life expectancy in Bulgarian peasants that consumed large quantities of fermented milk (Metchnikoff, 1908). The bacterium in the fermented milk was later speculated to be Lactobacillus bulgarcus which Metchnikoff suggested extended the longevity of the Bulgarian’s by offsetting the effects of harmful bacteria in the gastrointestinal tract (Gasbarrini, Bonvicini and
Gramenzi, 2016; Metchnikoff, 1908).
In the beef industry, direct-fed microbials (DFM) have been used for more than
20 years and are administered by encapsulated bolus or mixed in the feed. Probiotics and DFM are terms commonly used interchangeably. However, probiotic is defined as
“a live microbial feed supplement which beneficially affects the host by improving its
42 Texas Tech University, Andrea English, December 2019 intestinal microbial balance” (Heyman and Menard, 2001). The American Feed
Control Officials (AAFCO) and the FDA require probiotics, when fed to in the ruminant diet, to be referred to as DFM and the FDA further defines DFM as
“products that are purported to contain live (viable) microorganisms (bacteria and/or yeast)” (Quigley, 2011, AAFCO 1999, FDA, 2003). Recently there has been a shift in the beef industry away from the usage of sub-therapeutic antibiotics to an increased use of DFM for disease prevention, while also maintaining animal efficacy. DFM can be used to help improve growth, increase milk production and improve feed conversion (McAllister et al., 2011; Krehbiel et al., 2002). DFM have also been shown to be an effective method to reduce the shedding of Escherichia coli O157 in the feces of beef feedlot cattle, while also improving overall animal health (Brashears et al.,
2002). It’s important to consider the effect on performance when replacing sub- therapeutic antimicrobial usage with DFM, there has been work showing that DFM can in fact reduce the pathogen load while also having no detrimental effects on performance. Furthermore, cost is an important factor effecting inclusion into feedlot diets. Ware, (2003) showed that the cost of feeding a DFM on a per head bases was
1.5 to 2.0 cents per head per day, depending on the size of the feedlot a further example if cattle were on feed for approximately 160-170 days the cost would be around $2.40 and $3.40 per head for the entire feeding period (Dahl, Lyford, &
Brashears, 2004). DFM work in multiple different ways, and there are several different types/species. These commensal or beneficial bacteria play a significant role in the gastrointestinal tract of both humans and animals. Specifically, for ruminants, these commensal bacteria can produce short-chain fatty acids to reduce the intestinal pH and
43 Texas Tech University, Andrea English, December 2019 inhibit the growth of pathogens (Krehbiel et al., 2003). They can also provide protection against pathogens by competitive exclusion, can promote and maintain the immune system in young animals (Quigley, 2011). Several examples of bacteria used in DFM products for calves/cattle include: Lactobacillus acidophilus, L. lactis, L. plantarum, L. casei, Bacillus subtilis, B. lichenformis, Enterococcus faecium,
Bifidobacterium bifidum, B. longum, and B. thermophilum (Quigley, 2011).
Lactic Acid Bacteria
Lactic acid bacteria (LAB) are gram positive bacteria, non-spore-forming rods and cocci which ferment carbohydrase resulting in an end product of largely lactic acid (Brashears et al., 2005). Most probiotics and DFM are LAB, examples include
Lactobacillus, Bifidobacterium and Enterococcus species (Klein et al., 1998). These bacteria are ideal, as the bacteria being utilized as a probiotic must be able to survive the acidic environment of the stomach. If the bacteria can endure the acidic environment in the stomach, they are then more likely to be able to have beneficial effects in the lower small intestine and colon. Attachment in the mucus layer could be from the Surface (S-) layer which covers the cell surface of some strains of
Lactobacillus this S-layer helps with adhesion to the intestinal surface (Ventura et al.,
2002).
Lactobacillus salivarius specifically is a bacteriocin producer. L. salivarius has been isolated from the gastrointestinal tracts of humans, porcine, and poultry and several strains have shown promise in being used as probiotics (Chaves, Brashears, and Nightingale, 2017). Ayala et al., (2019) tested 53 LAB isolates from multiple
44 Texas Tech University, Andrea English, December 2019 sources such as cattle feces, meat, fruit and vegetables which demonstrated antagonistic capabilities against Salmonella, E. coli O157:H7, and Listeria monocytogenes. From those isolates 38 strains showed antagonistic effects against at least one of the pathogens tested. L. salivarius (L28) was effective at inhibiting
Salmonella, E. coli O157:H7 and L. monocytogenes (Ayala et al., 2019). Ayala et al.
(2019) also showed that L28 had a low number of virulence and AMR genes. Thus, making it a promising strain to be utilized as a DFM. Shokryazdan et al. (2017) showed promising results in utilizing a mixture of three L. salivarius strains as a probiotic in poultry fed at a rate of 0.5 or 1.0 g kg-1 of diet for 42 days. The combination of L. salivarius strains regardless if fed at 0.5 or 1.0 g kg-1 had positive effects on broilers such as increased body weight, improved feed conversion, decreased presence of E. coli and aerobes, as well as birds fed 1.0 g kg-1 significantly improved the villus height and crypt depth (Shokryazdan et al., 2017). In a study conducted by Carter et al. (2016) showed that birds dosed with Salmonella Enteritidis
S1400 then treated through oral gavage with a combination of probiotics (L. salivarius
59 and Enterococcus faecium PXN33) significantly reduced the colonization of
Salmonella in the ileum, caecum and colon at 43 days of administration in poultry
(Carter et al. 2016).
Direct-fed Microbials in Cattle and Controlling Pathogen Shedding
There are several different modes of action for DFM in ruminant animals, including, competitive attachment, immune response and an antibacterial effect
(Krehbiel et al., 2002). Competitive attachment is when the bacteria, such as
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Lactobacillus, out competes the pathogen for specific sites of adherence in the intestinal surface. The adhesion can be nonspecific by physiochemical factors, or specific by adhesive bacterial surface molecules and certain receptors (Krehbiel et al.,
2002). The successful attachment and survivability of probiotic strains is highly based on the host, dose administration, along with the nutritional status of the host
(Chicklowski et al., 2007).
It is well understood that certain strains of LAB can inhibit the growth of pathogens in both humans and animals, while also having beneficial effects on the immune system (Shu and Gill, 2002). Brashears et al. (2002) investigated the ability of
LAB to reduce E. coli O157:H7 in feedlot cattle feces and rumen fluid. Selected LAB strains were tested against E. coli O157:H7 in cattle feces and rumen fluid after 0, 24, and 48 hours of incubation. All strains tested in the manure significantly reduced the presence of E. coli O157:H7 at 24 hours when compared to the control (no LAB).
However, only 7 strains were able to maintain the reduction after 48 hours of incubation (Brashears, 2002). The most effective LAB strain at reducing O157:H7 in both the feces and rumen fluid was L. acidophilus M35 (Brashears, 2002).
The prevalence of E. coli O157 in feedlot cattle feces and on hides was determined by Brashears et al. (2002) while also measuring cattle performance when cattle were supplemented with a Lactobacillus based DFM. E. coli O157:H7 was collected from 12% (155/1,290) of the fecal samples collected. While the treatment group fed NPC 747 (1 x 109 CFU of Lactobacillus acidophilus NPC 747 per steer, mixed in water and added to the diet before feed delivery) during the entirety of the study was 49% less likely to shed E. coli O157:H7 compared to the control treatment.
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In addition, cattle performance was not negatively affected by the supplementation of the Lactobacillus based DFMs. (Brashears et al., 2002).
Younts-Dahl et al. (2003) showed cattle supplemented with the L. acidophilus strain NP51 at a dose of 109 CFU per steer per day were 77% less likely to shed E. coli
O157 compared to the control diet. While LNP51 (107 CFU per steer daily of L. acidophilus NP51 and 109 CFU per steer daily of Propionibacterium freudenreichii) was 63% less likely to shed O157 and MNP51 (108 CFU per steer daily of L. acidophilus NP51 and 109 CFU per steer daily P. freudenreichii) were also effective at reducing the presence of O157 in feedlot cattle as 66% were less likely to be positive for the pathogen (Younts-Dahl et al., 2003). Showing that L. acidophilus NP51 based
DFMs are effective at controlling the rate of shedding of E. coli O157 in feedlot cattle.
A two-phase feeding trail was conducted by Peterson et al. (2006) which saw comparable results to Younts-Dahl et al. (2003) when supplementing with L. acidophilus NP51. In 2002, 192 steers were fed and in 2003 256 steers were fed and diets consisted of NP51 at a feeding rate of 109 CFU per steer daily and a control diet with no supplementation. Cattle supplemented with NP51 over the two-year period were 35% less likely to shed E. coli O157:H7 in the feces compared to the control diet.
Stephens et el., (2007) assessed the effect of varying doses of a DFM on the presence of both E. coli O157 and Salmonella in cattle feces and on hides. They determined that cattle fed a DFM of 109 P. freudenreichii NP24 and 1 x 109 L. acidophilus NP51 CFU per steer daily were 74% less likely to shed E. coli O157 and
48% less likely to shed Salmonella in the feces. At the same feeding rate the presence
47 Texas Tech University, Andrea English, December 2019 on hides of E. coli O157 was 65% less likely to occur and 14% less likely for
Salmonella (Stephens et al., 2007).
During a challenge study by Zhao et al. (1997) dairy calves were inoculated, after 12 hours of fasting, with a probiotic consisting of non-pathogenic E. coli strains and then after 48 hours the same calves were inoculated with five isolates of E. coli
O157:H7 at a (2 x 109 CFU of each strain concentration). The control group consisted of 9 calves challenged with the five-strain mixture of E. coli O157:H7. Fecal samples or rumen samples were collected to isolate E. coli O157:H7 and the probiotic bacteria.
Results showed that the calves administered the probiotic decreased the duration of ruminal carriage of the E. coli O157:H7. The E. coli O157:H7 was only found in the rumen fluid for an average of 14 days’ post inoculation, while the control group had positives for up to 26 days in the rumen fluid. Also, interestingly, at necropsy the calves fed the probiotic had no E. coli O157:H7 in the rumen compared to the control group where E. coli O157:H7 was detected in the rumen, reticulum, or omasum of 7 of the 9 calves (Zhao et al., 1997). Thus, concluding that the mode of action may not be clear on how the probiotic reduced the level of carriage for E. coli, but the probiotic can in fact reduce the level of carriage and fecal shedding.
Direct-fed Microbials and Cattle Health
Direct-fed microbials can be an effective feed additive not only to reduce the rate of shedding of potentially harmful pathogens, for ruminants, but as a preventive measure to disease. This is significant considering the detrimental impact of disease on the animal health and productivity. There are estimates assessing the cost per head
48 Texas Tech University, Andrea English, December 2019 related to disease and death in feedlot settings and medical cost associated with such incidence. Feedlot pens with an approximate 3 to 5 % mortality rate have an approximate loss per head of $112, and that loss increases as the mortality percent increases, a greater than 10% mortality rate results in $1,431 per head in medical cost
(Maday, 2016). DFMs have been shown to reduce the death loss of heavy weight cattle weighing 320 kg and 365 kg by 0.85% and 1.01% respectively (McDonald et al., 2005).
The immune system helps protect the host and does so by two main components that work together to fight off injuries that occur either internally or externally which are the innate and adaptive immunity (Erickson and Hubbard, 2000;
Parham, 2015). The innate immunity is first to be activated with a non-specific response to any foreign pathogen or injury and the release of cytokines is then possible
(Erickson and Hubbard, 2000). Adaptive immunity is much more specific with its response to invaders, which includes lymphocytes that have targeted antigens
(Erickson and Hubbard, 2000; Parham, 2015).
Probiotics have been shown to have several benefits on host immunity, specifically in the gastrointestinal tract (GIT). The GIT is critical not only for appropriate absorption of nutrients, but it protects against invaders that are consumed through food/feed. The GIT immune cells are typically clustered in Peyer’s patches, lamina propria and intraepithelial regions which are made up of natural killer cells, macrophages, neutrophils, dendritic cells, T and B lymphocytes (Krehbiel et al.,
2002). Furthermore, Lactobacillus specifically have been shown to benefit the immune system, by activating macrophages and natural killer cells, releasing dose
49 Texas Tech University, Andrea English, December 2019 and/or strain specific cytokines, increasing immunoglobulin (Ig)A and decreases IgE production (Krehbiel et al., 2002) while also increasing the intestinal permeability
(Ashraf and Shah, 2014).
Cytokines are proteins that communicate with cells that are involved in the immune response (Owen et al., 2009). During the immune response cytokines are produced to assist in immediate invasion of pathogens, which include interleukin (IL)-
1, IL-6, tumor necrosis factor-α (TNF-α) and interferons (IFN). Cytokines are mainly produced by helper T cells and macrophages (Zhang and An, 2007). For example interferons (IFN) were first discovered in 1957, by their ability to protect cells from viral infections they have also been known to regulate T and B lymphocytes, natural killer cells, macrophages and control some tumor cell growth (Pestka et al., 2004;
Capon et al., 1985). Interferons are divided into two groups Type Ⅰ which consists of seven classes: IFN-α, IFN-β, IFN-δ, IFN-ε, IFN-κ, IFN-τ, IFN-ω and Limitin. Type Ⅱ which is only made up of IFN-γ (Pestka et al., 2004). In a study conducted by Sohn et al. (2007) demonstrated that during intramammary infection polymorphonuclear neutrophil leukocytes (PMN) when introduced to lipopolysaccharide (LPS) significantly increased the secretion of IFN-γ in dairy cows, suggesting that the presence of IFN-γ increases during a bacterial infection (Sohn et al., 2007). Interleukin
17 (IL-17) is produced by the T helper 17 subset of CD4+ T cells and commonly referred to as IL-17 which is made up of 6 ligands (IL-17A – IL-17F) (Iwakura et al.,
2011). IL-17 plays a major role in the both the adaptive and innate immune response to several pathogens and in some chronic and autoimmune diseases (Mensikova et al.,
2013). In humans, it has been investigated that IL-17A was up regulated in patients
50 Texas Tech University, Andrea English, December 2019 with diagnosed Crohn’s disease and ulcerative colitis which are both autoimmune diseases (Song and Qian, 2013). IL-17A and F have been associated with other inflammatory diseases such as rheumatoid arthritis, multiple sclerosis, psoriasis, and asthma (Song and Qian, 2013). IL-17A can also protect against the invasion of pathogens, during the first signs of infection IL-17A and F are produced by innate lymphocytes and later through the adaptive immune response (Song and Qian, 2013).
When the cytokines are produced through the immune response, genes such as cytokines, chemokines, and antibacterial peptides are produced to then assist in fighting the pathogen invasion (Song and Qian, 2013). In a study conducted by
Raffatellu et al. (2007) Holstein calves were inoculated with Salmonella, two hours post infection samples were recovered, and the inflammatory response was assessed.
IL-17 increased in Typhimurium infected animals 56-fold compared to controls
(Raffatellu et al., 2007). However, Salmonella is not the only pathogen that causes an increase of IL-17, Shigella flexneri in rabbits, also Escherichia coli in piglets have shown to upregulated IL-17 due to infection (Mensikova et al., 2013). Interleukin 1
(IL-1) is made up of eleven ligands and is involved more in the innate immune response, controlling inflammation and do so through activators and suppressors
(Dinarrello, 2009). Furthermore, IL-1 is an important host defense against pathogenic organisms and can be helpful fighting those invasive microorganisms (Yu et al.,
1998). Chemokines play a major role in immunity as they assist in the movement of leukocytes, and a substantial number of chemokine and chemokine receptors have been identified in humans and mice, however only 7 have been identified in cattle and dogs. Research has focused on IL-8 (now named CXCL8) which target neutrophils,
51 Texas Tech University, Andrea English, December 2019
MCP-1 (now named CCL2) target monocytes, and IP-10 (now named CXCL10) which target lymphocytes (Gangur et al., 2002). Thus, the current study aimed to determine if supplementing cattle with L28 would have an effect on cytokine production compared to cattle feed sub-therapeutic antimicrobials, or no additional feed additive.
Conclusion and Objectives
Direct-fed microbials in the ruminant diet can play a positive role in production, by improving feed efficiency and average daily gain, also mitigating the spread and shedding of pathogenic organism in the feces. DFM can also have a protective role with the production of certain cytokines to fight against pathogens and diseases. Suggesting that they could be a positive alternative to sub-therapeutic antimicrobials to control the emergence of AMR bacteria and also maintain cattle health and performance.
Thus, the first objective of this study was to evaluate the efficacy of utilizing L. salivarius L28 as a direct-fed microbial in feedlot cattle to reduce the frequency of E. coli O157 and Salmonella found in cattle feces and determine the effect on the presence of E. coli O157, non-O157 STEC, and Salmonella on carcasses through the processing plant.
The second objective of this study was to determine if L28 fed as a DFM can be an effective alternative to subtherapeutic antimicrobials to mitigate the presence of
AMR in commensal and pathogenic bacteria shed in the feces of feedlot cattle. Also,
52 Texas Tech University, Andrea English, December 2019 to determine if there are any shifts to the microbiome or changes in cytokines of the feedlot cattle supplemented with L28.
53 Texas Tech University, Andrea English, December 2019
CHAPTER 2
2. DETECTION OF ESCHERICHIA COLI O157 AND SALMONELLA FROM FEEDLOT CATTLE FECES AND FROM CARCASSES SUPPLEMENTED WITH LACTOBACILLUS SALIVARIUS L28 AS A DIRECT-FED MICROBIAL
Abstract
Escherichia coli O157:H7 and Salmonella are two foodborne pathogens of significant public health concern. Cattle are a common reservoir for both of these pathogens and recent outbreaks have been associated with beef products. Direct-fed microbials (DFM) can be an effective pre-harvest intervention to reduce the presence of these pathogens in feedlot cattle feces. The objective of this study was to determine the frequency of E. coli O157 and Salmonella in cattle feces and on carcasses that were fed a newly isolated DFM L. salivarius L28. A total of 144 steers were divided into three dietary treatment groups. Fecal samples (N=322) were collected during the feeding trial, with collections every 28 days (n=36/collection day) until harvest.
Additionally, carcass swabs (N=431) were collected during slaughter at three processing locations. Following standard laboratory procedures fecal samples were analyzed for E. coli O157 and Salmonella in addition to non-O157 on carcasses. E. coli O157 and Salmonella was isolated from 45 and 46 respectively, of the 322 fecal samples collected during the feeding trial. The base treatment group shed significantly less (10.4%; P = 0.04) E. coli O157 compared the control (19.4%) and monpro
(12.0%) groups. No significant differences were detected across treatment for the presence of Salmonella over time. However, numerical differences showed the monpro treatment with lower presence detected at 12.0%, base (13.2%) and control
54 Texas Tech University, Andrea English, December 2019
(17.6%). As the feeding trial progressed the presence of both E. coli O157 and
Salmonella decreased for all treatments. At slaughter, 1/144 carcass tested positive for
E. coli O145 at pre-evisceration. There was a significant (P < 0.0001) effect in pen locations with the north pens (block 2 cattle) shedding significantly higher rates of
Salmonella regardless of treatment. There was no significant response to cattle that were fed the L28 and the detection of E. coli O157 and Salmonella compared to other treatment groups.
55 Texas Tech University, Andrea English, December 2019
Highlights
• Cattle in the base treatment group fed no antimicrobial, or DFM, shed less E.
coli O157 over the entire study.
• E. coli O157 and Salmonella were not detected on any carcasses at any of the
processing steps sampled.
• Feeding L28 as a DFM had no negative effect on the pathogen presence
providing an opportunity to further investigate its efficacy when incorporated
into feedlot cattle diets.
56 Texas Tech University, Andrea English, December 2019
Introduction
Foodborne illness is a significant public health burden affecting approximately
48 million people each year, leading to 128,000 hospitalizations and 3,000 deaths in the United States (Scallan et al., 2011; CDC, 2018c). Escherichia coli O157:H7 and
Salmonella are two serious foodborne pathogens with cattle as one of the main reservoirs. E. coli O157:H7 can cause serious health conditions including diarrhea, hemorrhagic colitis and potentially life threatening hemolytic uremic syndrome (Xu, et al., 2014). E. coli O157 can also have a large economic impact. In the United States estimated cost of healthcare, social care, and productivity losses are nearly $600 million per year and for the food industry it is even greater, considering recalls and reduced trade which can elevate this estimate closer to tens of millions of dollars lost
(Matthews et al., 2013). Salmonella is a leading cause of foodborne illness worldwide and continues to be a significant concern in the United States. The CDC estimates
Salmonella is responsible for 1.2 million illnesses of which 23,000 are hospitalizations and 450 deaths each year (CDC, 2019f). E. coli O157 and Salmonella related illnesses have been associated with consumption of beef products along with other products such as eggs, leafy greens, soy nut butter, sprouts and raw cookie dough (CDC, 2015;
CDC, 2019e). E. coli O157:H7 in 1994 was declared to be an adulterant in ground non-intact beef products by the U. S. Food Safety and Inspection Service (FSIS)
(Anonymous, 1999). There have been numerous studies determining the prevalence of
E. coli O157 and Salmonella in feedlot cattle feces and on carcasses with the prevalence trending higher during summer months compared to winter (Barkocy-
57 Texas Tech University, Andrea English, December 2019
Gallagher et al., 2003). Barkocy-Gallagher et al. (2003) found that E. coli O157 prevalence during the summer was 12.9% in feces, compared to 0.3% during the winter months, while Salmonella presence during the summer was 9.1% and 2.5% during winter. According to the National Animal Health Monitoring System
(NAHMS) feedlot study in 2011, that monitors the presence of Salmonella in feedlots,
Salmonella was detected at least once in 60.3% of the 68 feedlots observed (APHIS,
2014). With 35% positives from the 202 total pens tested. The three most common
Salmonella serotypes identified during the study were Anatum, Montevideo and
Kentucky (APHIS, 2014).
Thus, there is a continuous need to investigate effective pre-harvest interventions to reduce the rate of cattle shedding of pathogens prior to being sent to harvest, which can potentially help reduce the likelihood of pathogens entering the food supply through carcass contamination.
Pre-harvest interventions such as probiotics are a potential option for feedlot cattle. Probiotics, when fed in the ruminant diet, are commonly referred to as direct- fed microbial (DFM) and the U.S. Food and Drug Administration (FDA) defines DFM as “products that are purported to contain live (viable) microorganisms (bacteria and/or yeast).” Lactobacillus based direct-fed microbials (DFM) have been utilized in the beef industry for more than 20 years with promising success in reducing the pathogen presence in feces of feedlot cattle, while also improving growth and milk performance, and increasing feed efficiency (McAllister et al., 2011; Krehbiel et al.,
2003). Brashears et al., (2002a) showed positive effects of lactic acid bacteria reducing
E. coli O157 presence in cattle feces and rumen fluid. Younts-Dahl et al., (2004)
58 Texas Tech University, Andrea English, December 2019 reported that feeding L. acidophilus strain NP51 at a rate of 109, 108, and 107 CFU per steer per day, were effective at reducing the prevalence of E. coli O157 in fecal shedding throughout the entirety of the feeding trial. Feeding NP51 at a rate of 109 E. coli O157 was 77% less likely to be detected in the feces compared with the control steers (Younts-Dahl et al., 2004). Also, Stephens et al. (2007) showed that feeding P. freudenreichii (NP24) plus L. acidophilus (NP51) at a rate of 1 x 109 CFU per steer per day was effective at reducing the presence of Salmonella in feedlot cattle feces and on hides.
Lactobacillus salivarius L28 is a newly isolated bactericin-producing bacteria that has shown promising effects at reducing E. coli O157, Salmonella and Listeria in vitro (Ayala et al., 2017). Thus, the objective of this study was to evaluate the efficacy of utilizing L. salivarius L28 as a direct-fed microbial in feedlot cattle to reduce the frequency of E. coli O157 and Salmonella found in cattle feces and determine the effect of the presence of E. coli O157, non-O157 STEC, and Salmonella on carcasses through the processing plant.
Material and Methods
Cattle, pen assignments and treatments.
The feedlot finishing study was conducted from October 2016 to March 2017.
One hundred and forty-four crossbred steers were received at the Texas Tech
University Burnett Center for Beef Cattle Research and Instruction (Lubbock, TX).
All steers were obtained from a single source with an average body weight of 371 + 19 kg. All cattle were initially processed which included treatment for clostridial, viral
59 Texas Tech University, Andrea English, December 2019 and mycoplasma diseases, treatment for internal and external parasites, individual tag placements, and implantation of Synovex Plus (200 mg TBA + 28 mg estradiol benzoate (Zoetis, Florham Park, NJ).
Prior to study initiation all steers were sorted into 12 blocks. Pen assignments were randomly assigned to one of three dietary treatments with 48 steers per treatment
(4 steers per pen). There was a total of 12 pens designated for each dietary treatment, with a total of 36 pens. Cattle were housed in adjoining partially slotted-floor concrete pens. No β-adrenergic agonists was fed.
Composition of dietary treatments is shown in Table 1. All treatments were included in a steam-flaked corn-based finishing diet fed once daily using a slick bunk management approach.
Table 1. Treatment description and formulation with all diets based on a traditional finishing diet.
Treatment Designation Diet Formulation Base • No Direct-fed microbial (DFM) • No Tylosin • No Monensin Control • Tylosin; 88mg/animal/day of diet dry matter (DM) (Tylan 40; Elanco, Greenfield, IN) • Monensin; 330 mg/animal/day of diet DM (Rumensin 90, Elanco) Monpro • DFM, Lactobacillus salivarius (L28) at a feeding rate of 106 CFU/animal/day • Monensin; 330 mg/animal/day of diet DM • No Tylosin
Cattle were weighed at 28 day intervals, alternating between pen and individual weights. Individual body weight (BW) was taken on days 0, 56, and 112; final BW was collected on days 140 and 158 for the 1st and 2nd slaughter groups,
60 Texas Tech University, Andrea English, December 2019 respectively. Pen weights were collected on days 28 and 84. Steers were shipped when approximately 60% within a block had sufficient external fat cover to grade United
States Department of Agriculture Choice.
Sample Collection.
Fecal grabs were collected from the rectum of three animals per pen per treatment at diet initiation (day 0) and day 56 on feed while cattle were restrained in a processing chute. After immediate transportation to the laboratory, composite samples were made representing each respective pen (n =36/day) with equal parts from each individual fecal collected (approximately 100 g). Additionally, an individual fecal grab was collected from each animal prior to harvest at days 140 (group 1, n =72) and
158 (group 2, n =72) For each fecal collection a new latex glove and arm-length palpation sleeve was worn to transfer samples immediately into individually labeled specimen cups.
Fecal pats from freshly deposited fecal samples were collected from the floor of each pen, representing a minimum of 3 animals per pen, on day 28, 84, and 112
(n=36/collection day), using plastic spoons. Samples were then transferred into individually labeled specimen cups for the detection of E. coli O157 and Salmonella to monitor pathogen presence throughout the feeding trial.
Carcasses were swabbed from the right shoulder area of the carcasses, approximately a 600 cm2 area (Younts-Dahl et al., 2003) throughout the slaughtering processes; pre-evisceration (n =144), post-evisceration (n =143), and in the cooler (n
=144) with sterile cellulose sponges pre-moistened with 25 ml of buffered peptone water (BPW, World Bioproducts; Mundelein, Illinois). For all collections, samples
61 Texas Tech University, Andrea English, December 2019 were placed in plastic, insulated coolers with ice packs for transport back to the food microbiology laboratory at Texas Tech University for microbial analysis.
Microbial Detection on Fecal Samples.
Fecal Samples.
Fecal samples (n =322) were subject to bacterial isolation and detection of
Escherichia coli O157:H7 and Salmonella spp.
Escherichia coli O157 detection.
For detection of E. coli O157, 1 g of feces was weighed and transferred to 9 m of Gram-Negative broth with vancomycin, cefixime and cefsulodin (GN-VCC; Hardy
Diagnostics; Santa Maria, CA) and incubated at 37°C for 6 h. Enriched GN-VCC was then further subjected to Immunomagnetic Separation techniques (IMS) using anti-E. coli O157 Dynabeads® (Invitrogen; Grand Island, New York). The product from IMS was then spread plated onto CHROMagar™ O157 agar (CHROMagar™; Springfield,
NJ). Presumptive positives colonies were confirmed using latex agglutination
(DrySpot™ E. coli O157 Latex Agglutination Test, Thermo Scientific; Lenexa, KS).
Confirmed positive isolates were enriched in Tryptic Soy Broth (TSB; Becton,
Dickinson and Company; Franklin Lakes, NJ) for 24 h at 37°C and frozen in duplicate with 20% glycerol. Further confirmation was done on 49 previously confirmed E. coli
O157 isolates by testing genomic DNA by real-time (quantitative) PCR (qPCR), using the following primers: slt1 F: 5’-TGTAACslt1TGGAAAGGTGGAGTA-3’; slt1 R:
5’-GCTATTCTGAGTCAACGAAAA-3’; slt2 F: 5’-
GTTTTTCTTCGGTATCCTATTC-3’; slt2 R: 5’-GATGCATCTCTGGTCATTGTA-
3’; int F: 5’-GACTGTCGATGCATCAGGCAA-3’; int R: 5’-
62 Texas Tech University, Andrea English, December 2019
TTGGAGTATTAACATTAACCC-3’ (Nielson and Anderson, 2003). Each PCR amplification was performed in a 25-µL reaction volume containing SYBR Green
Master Mix (Qiagen, Valencia, CA), 0.2 µm of primer stl1/slt2, 0.075 µm of primer int and 2.5-µL of each RNA sample. Amplification was performed in a MX3005P
(Stratagene; La Jolla, CA, USA) instrument with the following thermocycler program:
2 min at 95°C, followed by 20 cycles of 30 s at 95°C, 1 min at 59°C, 1 min at 72°C, then 20 cycles of 30 s at 95°C, 1 min at 49°C and 1 min at 72°C, completed by a final elongation step of 5 min at 72°C.
Salmonella Detection.
Approximately, 1 g of fecal sample was aseptically weighed and placed into 9 ml of tetrathionate broth with iodine supplementation (TT; Neogen; Lansing, MI) and
1 g into Rappaport-Vassiliadis broth (RV; Oxoid; Hampshire UK) and incubated at
42°C for 18-24 h. Following enrichment both TT and RV samples were streaked to
Xylose-Lysine-Tergitol agar (XLT4; Hardy diagnostics; Santa Maria, CA) and incubated for 24-48 h at 37°C. Presumptive positive Salmonella colonies by phenotypical characteristics on XLT4 agar (H2S-positive; black or black centered with a yellow periphery after 18-24 h incubation or entirely black) were subject to latex agglutination (Oxoid; Hampshire, UK) for confirmation using manufacturer’s instructions. Isolates which agglutinated positive were selected for further enrichment into TSB for 24 h at 37°C and subject to additional confirmation completed using
BAX® system real-time PCR assay (Hygiena, LLC; USA) for Salmonella. Samples with a positive result were grown in TSB for 18-24 h at 37°C and frozen in duplicate with 20% glycerol. 63 Texas Tech University, Andrea English, December 2019
Microbial Detection of Carcass Samples.
Carcass swab samples (n =431) were subject to bacterial detection of
Escherichia coli O157, non-O157 shiga-toxin E. coli (STEC) and Salmonella spp.
Carcass sponges in BPW were stomached for 30 s at 230 rpm then 1 ml of sample was added to 9 ml of Tryptic Soy Broth (TSB; Becton, Dickinson and Company; Franklin
Lakes, NJ) and incubated for 18-24 h at 42°C. Samples were then analyzed using
GeneDisc® Cycler (Pall Corporation; Port Washington, New York) for Salmonella and STEC detection.
Presumptive Salmonella positives from GeneDisc® were selected from the original TSB enrichment broth, and 1 ml was added to 9 ml of TT broth with iodine and 1 ml into 9 ml of RV for selective enrichment. Both TT and RV were incubated at
42°C for 24 h then streaked to xylose lysine deoxycholate (XLD; Hardy-Diagnostics;
Santa Maria, CA) and brilliant green sulfa (BGS; Remel; Lenexa, KS) agar plates.
XLD and BGS were incubated for 18-24 h at 37°C and presumptive Salmonella colonies were subjected to latex agglutination for confirmation using manufacturer’s instructions. Isolates confirmed positive through agglutination were transferred to TSB and grown at 37°C for 24 h then frozen in duplicate with 20% glycerol.
Positive samples through GeneDisc for E. coli O157 and non-O157 STEC were selected from the original TSB enrichment broth and subject to IMS using anti-
E. coli O157, O26, O111, O145, O45, O103 and O121 Dynabeads®. The product from IMS was either spread plated to Tryptone Bile X-Glucuronide (TBX; Neogen;
Lansing, MI) or CHROMagar™ O157 agar and subject to latex agglutination.
64 Texas Tech University, Andrea English, December 2019
Confirmed positive isolates were enriched in TSB for 24 h at 37°C and frozen in duplicate with 20% glycerol.
Statistical analysis.
Statistical analysis was performed using SAS 9.4 (SAS Institute Inc., Cary,
N.C.). E. coli O157 and Salmonella results were reported as negative = 0 and positive
= 1. Organism presence frequency data were analyzed using chi-squared goodness of fit with the PROC FREQ procedure of SAS, controlling by treatment and sampling day. Odds ratios were determined using relative risk 2 X 2 tables within the PROC
FREQ procedure. An analysis of variance procedure, PROC GLM, was used to calculate least squared means separation between treatments for pen location (north versus south pens). Significance for all analysis was set at p < 0.10.
Results and Discussion
No steers were removed during the entirety of the study from October 2016 –
March 2017. There was no significant difference across treatments for final carcass adjusted body weight and overall average daily gain (ADG), carcass weight, dressing percent, loineye area and yield grade. All carcasses graded USDA choice or better
(unpublished data). All experimental procedures were conducted in accordance with an approved Texas Tech University Animal Care and Use Committee (protocol number 16054-06)
E. coli O157 Detection.
In total, E. coli O157 was isolated from 45 of the 322 fecal samples collected during the entire feeding trial, with the control group having the highest frequency of
65 Texas Tech University, Andrea English, December 2019 detection at 19.4%, base at 10.4%, and monpro with 12.0 % positive E. coli O157 samples (Figure 1). The base treatment group had significantly less (P = 0.04) E. coli
O157 positives during the complete feeding trial compared to control and monpro treatment groups. Over time the base group was 52% less likely to shed E. coli O157 compared to the control treatment (Table 2) with a relative risk of 0.53 (P = 0.08). The monpro treatment was 15% less likely to have E. coli O157 presence compared to the base treatment (Table 2). Younts-Dahl et al. (2004) reported similar prevalence data of E. coli O157 in feedlot cattle compared to the current study and showed that supplementing feedlot cattle with a Lactobacillus direct-fed microbial was effective at reducing the presence of E. coli O157 in cattle feces. Cattle supplemented with
HNP51 (109 CFU per steer daily L. acidophilus NP51 and 109 CFU per steer daily P. freudenreichii), MNP51 (108 CFU per steer daily L. acidophilus NP51 and 109 CFU per steer daily P. freudenreichii) and LNP51 (107 CFU per steer daily L. acidophilus
NP51 and 109 CFU per steer daily P. freudenreichii) had a lower prevalence of E. coli
O157 throughout the complete feeding trial, at 6.8, 9.9, 10.5 % respectively, compared to their control at 23.9 % which was not supplemented with any direct-fed microbial
(Younts-Dahl et al., 2004).
On day 0, the control cattle group had 66.7% (8/12) of fecal samples positives for E. coli O157 where the base treatment had 50% (6/12) positive and monpro group with the lowest percentage of positives at 41.7% (5/12) (Figure 2). The control group had significantly greater (P < 0.10) shedding of E. coli O157 on day 0 compared to the monpro treatment group. However, on day 28 of feeding the monpro treatment group had a higher percentage of positives at 33.3%, while control and base treatments had
66 Texas Tech University, Andrea English, December 2019
16.7% of samples positive for E. coli O157. Control cattle on day 56 had higher rates of shedding E. coli O157 at 41.7% compared to monpro (33.3%). The base treatment group had significantly lower rates of shedding (P < 0.10), with only 1 confirmed positive sample, compared to control cattle on day 56. Presence of E. coli O157 was the same for days 84 and 112 for all treatment groups with control having 8.3% confirmed positives, and no E. coli O157 presence for base and monpro treatments. At final collection prior to harvest (days 140 and 158 for group one and two respectively) control cattle had 19.5% (4/48) positive samples, while base treatment had 5.3%
(2/46), and no cattle in the monpro treatment had positive fecal samples prior to harvest (Figure 2). With similar results, Brashears et al. (2002b) reported a gradual decrease in the presence of E. coli O157 in feedlot cattle feces, with an 11.1% average decrease in the odds of E. coli O157 being detected.
Salmonella Detection.
Salmonella was isolated from 46 of the 322 fecal samples collected from
October to March. The control group had the highest Salmonella presence during the feeding trial at 17.6% (19/322), furthermore total base and monpro treatment positives were 13.2 and 12.0%, respectively with no significant differences detected across treatments (P = 0.62). There were no significant differences detected for the presence of Salmonella in fecal samples from control (P > 0.10; odds ratio = 0.71) and monpro
(P > 0.10; odds ratio = 1.11) cattle compared to the base treatment during the complete feeding trial (Table 2).
67 Texas Tech University, Andrea English, December 2019
At the initiation of the feeding trial (day 0), Salmonella was present in 41.7% of both control and base treatment groups and 33.3% in the monpro group. While on day 28, less Salmonella was detected in the monpro cattle group (2/12) with 25% presence in both control and base treatment groups. Detection of Salmonella at day 56 was the same for all three treatment groups at 8.3% (1/12). Salmonella shedding was significantly less at days 56 and 84 compared to days 0 and 28 (P < 0.10).
Furthermore, on day 84 the control and monpro treatment group each had 1 positive fecal sample while no Salmonella was detected in the base group. On day 112, no
Salmonella was detected in the base treatment group while the same presence (16.7%) was detected in the control and monpro groups. At the end of the feeding trial, (days
140 and 158), the control treatment had the highest presence of Salmonella at 18.3%
(7/48), while the base group had 13.2 % positives followed by the monpro treatment shedding the lowest percentage of Salmonella at 7.9% (3/48) (Figure 3). For the current study the presence of Salmonella decreased or remained the same with increased time on feed which is similar to previously reported presence of Salmonella shedding in feedlot steers (APHIS, 2014). However, other studies have also reported a decreased presence of Salmonella as days on feed increases (Khaitsa, et al., 2006).
There are multiple factors that have been reported that could contribute to the different reports such as seasonality, pen environment/condition, and transportation stress
(Smith et al., 2001; Miller, et al., 2008; Ekong, et al., 2015, Barham, et al., 2001).
Pen Location Differences.
E. coli O157 was detected in 14.2% (23/162) of fecal samples collected from block 2, and 13.1% (21/160) in block 1 (Table 3). No significant differences were
68 Texas Tech University, Andrea English, December 2019 detected in presence comparing block 1 to block 2 (P = 0.90). During day 0 of the feeding trial E. coli O157 was the most prevalent at 44.4% for block 2 and 61.1% in block 1. E. coli O157 presence for block 2 on days 28, 56, 84, 112, and 140 were 22.2,
38.9, 5.6, 5.6 and 2.8% respectively. Block 1 cattle had less E. coli O157 shedding during the remaining time on feed with 22.2, 16.7, 0, 0, and 4.3% positive fecal samples on days 28, 56, 84, 112, and 140.
Block 2 cattle had significantly greater (P < 0.0001) Salmonella fecal shedding during the complete study compared to block 1. Total Salmonella presence for block 2 was 19.8% (32/162), compared to 5.6% (9/160) for block 1 (Table 3). Day 0 of diet initiation Salmonella presence was the highest for both block 1 and 2 at 33.3% and
44.4% respectively. In block 1 Salmonella was only detected at one additional time point, on day 28, 16.7% of samples were positive and Salmonella was not detected again for the completion of the study. However, for block 2 Salmonella was detected on day 28 at 27.8%, and gradually decreased to 16.7% on day 56. Days 84, 112, and
140 had 11.1, 22.2, and 13.9% positive Salmonella samples.
The presence of a pen having both E. coli O157 and Salmonella positives was recorded, however no significant differences were detected comparing block 1 and block 2 presences for both pathogens. E. coli O157 and Salmonella was present in 7 of
162 samples for block 2 and 3.8% (6/160) for block 1. With the highest rate of both organisms being present on day 0, with block 2 having 4 out of 18 pens positive and block 1 having 5 of 18 pen positives. Day 28 for block 2 had 11.1% of pens positive for both Salmonella and E. coli O157, while block 1 had 5.6% positive pens.
Furthermore, on day 140, block 2 had 1.4% positive pens for both organisms.
69 Texas Tech University, Andrea English, December 2019
It is well understood that cattle are a reservoir for both E. coli O157 and
Salmonella which can be shed asymptomatically, however cattle are also susceptible to reinfection through environmental factors, such as the pathogens being transferred through feed, water sources, or simply persisting in the pen environment. In the current study we saw no differences between group 1 and 2 and E. coli O157 shedding however, group 2 steers shed significantly higher rates of Salmonella (Table 3). There is extensive work focused on environmental factors and transmission of E. coli O157 in feedlot cattle (Bach, et al. 2002, Smith et al., 2001, McGee, et al., 2004), but less work directed towards environmental transmission of Salmonella in feedlot cattle. For this study it is speculated that there could be similar environmental factors affecting the persistent Salmonella shedding in group 2 steers. Water troughs have been shown to be a major reservoir for harboring E. coli O157 for up to 245 days (LeJeune, et al.,
2001), pen environment has also been a contributor to increased shedding of E. coli
O157 in cattle, with muddier conditions resulted in greater shedding (Smith et al.,
2001).
Postharvest.
From the total 431 swab samples collected during harvest at pre-evisceration (n
=144), post-evisceration (n =143) and in the cooler (n =144) no E. coli O157 or
Salmonella was detected. However, one carcass screened positive for E. coli O145 at pre-evisceration in the control treatment group. Arthur et al. (2004) and Elder et al.
(2000) have shown correlations to the presence of pathogen shedding in feces of feedlot cattle and on hides correlates to the presence of pathogens further along in
70 Texas Tech University, Andrea English, December 2019 processing plants. Thus, effective intervention applications and sanitary dressing procedures in the commercial processing plant could be another result of the lack of prevalence of E. coli O157, non-O157 STEC and Salmonella in the processing plant.
Conclusion
The current study is the first to our knowledge utilizing Lactobacillus salivarious L28 as a DFM to reduce the fecal shedding of E. coli O157 and
Salmonella in beef feedlot cattle. Over the complete feeding trial, the control cattle had a higher presence of both E. coli O157 and Salmonella in the feces, although not significantly different than monpro and base treatments. The current study also shows a trend of less Salmonella shedding in the monpro treatment group. Suggesting that the L28-supplementation could be an effective pre-harvest control of Salmonella presence in feedlot cattle however, more work is needed to verify the differences in treatment groups. Also, this study was relatively small and conducted during months where pathogen shedding has been proven to be lower, thus further research is warranted to determine the efficacy of the current DFM on reduction of fecal pathogen shedding in feedlot cattle feces and presence on carcasses at harvest during months of recorded higher shedding rates. It is important to continue to find pre-harvest interventions that can effectively reduce the shedding of foodborne pathogens.
Reduction in shedding of these pathogens in feedlot cattle reduces the pathogenic microbiological load entering commercial processing plants ultimately reducing the likelihood of these pathogens contaminating fresh beef and entering the food supply.
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100 Control Base Monpro 90 80 70 60 50 40 Percent Positives a 30 b a,b 19.4 17.6 20 13.2 10.4 12.0 12.0 10 0 E. coli O157 Positives Salmonella Positives
Figure 1.Overall detection of E. coli O157 and Salmonella from fecal samples collected from October 2016 to March 2017. Base = No DFM, tylosin, monensin (n = 106), control = tylosin and monensin (n=108), and monpro = DFM (L28), and monensin, no tylosin (n = 108). Different letters represent significant differences across treatments for each organism (P<0.1).
72 Texas Tech University, Andrea English, December 2019
100
90 Control Base Monpro 80
70 a
60 a, b
50 b b 40 Percent Positives b, c b, c 30
20 c, d c, d d, e d, e d, e d, e 10 d, e d, e d, e d, e d, e e 0 0 28 56 84 112 >140
Days on Feed
Figure 2. Detection of E. coli O157 from fecal samples collected from October 2016 to March 2017. Fecal samples on days 0, 56, and >140 (prior to harvest) were rectal grabs while days 28, 84, and 112 were collected from the floor of each treatment pen. For days 0, 28, 56, 84 and 112 (n=36) and day >140 (n=142). Treatments: base = No DFM, tylosin, monensin, control = tylosin and monensin, and monpro = DFM (L28), and monensin. Columns with different letters represent significant differences (P < 0.10).
73 Texas Tech University, Andrea English, December 2019
100
90 Control Base Monpro 80
70
60
50 a a
Percent Positives 40 a,b
30 a,b,c a,b,c c,d b,c,d b,c,d 20 b,c,d c,d c,d c,d c,d c,d c,d d 10 d d 0 0 28 56 84 112 >140 Days on Feed
Figure 3. Detection of Salmonella from fecal samples collected from October 2016 to March 2017. Fecal samples on days 0, 56, and final (prior to harvest) were rectal grabs while days 28, 84, and 112 were collected from the floor of each treatment pen. For days 0, 28, 56, 84 and 112 (n=36) and day >140 (n=142). Treatments: base = no DFM, tylosin, monensin, control = tylosin and monensin, and monpro = DFM (L28), and monensin. Columns with different letters represent significant differences (P < 0.10).
74 Texas Tech University, Andrea English, December 2019
Table 2. Overall odds ratios (OR), relative risk (RR) for potential positives, 95% confidence intervals (CI) for the RR and P values for the likelihood of fecal shedding of E. coli O157 and Salmonella in control and monpro treatments compared to the base treatment from October 2016 to March 2017.
Treatment OR RR CI P values Fecal shedding of E. coli O157 Controlb vs Basea 0.48 0.53 0.27-1.05 0.08 Monproc vs Base 0.85 0.86 0.40-1.84 0.83 Fecal shedding of Salmonella Control vs Base 0.71 0.75 0.39-1.42 0.45 Monpro vs Base 1.11 1.09 0.54-2.22 0.84 a No Direct-fed microbial (DFM), no tylosin, no monensin. b Tylosin; 88mg/animal/day of diet dry matter (DM), monensin; 330 mg/animal/day of diet DM. c DFM; Lactobacillus salivarius (L28) at a feeding rate of 106 CFU/animal/day, monensin; 330 mg/animal/day of diet DM, and no tylosin.
75 Texas Tech University, Andrea English, December 2019
Table 3. Visual representation of samples testing positive for E. coli O157 and/or Salmonella by pen number and sampling time. Significant difference was set at (P < 0.10) comparing north and south pen presence for E. coli O157 (E), Salmonella (S), both Salmonella and E. coli O157 (S/E). North Pens – Block 2 Month DOF1 n 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2016-17 0 18 October S/E S E S E S/E S/E S S S/E E E 28 18 November S/E E S/E E S S S 56 18 December S E E E E S S E E E 84 18 January E S S 112 18 February S E S S S >1402 72 March S S S S S S S/E S E S S South Pens – Block 1 Month DOF1 n 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 2016-17 0 18 October S/E S/E E E E E E E S S/E S/E S/E 28 18 November S/E S S E E E 56 18 December E E E 84 18 January 112 18 February >1402 70 March E E E 1DOF – days on feed 2On day 140 individual fecal grabs were collected thus presence is based on individual animal presence. During days 0 and 56 fecal samples were made into composites (3 to 4 fecal rectal grabs = 1 composite sample per pen). During days 28, 84 and 112 fecal samples were made into composites (3 to 4 fecal pats per pen = 1 composite sample per pen). Base = No DFM, Tylosin, Monensin (Pens: 4, 6, 9, 14, 16, 19, 39, 38, 34, 32, 27, 25) Control = Tylosin and Monensin (Pens: 3, 7, 10, 13, 15, 20, 40, 36, 33, 30, 29, 24) Monpro = DFM (L28), and Monensin (Pens: 5, 8, 11, 12, 17, 18, 41, 37, 35, 31, 28, 26
76 Texas Tech University, Andrea English, December 2019
CHAPTER 3
3. ANTIMICROBIAL RESISTANCE PATTERNS AND MICROBIOME CHANGES IN FEEDLOT CATTLE SUPPLEMENTED WITH LACTOBACILLUS SALIVARIUS L28
Abstract
Antimicrobials such as tylosin and monensin together with direct-fed microbials (DFM) are feed additives that have been widely used in livestock production systems. The subtherapeutic use of antimicrobials is considered one of the contributing causes of the growing antimicrobial resistance public health threat. Thus, the first objective of this study was to determine if L. salivarius L28 could mitigate antimicrobial resistant commensal and pathogenic bacteria during the feeding period.
Furthermore, few studies have been developed analyzing the impact of these feed additives on the microbiome of the animal. Thus, the second objective was to assess the effect of the L28 supplementation on cattle gut microbiota and immune system through microbiome analysis and cytokine quantification, respectively.
A total of three dietary treatments (control, base and monpro) based on conventional high concentrate diets were fed to finish cattle (n=144) for harvest. Fecal samples were collected on days 0, 56 and 140. Traditional culture methods were used to isolate bacteria. Antibiotic susceptibility testing was done using the micro-broth dilution (Sensititre™) susceptibility minimum inhibitory concentration plates, following the National Antimicrobial Resistance Monitoring System protocol. For microbiome analysis DNA was extracted from fecal samples and 16s-metageomics was performed on an Illumina Miseq. Blood samples were collected for cytokine and chemokine analysis on day 140 from one animal per pen. 77 Texas Tech University, Andrea English, December 2019
Overall generic E. coli resistance for each treatment group was 39.8%, 44.2% and 18.3% for base, control and monpro, respectively. Enterococcus resistance was higher overall with base, control and monpro having 87.8%, 93%, and 83.3% of resistant isolates, respectively. Relative abundance of bacteria in the phyla
Bacteroidetes increase over time with the overall reduction in relative abundance of all other bacteria. For the base treatment group there was the greatest changes over time.
No differences across treatments were detected for cytokine and chemokine quantification.
Supplementation of cattle diets with L28 was the most effective at mitigating the AMR generic E. coli although had little effect on the rate of Enterococcus AMR resistant. Further work is warranted to better understand the full potential of L. salivarius L28 as a feed supplement for feedlot cattle.
78 Texas Tech University, Andrea English, December 2019
Highlights
• The rate of AMR generic E. coli was significantly decreased in the group of
cattle supplemented with L28.
• L28 had no effect on the rate of AMR Enterococcus isolates tested.
• Supplementation with subtherapeutic antimicrobials and L28 had no
significant effects on the microbiome of feedlot steers.
• Cattle diet had no effect on cytokine or chemokine concentration.
79 Texas Tech University, Andrea English, December 2019
Introduction
The development of antimicrobials has been one of the greatest achievements in human and animal health, significantly reducing the burden of disease. Although, shortly after development of the first antibiotic microorganisms started to develop resistance. It is speculated that widespread usage of antimicrobials specifically for food producing animals (beef, poultry, swine, and aquaculture) has increased selection pressure and contributed to the spread of antimicrobial resistant pathogens and infections globally (Laxminarayan et al., 2015). Antimicrobials have been used in animal production systems since the 1950’s for three main purposes: prophylactic
(preventive), metaphylaxis (treatment of infected animals or susceptible animals) and subtherapeutic/growth promoters (to improve animal growth rates and feed efficiency)
(Laxminarayan et al., 2015). Subtherapeutic doses of antimicrobials in cattle specifically have been used widely to treat and prevent liver abscesses (Brashears et al., 2006). The most widely marketed antimicrobial drugs in 2016 for food producing animals include tetracycline at 5,866,588 kg which is a drug class of medical importance and ionophores at 4,602,971 kg; however, ionophores are not of medical importance (FDA, 2016).
Antimicrobial resistant infections are more difficult to treat, compared to susceptible bacterial infections and can result in longer and more frequent hospital visits, longer duration of illness, and increased mortality (Aidara-Kane et al., 2018). A study conducted by Roberts et al., (2009) analyzed the hospital costs of AMR
80 Texas Tech University, Andrea English, December 2019 infections in Chicago, the medical cost for AMR infections ranged from $18,588 to
$29,069 per patient, with a 2-fold higher death rate (Roberts et al., 2009).
The CDC estimates that 2 million people contract an AMR bacterial infection with specific resistance to the drug designated to treat the infection. From these AMR infections, approximately 23,000 deaths occur and more die as a direct result of complications from the AMR infection (CDC, 2013). Thus, the specific concern is for the antimicrobials that are used in both human and veterinary medicine and are essential for treating human bacterial infections. The FDA made recent changes to the
Veterinary Feed Directive to improve the “judicious use of medically important antimicrobial drugs in food producing animals” which restricts the use of such antimicrobials as growth promoters, in animal feed or water, and requires veterinary oversight for the remaining therapeutic use (anonymous, 2015). It is important to monitor AMR bacteria in cattle through the entirety of the feeding trial to determine the rate of change over time.
In the current study commensal (good bacteria) and pathogenic organisms were monitored for AMR. It is well known that cattle are natural reservoirs for bacteria such as Escherichia coli (generic non-type specific and O157:H7), Salmonella spp. and Enterococcus spp. In fact, E. coli makes up approximately 1% of the colonic bacteria in cattle (Diez-Gonzalez et al., 1998). Commensal organisms that are commonly found in the gastrointestinal tract of animals and are ubiquitous in nature such as E. coli and Enterococcus spp. are critical to monitor when discussing AMR because of their ability to acquire and transfer antimicrobial resistance genes.
Specifically, vancomycin-resistant Enterococcus (VRE) is considered a serious threat
81 Texas Tech University, Andrea English, December 2019 by the CDC with over 20,000 infections each year (of the approximately 66,000 total
Enterococcus infections), resulting in 1,300 deaths (CDC, 2013). Enterococcus are of significant medical importance as they are a leading cause of nosocomial infections in humans each year. E. coli is a prominent bacterium in the cattle gastrointestinal tract and can serve as reservoir of AMR genes which can be exchanged with other bacterial species. Furthermore, Salmonella is a growing zoonotic pathogen, causing an estimated 1.4 million infections each year (Scallan, 2011). Drug resistant non- typhoidal Salmonella has shown to cause 100,000 drug resistant infections each year in the U. S. and drug resistant Salmonella serotype Typhi results in 3,800 drug resistant infections each year (CDC, 2013). Multi-drug resistant (MDR) Salmonella has also become an emerging problem worldwide with increased resistance to clinically important antibiotics such as fluoroquinolones and third generation cephalosporins (Hur et al., 2012). According to the National Antimicrobial Resistance
Monitoring Systems (NARMS) annual animal report in 2011, 20.0% of the detected in cattle Salmonella had resistance to 3 or more antimicrobial classes, and 12.6% had resistance to ampicillin, chloramphenicaol, sulfisoxazole and tetracycline (ACSSuT).
In the current study in combination with the phenotypic AMR patterns we further investigated the genotypic AMR determinants of pathogenic isolates of E. coli O157 and Salmonella. Gathering the knowledge and comparing the genotype and phenotype of these pathogenic microorganisms can provide a better picture of what is potentially contributing to the resistance.
Lactic acid bacteria have been used widely in feedlot cattle as DFM to reduce the shedding of E. coli O157 while also maintaining animal performance. Brashears et
82 Texas Tech University, Andrea English, December 2019 al. (2002) reported that feeding Lactobacillus acidophilus NPC 747 at a rate of 1 x 109
CFU per head per day can reduce the shedding of E. coli O157 and reduce the pathogen’s presence on hides at harvest. However, there is limited research on the use of DFM in feedlot cattle diets to reduce the presence of antimicrobial resistant bacteria shed in the feces.
The gut microbiota present in the animal’s gastrointestinal tract is critical in maintaining the animal’s health, growth and productivity (Dowd et al., 2008). The microbial community in ruminant animals is vast and diverse and there can be upwards of 1011 bacteria per gram of feces (Dowd et al., 2008; Hungate, 1966). Bacteria are the most abundant in the rumen at 1010 – 1011 cells/ml and over 200 species (Matthews et al., 2019) but they are not the only organism that makes up the microbial community, archaea, protozoa and fungi are also present. Ruminant animals are extremely effective
“upcyclers” as they consume a variety of low-quality plant matter and turn that into high quality protein (Stalcup, 2019). Even though they do not have the enzyme needed to break down complex polysaccharides found in plants the diverse microbial community in the rumen assists in effective fermentation (Henderson et al., 2015). Resulting in end products of volatile fatty acids (VFA) and microbial crude protein (MCP) which provide significant energy and protein sources for animal productivity (McCann et al., 2014;
Henderson et al., 2015). The importance of the gut microbiota in ruminants has been well established but there has been less focus on how common feed additives such as
DFM, and subtherapeutic antimicrobials could shift the rumen microbial community.
Probiotics have been shown to also help regulate the host immune response by sending nonspecific (innate) or specific (adaptive) responses against pathogens.
83 Texas Tech University, Andrea English, December 2019
However, there are mixed results on DFM and cytokine production, results are dependent on several factors such as strain, does and feeding time (Krehbiel et al.,
2002). Thus, the current study is the first to investigate the effect of Lactobacillus salivarius L28 on the concentration of cytokines in feedlot cattle fed for >140 days.
Thus, the objectives of this study were to determine if L. salivarius L28 fed as a DFM can be used as an effective alternative to subtherapeutic antimicrobials to mitigate the presence of AMR bacteria and to analyze microbiome and cytokine changes of feedlot cattle supplemented with L28, and/or subtherapeutic antimicrobials.
Material and Methods
Cattle, pen assignments, and treatments.
The feedlot finishing study was conducted from October 2016 to March 2017.
One hundred and forty-four crossbred steers were received at the Texas Tech
University Burnett Center for Beef Cattle Research and Instruction (Lubbock, TX).
All steers were obtained from a single source with an average body weight of 371 + 19 kg. All cattle were initially processed which included treatment for clostridial, viral and mycoplasma diseases, treatment for internal and external parasites, individual tag placements, and implantation of Synovex Plus (200 mg TBA + 28 mg estradiol benzoate (Zoetis, Florham Park, NJ).
Prior to study initiation, all steers were sorted into 12 blocks. Pens were randomly assigned to one of three dietary treatments with 48 steers per treatment (4 steers per pen). There was a total of 12 pens designated for each dietary treatment, with a total of 36 pens. Cattle were housed in adjoining partially slotted-floor concrete
84 Texas Tech University, Andrea English, December 2019 pens, no β-adrenergic agonist was fed. See Table 1 for dietary treatments. All treatments were included in a steam-flaked corn-based finishing diet fed once daily using a slick bunk management approach.
Cattle were weighed at 28-day intervals, alternating between pen and individual weights. Individual body weight (BW) was taken on days 0, 56, and final
BW was collected on days 140 and 158 for the 1st and 2nd slaughter groups, respectively. While pen weights were measured on days 28, 84 and 112. Steers were shipped when approximately 60% of steers within a block had enough external fat cover to grade United States Department of Agriculture (USDA) Choice.
Sample collection.
Fecal grabs were collected from the rectum of three to four animals per pen per treatment at day 0 of diet initiation and day 56. Each individual fecal collected was weighed (50 g ± 10 g) and made into one composite sample per pen (n=36/day) once in the microbiology laboratory at Texas Tech University. A fecal grab was collected from each individual animal on the day prior to harvest (>140, n=144) at 140 d (group
1, n=72) and 158 d (group 2, n=72) while restrained in processing chutes. Latex gloves and arm-length plastic palpation sleeves were worn during collections and transferred into individually labeled specimen cups. A new sleeve was used for each individual fecal collection.
Microbial detection.
Fecal samples were subject to bacterial isolation and detection of Escherichia coli O157, Salmonella spp., E. coli (generic non-type specific), and Enterococcus spp.
85 Texas Tech University, Andrea English, December 2019
Detection protocols for E. coli O157 and Salmonella spp. as described in the Materials and Methods section of Chapter 2.
Enterococcus spp. and E. coli detection.
A 10 g portion of fecal sample was weighed and transferred into sterile 24 oz
Whirl-pak™ filter bags and 90 ml of phosphate buffered saline (PBS; Thermo
Scientific; Rockford, IL) was added then stomached (model 400 stomacher, Seward,
Worthington, UK) at 230 RPM for 2 minutes to create a fecal slurry.
Recovery of Enterococcus was done following Fluckey et al., 2009 by streaking 0.1 ml of the previously described fecal slurry to Kenner Fecal (KF)
Streptococcus agar (Becton, Dickinson and Company Difco; Sparks, MD) supplemented with 1% triphenyltetrazolium chloride (TTC) solution as a color indicator and incubated for 24-48 h at 35°C. Three typical colonies (small, pink or red) from each plate were confirmed by growing in brain heart infusion (BHI; Becton,
Dickinson and Company Difco; Sparks, MD) broth and incubated for 24 h at 35°C.
After incubation, 1 ml of the overnight culture was transferred into BHI with 6.5%
NaCl, to determine the ability to withstand high salt concentrations, and incubated at
35°C for 72 h. An additional 1 ml was transferred to KF Streptococcus broth and incubated for 24 h at 45°C to assess growth at high elevated temperatures. After confirmation, isolates were grown in BHI for 24 h at 37°C and frozen in duplicate with 20% glycerol.
For detection of E. coli, a 0.1 ml of the fecal slurry was streaked for isolation in duplicate onto MacConkey (MAC; Becton, Dickinson and Company; Sparks, MD) agar and incubated for 18-24 h at 37°C. Following incubation 3 typical colonies (pink,
86 Texas Tech University, Andrea English, December 2019 well-defined borders) were re-streaked onto … for further isolation. A single isolated colony was grown in TSB and incubated for 24 h at 37°C and frozen in duplicate with
20% glycerol.
Antimicrobial susceptibility.
Previously confirmed and frozen colonies were streaked onto 5% sheep blood agar plates (BAP) and incubated for 18-24 h at 36+1°C and subjected to antibiotic susceptibility testing using the semi-automated broth microdilution system
(Sensititre™, Trek Diagnostic Systems, Inc., Thermo Fisher Scientific; Oakwood
Village, OH) to determine minimum inhibitory concentration of antimicrobials
(NARMS, 2016). Breakpoints for each antimicrobial tested were interpreted using the
Clinical and Laboratory Standards Institute (CLSI, 2013) when available or with the
National Antimicrobial Resistance Monitoring System (NARMS). The following quality control (QC) strains were used for testing Salmonella and E. coli: E. coli
ATCC 25922, Enterococcus faecalis ATCC 29212, Staphylococcus aureus ATCC
29213 and Pseudomonas aeruginosa ATCC 27853. Furthermore, the following QC strains were used for Enterococcus testing: E. faecalis ATCC 29212, E. coli ATCC
25922, and Staphylococcus aureus ATCC 29213.
For susceptibility testing of Enterococcus spp. (n=337) SensititreÔ gram positive MIC plates were utilized (CMV3AGPF; ThermoFisher Scientific; Oakwood
Village, OH). A 0.5 McFarland polymer turbidity standard was calibrated in a
SensititreÔ Nephelometer (ThermoFisher Scientific; Oakwood Village, OH) prior to measuring inoculated samples. With a sterile cotton swab, 1-4 colonies (more if needed) were selected from the freshly grown BAP plates and suspended in 5 ml of 87 Texas Tech University, Andrea English, December 2019 demineralized water and adjusted as needed to meet the equivalent of the 0.5
McFarland. Once the proper concentration was met, 10 µl of the suspension was added to 11 ml of adjusted Mueller-Hinton broth with TES (Thermo Fisher Scientific;
Oakwood Village, OH). Test tube caps were replaced with dosing heads and inserted to auto-inoculator. A total of 50 µl of sample was added to each well in the gram- positive MIC plate. After dispensing, plates were covered with clear plastic adhesive seal and incubated for 18 h at 36+1°C and Vancomycin was read at 24 h. Plates were read using the SensititreÔ OptiReadÔ automated fluorometric plate reading system
(ThermoFisher Scientific; Oakwood Village, OH).
Susceptibility testing of Salmonella (n=22), E. coli (n=358) and E. coli O157
(n=32) SensititreÔ Gram-negative MIC plates were utilized (CMV2AGNF;
ThermoFisher Scientific; Oakwood Village, OH). Analysis was performed as described in the previous paragraph
Table 4. Antimicrobial class, antimicrobial agent, abbreviations and minimum inhibitory concentrations (µg/ml) for Salmonella and E. coli. Susceptible Resistant Antimicrobial Class Antimicrobial Agent Abbreviations (µg / ml) (µg / ml) Macrolides Azithromycin AZITHR ≤16 ≥ 32 Aminopenicillins Ampicillin AMPICI ≤ 8 ≥ 32 β-Lactam/β- Lactamase Inhibitor Amoxicillin/Clavulanic Acid AMOCLA ≤ 8/4 ≥ 32/16 Combinations Ceftriaxone CEFTIF ≤ 1 ≥ 4 Cephems Cefoxitin CEFOXI ≤ 8 ≥ 32 Ceftiofur CEFTRI ≤ 2 ≥ 8 Phenicols Chloramphenicol CHLORA ≤ 8 ≥ 32 Ciprofloxacin CIPROF ≤ 0.06 ≥ 1 Quinolones Nalidixic Acid NALAC ≤ 16 ≥ 32 Folate Pathway Trimethoprim/sulfamethoxazole TRISUL ≤ 2/38 ≥ 4/76 Inhibitors Sulfisoxazole SULFIZ ≤ 256 ≥ 512 Gentamicin GENTAM ≤ 4 ≥ 16 Aminoglycosides Streptomycin STREPT ≤ 32 > 64 Tetracyclines Tetracycline TETRA ≤ 4 ≥ 16 88 Texas Tech University, Andrea English, December 2019
Table 5. Antimicrobial class, antimicrobial agent, abbreviations and minimum inhibitory concentrations (µg/ml) for Enterococcus. Susceptible Resistant Antimicrobial Class Antimicrobial Agent Abbreviations (µg / ml) (µg / ml) Phenicols Chloramphenicol CHLORA ≤ 8 ≥32 Quinolones Ciprofloxacin CIPROF ≤ 1 ≥4 Lipopeptides Daptomycin1 DAPTOM ≤ 4 n/a Gentamicin GENTAM ≤ 500 >500 Aminoglycosides Kanamycin KANAMY ≤ 512 ≥1024 Lincosamide Lincomycin LINCOM ≤ 2 ≥8 Oxazolidinones Linezolid LINEZO ≤ 2 ≥8 Nitrofurantions Nitrofurantoin NITRO ≤ 32 ≥128 β-Lactam Penicillin PENICI ≤ 8 ≥16 Streptogramins Quinupristin/dalfopristin SYNERC ≤ 1 ≥4 Tetracyclines Tetracycline TETRA ≤ 4 ≥16 Glycylcycline Tigecycline1 TIGECY ≤ 0.25 n/a Tylosin tartrate TYLO ≤ 8 ≥32 Macrolides Erythromycin ERYTH ≤ 0.5 ≥8 Glycopeptides Vancomycin VANCOM ≤ 4 ≥32 1No current CLSI resistant breakpoint for the bacteria/antimicrobial combination
Whole genome sequencing.
E. coli O157 and Salmonella isolates were further analyzed by whole genome sequencing. Previously frozen, Salmonella (n=22) and E. coli O157 (n=32) isolates were grown overnight (18-24 hours), after incubation (37°C) 1.5 ml of the overnight cultures were used for pure genomic DNA (gDNA) extraction using the GenElute
Bacterial Genomic DNA kit (Sigma-Aldrich; St. Louis, MO) following manufacturer’s recommendations. gDNA was quantified using a NanoDrop 2000 Spectrophotometer
(Thermo Scientific; Oakwood Village, OH) and sent to the Texas Department of State
Health Services for WGS. Raw data was further investigated using the Center for
Genomic Epidemiology website for the identification of acquired antibiotic resistance genes (Zankari et al., 2012).
89 Texas Tech University, Andrea English, December 2019
Sample collection and microbial analysis.
For microbiome analysis, 100 g of fecal sample was collected rectally from 12 previously identified animals per treatment on days 0 and 140 (n=34; control (12), monpro (12), base (10)) for each day. Samples were stored at -80°C until further analysis. DNA was extracted from fecal samples using the QIAamp DNA Mini Stool
Kit (QIAGEN; USA) following manufacturer’s recommendations. DNA from each sample was used to prepare libraries using the Illumina 16S-metagenomics library preparation protocol by the PCR amplification of the V3-V4 region of the 16S rRNA genes. An indexed library was prepared for each sample using the Nextera XT index kit v2, PCR amplicons were then purified using AMPure XP beads. Cleanup products were quantified in triplicate using a Qubit 2.0 fluorometer, pooled at equal concentrations, and finally diluted to a final concentration of 4.5 pM. Samples were sequenced using a
MiSeq Reagent kit v3 (600 cycle) on an Illumina MiSeq sequencer.
Blood collection and cytokine analysis.
Jugular blood samples, approximately 10-15 ml, were collected (n=36) and sera were analyzed at the end of the feeding trial prior to harvest. One steer per pen was randomly designated for collection (n=1/pen; 12 steers/treatment). Whole blood was collected into 15 ml non-additive evacuated tubes and allowed to clot for 24 h at
4°C (Parr et al., 2014) and subsequently centrifuged at 1250 × g, 4°C in order to harvest sera. Serum was transferred to 1.5 ml microcentrifuge tubes and stored at ≤ -
20°C. Cytokine analysis was performed from the serum collected using a
MILLIPLEX® MAP Custom Bovine 15-Plex Magnetic Bead Panel (Millipore Sigma;
Burlington, MA) 96-well magnetic panel with specific targeted cytokines: IL-1ɑ, IL-
90 Texas Tech University, Andrea English, December 2019
1ß, IL-1RA, IL-6, IL-17A, IL-2, IL-4, IFN-g, IL-8, IP-10, CCL2, CCL3, IL-10, CCL4 and TNF-ɑ. The magnetic bead panel was used according to manufacturer instructions. Briefly, assay buffer was added to each well plate, sealed and mixed on a shaker for 10 min at room temperature (20-25°C). Assay buffer was removed and 25
µl of the controls, and assay buffer was added to appropriate sample wells. Matrix solution (25 µl) was added to the background, standards, and control wells and 25 µl of sample into the designated wells. Plates were sealed and allowed to incubate for
16-18 hour at 4°C. All contents were removed and washed then 25 µl of detection antibodies were added and allowed to agitate for 1 hour at room temperature.
Streptavidin-Phycoerythrin (25 µl) was added to each well with the antibodies and allowed to shake for 30 minutes at room temperature. Contents were removed washed and 150 µl of sheath fluid was added to each well and allowed to shake for 5 minutes at room temperature. Plates were run on a Luminex 200™ Instrument with x-map technology (Luminex Corporation; USA).
Statistical Analysis
Data generated from the SensititreÔ OptiReadÔ automated fluorometric plate reading system was analyzed to determine resistance patterns. Isolates were classified as pansusceptible (susceptible to all antimicrobials tested), susceptible or resistant.
Isolates with intermediate resistance were classified as susceptible for this document.
Data was then imported to SAS (version 9.4; SAS Institute Inc., Cary, N.C.) for statistical analysis. The PROC GML procedure in SAS was used. For overall resistance effects of treatment, day and treatment × day interaction were considered.
Analysis was also done for antibiotics of interest considering treatment effect. 91 Texas Tech University, Andrea English, December 2019
Differences in least squares means were determined for each model. Significance for analysis was set at p < 0.10. Descriptive statistics were generated by MEANS procedures in SAS for each cytokine and chemokine. The GLM procedures in SAS was also used with treatment as the only effect. Any values below the MinDC or above the MaxDC were removed prior to analysis (Table 14).
Raw Fastq files generated from 16-S metagenomics analysis were demultiplexed, filtered and processed using R (The R Foundation). Differential abundance was determined using the DeSeq package in R. Significance was determined using the Wilcoxon signed-rank test, defined as p<0.05. ß diversity was determined using the weighted UniFrac distance with a permutational multivariate analysis of variance (PERMANOVA) to determined differences. The effects of day and treatment were considered, significance defined as p<0.05. For ɑ diversity the richness and Shannon diversity metrics were used.
Results and Discussion
Generic E. coli
Generic E. coli was isolated from 100% of the fecal samples on day 0 (n=36), and 56 (n=36). Three isolates were collected from each fecal sample thus a total of
108 isolates were used for susceptibility testing on days 0 and 56. On day 140 generic
E. coli was isolated from 142 out of 144 fecal samples. All isolates were tested against
14 antimicrobial drugs to determine the AMR (Table 4). From the total 358 generic E. coli isolates analyzed, 66% were pansusceptible to all 14 antimicrobials tested, 13.7% were resistant to 1 antimicrobial, 12.3% resistant to 2 antimicrobials, and 8.1% of the
92 Texas Tech University, Andrea English, December 2019 isolates were resistant to 3 or more antimicrobial drugs. Generic E. coli is an important and useful indicator organism to monitor for AMR and AMR genes as they could be transferred to pathogenic organisms such as Salmonella (NARMS, 2015). They are also abundant in the gastrointestinal tract and commonly found in the environment, thus they can easily be transferred from animals to human, human to human, or environment to human.
Resistance to one antimicrobial was 39.8%, 44.2% and 18.3% for treatments base, control and monpro, respectively (Figure 4). With the monpro treatment group having significantly less (p = 0.001) AMR generic E. coli. At each individual day analyzed (0, 56, and 140), the monpro treatment had significantly less (p = 0.001) generic E. coli resistant to at least one antimicrobial. These observations suggest that the supplementation of cattle diets with L28 could decrease the incidence of AMR generic E. coli.
Isolates from the monpro treatment group had the greatest percentage of susceptible isolates on days 0, 56 and 140. This group had also the less number of isolates with resistance to 3 or more antimicrobials on all three days. On day 0 the control treatment had the highest percentage of isolates with resistance to one antimicrobial at 30.6% compared to 8.3% and 2.8% in the base and monpro treatments, respectively (Figure 6). The base treatment on day 0 had 30.6% of isolates resistant to two antimicrobials. Also, the base and control groups had 8.3% of isolates resistant to 3 or more antimicrobials. On day 56 the control treatment had the most isolates (30.6%) resistant to at least one antimicrobial, while the monpro and base groups had 16.7% and 5.6% of isolates with resistance to one antimicrobial,
93 Texas Tech University, Andrea English, December 2019 respectively. The base group had the most isolates (22.2%) with resistance to 3 or more antimicrobials on day 56. For day 140, the base treatment had 6.5%, 2.2% and
13% of isolates with resistance to 1, 2, and greater than 3 antimicrobials, respectively.
The control treatment had 16.7%, 2.1% and 4.2% of isolates with resistance to 1, 2, and greater than 3 antimicrobials, respectively (Figure 6). Similar rates of resistance were recorded by NARMS (2015) in beef cattle ceca samples with 41% () of the isolates having resistance to at least one antimicrobial. Multidrug resistance was found in 7% of isolates from retail ground beef samples, and 11% in isolates from dairy cattle (NARMS, 2015).
The percent of multidrug resistant generic E. coli was further investigated as some isolates had resistance to up to 8 antimicrobials tested (Table 6). The control treatment group on day 0 and 56 had 8.3% of isolates with resistance to 8 antimicrobials. On days 56 and 140, 2.8% and 2.2% isolates from the base treatment had resistance to 4 antimicrobials, respectively.
Generic E. coli isolates in this study were most commonly resistant to streptomycin and tetracycline (Table 7). The base treatment had significantly greater
(p < 0.1) number of isolates (n=39) with resistance to streptomycin while the control and monpro group had 20 and 12 isolates respectively, with resistance to streptomycin. The number of tetracycline resistant isolates included 46, 44, and 21 for base, control and monpro treatments, with the base treatment having a significantly higher (p < 0.1) number of isolates with resistance. Base and control treatments had 16 and 15 isolates, respectively with resistance to chloramphenicol while the monpro treatment only had 1 isolate with resistance. The control group had 6 isolates with
94 Texas Tech University, Andrea English, December 2019 resistance to ampicillin, amoxicillin/clavulanic acid, ceftriaxone, cefoxitin, nalidixic acid and ceftiofur each. Fluckey et al. (2006) similarly collected fecal samples from feedlot cattle and studied the AMR of generic E. coli. From the 267 generic E. coli they found the most prevalent resistance was to sulfamethoxazole (79.03%), trimethoprim-sulfamethoxazole (29.96%), tetracycline (13.48%), cephalothin (5.62%), streptomycin (5.62%), kanamycin (4.87%), and ampicillin (1.12%). In the current study the highest rates of resistance for generic E. coli were to streptomycin and tetracycline, with no detected resistance to sulfamethoxazole. Benedict et al. (2015) found similar results in feedlot cattle as the generic E. coli was most commonly resistant to tetracycline, streptomycin and sulfisoxazole.
Figure 7 shows the percentage of generic E. coli isolates with resistance to specific antimicrobial classes, the most common resistance was to tetracyclines, aminoglycosides and phenicols (specifically for control and base treatments). The control treatment group had 44%, 20%, 18%, and 15% of the generic E. coli isolates with resistance to tetracyclines, aminoglycosides, cephems, and phenicols, respectively and 6% to ß-lactams and aminopenicillins. Base treatment isolates had
46%, 39%, 39% and 16% resistance to tetracycline, aminoglycosides, cephens, and phenicols, along with only 1% to cephens and quinolones and 2% to aminopenicillins.
The monpro treatment with resistance to the fewest classes including tetracyclines with 21% resistant isolates, 12% resistant to aminoglycosides, 1% to phenicols, and
2% of isolates resistant to aminopenicillins. The tetracycline class of antimicrobials is classified as highly important by the WHO, and aminoglycosides are of critical importance. Resistance to these specific antimicrobials was reduced in cattle
95 Texas Tech University, Andrea English, December 2019 supplemented with L28; however, cattle in the base group which were supplemented with no feed additive had similar resistance patterns to cattle supplemented with subtherapeutic antibiotics.
The most frequent resistance patterns for the generic E. coli were also determined on days 0, 56, and 140 (Table 8). The most common resistance pattern on day 0 was to STREPT-TETRA with 15.7% of isolates having this resistance pattern, followed by just TETRA at 11.1%. For day 56, the most frequent pattern was also
STREPT-TETRA at 16.7%, followed by TETRA at 13%, similar to day 0. However, on day 140 isolates had the most resistance to TETRA at 8.5%, followed by 4.2% of isolates with the AMR pattern CHLORA-STREPT-TETRA.
Overall, the monpro treatment which consisted of cattle supplemented with
L28 had less overall generic E. coli with resistance compared to the other treatment groups not supplemented with L28 over the entire feeding trial. Thus, it appears that supplementing cattle diets with L28 could be effective at mitigating the AMR of gram- negative organisms like E. coli.
Enterococcus
A total of three isolates from each fecal sample were selected when possible for susceptibly testing. Thus, 102 Enterococcus isolates were recovered, from a possible 108, on day 0 and 107 isolates on day 56. On day 140 Enterococcus was isolated from 88.9% (128/144) of the fecal samples. This observation is not surprising as bacteria from the genus Enterococcus are common inhabitants of the gastrointestinal tract and are ubiquitous in the environment (Fisher and Phillips, 2009).
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All Enterococcus isolates were tested against 15 antimicrobial drugs to determine the
AMR profile (Table 5). From all 337 Enterococcus isolates tested 40 isolates were pansusceptible to all antimicrobials tested. Twenty five percent were resistant to one drug, 19.3% were resistant to 2 drugs while 44.2% were resistant to 3 or more antimicrobials.
Overall resistance for each treatment base, control and monpro was 87.8%,
93%, and 83.3%, respectively (Figure 8). Isolates from the monpro treatment had significantly (p = 0.028) less antimicrobial resistance. Antimicrobial resistance over time increased the longer cattle were on feed. On day 0, control treatment (77.1%) had significantly (p = 0.0001) higher rates of resistance compared to the monpro treatment
(56.3%) (Figure 9). However, on days 56 and 140 there were no significant differences between days or across treatments. Numerically, isolates from the monpro treatment group had lower rates of resistance compared to isolates from the other treatments analyzed. On day 56, base, control and monpro had 97.1%, 100%, and
94.1% of isolates showing antimicrobial resistance. On day 140, 97.8% of isolates in the base group were resistant to at least one antimicrobial, while 100% in the control group had resistance, and 95% of isolates from the monpro treatment were resistant to at least one antimicrobial (Figure 9).
Figure 10 shows the AMR of the Enterococcus isolates across treatments and over time. The percentage of susceptible Enterococcus isolates decreased after day 0 for each treatment group. While the resistance to 3 or more antimicrobials increased for each treatment group to 62.2%, 72.5% and 62.8% for base, monpro and control treatments, respectively. On day 0 of the feeding trial 5.7% of isolates in the control
97 Texas Tech University, Andrea English, December 2019 group had resistance to 5 antimicrobials, while the base, control and monpro groups had 11.4%, 2.9%, and 6.3% of isolates resistant to 4 drugs, respectively. On day 56, control and monpro had 19.4% and 5.6% of isolates with resistance to 4 antimicrobials. However, on day 140, the base treatment had 4.4%. 2.2%, 2.2%,
20.0% and 2.2% of isolates with resistance to 4, 5, 7, 8, and 9 drugs, respectively.
Control treatments had 4.7%, 4.7% and 18.6% of isolates with resistance to 4, 5, and 8 drugs. While the monpro treatment had 7.5%, 2.5%, 20.0%, and 2.5% of isolates with resistance to 4, 5, 8 and 9 antimicrobials, respectively.
Enterococcus isolates were most commonly resistant to lincomycin, tetracycline, tylosin tartrate, daptomycin and erythromycin (Table 10). For lincomycin
87 isolates in the base group were resistant, 102 isolates in the control group and 85 in the monpro treatment. Fifty-one isolates in the base group, 65 in control, and 47 in the monpro group were resistant to tetracycline. For tylosin tartrate 44 isolates in the base group had resistance, 50 in the control group and 41 in the monpro treatment were resistant. Twenty-four isolates in the base group had resistance to erythromycin, 28 isolates form the control group and 23 from the monpro treatment group. Twenty-three isolates in the base group were resistant to daptomycin while 22 and 20 isolates from the control and monpro had resistance, respectively.
Jackson et al. (2010) found similar results in enterococci isolates from dairy cattle where the most commonly found resistance was to lincomycin (92.3%), flavomycin (71.9%) and tetracycline (24.5%). They also found 25 different multidrug resistance patterns, and 1 isolate with resistance to 7 antimicrobials (erythromycin, kanamycin, lincomycin, streptomycin, tetracycline, tylosin and
98 Texas Tech University, Andrea English, December 2019 quinupristin/dalfopristin) (Jackson et al., 2010). In the current study at the end of the feeding trial 2.2% and 2.5% of isolates from the monpro had resistance to 9 antimicrobials. Fluckey et al. (2008) was determined the AMR of Enterococcus isolates collected from feedlot cattle while in the feedlot and at the abattoir. They determined that all 279 isolates were resistant to at least one antimicrobial, with the most commonly resistance found to chloramphenicol (100%), followed by flavomycin
(90.3%), lincomycin (87.8%), tylosin (78.5%), erythromycin (76.3%), tetracycline
(58.9%), synercid or quinupristin-dalfopristin (47.7%), bacitracin (17.9%), streptomycin (9%), ciprofloxacin (1.4%), linezolid (0.7%), and finally salinomycin
(0.4%). Although Fluckey et al. (2008) found predominantly chloramphenicol resistance which was not found in this study, the lincosamides, macrolide and tetracycline resistance was similar to results found in the current study. Resistance to both lincosamides and macrolides is linked as both antimicrobials target the 50s ribosomal subunit thus co-resistance could occur (Kak and Chow, 2002; Leclercq,
2002). The sub-therapeutic supplementation of tylosin in the control cattle could also be promoting the increase in macrolide resistance. Although the percent of resistant
Enterococcus was already at 65.7%, 77.1%, and 56.3% for base, control and monpro treatments respectively on day 0 with no prior supplementation with tylosin or no sub- therapeutic antimicrobial.
The most common AMR patterns for the Enterococcus isolates are listed in
Table 11. For day 0 the most common pattern of drug resistance was to LINCOM-
TETRA-TYLO with 20.6% of isolates resistant to these drugs. Isolates from day 56, were most commonly resistant to LINCOM (40.7), and LINCOM-TETRA (20.4%).
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The diversity in AMR patterns increased on day 140, with the most common pattern of resistance being to LINCOM-TETRA-TYLO (28.9%), with 19.5% of isolates resistant to DAPTOM-ERYTH-LINCOM-LINEZO-PENICI-SYNERC-TYLO-VANCOM and 14.1% of isolates resistance to just LINCOM.
There was a high percentage of isolates, regardless of treatment, with resistance and multiple drugs resistance during the entire feeding trial. However, the appearance of vancomycin resistance at the final days on feed is of a concern.
Vancomycin resistant Enterococcus was detected in 11 isolates from the base group, 8 from control cattle and 9 from the monpro treatment. Vancomycin resistant
Enterococcus is a serious threat by the CDC as vancomycin is a drug of last resort for nosocomial infections and is estimated to cause 1,300 deaths each year in the U. S.
(CDC, 2013). There is no apparent treatment effect promoting the increase of vancomycin resistant Enterococcus. Further research is needed to investigate the AMR genes that could be present in the Enterococcus isolates collected during this study.
Enterococcus may not be a foodborne pathogen of high concern but should be monitored as they are ubiquitous in the environment and can harbor high levels of
AMR and can easy transfer resistance genes to other pathogens (Fisher and Phillips,
2009) which could be of more direct concern for foodborne illness.
E. coli O157
Isolates were tested against 14 antimicrobials (Table 4) and 32 confirmed E. coli O157 isolates, were susceptible to 9 antimicrobials. The only phenotypic resistance detected in E. coli O157 isolates were isolated from the monpro treatment
100 Texas Tech University, Andrea English, December 2019 group on day 56, where ampicillin, ceftiofur, chloramphenicol, streptomycin, and tetracycline each had one isolate with resistance. Table 13 shows the resistance genes detected. The most common resistant gene detected across all treatments and days was mdf(A) which encode resistance for macrolide antibiotics. Although, no E. coli O157 was isolated from the monpro treatment group on day 140, the most common AMR determinants detected confer resistance to the aminoglycoside class of antimicrobials.
The gene aph(3’)-lb was found in one isolate from the base treatment on day 0, and 1 isolate from treatments control and monpro on day 56. Aac(2’)IIa and aac(6’)-Iaa was found in 1 isolate from the control group on day 0. One isolate on day 140 in the control group was positive for the aadAIb gene. Aph(6’)-Id was found in one isolate from the base group on day 0, and on day 56 from the control and monpro treatment groups. While the fosA7 gene was only present in 1 isolate from the control group on day 0. floR which confers resistance to phenicols were present in the base group on day 0, and control and monpro groups on day 56. There was only one ß-lactam gene present (blaOXA-2) in the control group on day 140. Sul2 and tetA genes were both found in the base group on day 0, control and monpro treatments on day 56, and control group on day 140.
Yang et al. (2017) studied AMR genes found in multi-drug resistance bacteria isolated from livestock manure and from 33 isolates 47 genes conferred resistance to 8 antimicrobial classes (tetracycline, macrolides, ß-lactams, sulfonamides, streptogramins, aminoglycosides, quinolones and vancomycin). Similar to the current study aminoglycosides genes (aadA1, aacA/aphD, aacC2, aphA3, and aac(6’)-Ib) were the most common and diverse. A study by Chambers et al. (2015) studied the
101 Texas Tech University, Andrea English, December 2019 effect of AMR genes isolated from bacteria in fecal samples from dairy cattle treated with third generation cephalosporin compared to not treated cattle. Overwhelmingly, tetracycline genes (>85%) were the most frequently detected in the fecal samples with no treatments effect. Furthermore, Iweriebor et al. (2015) isolated E. coli O157 from dairy cattle in South Africa and studied the AMR and AMR genes. From 95 isolates chloramphenicol (89.5%), ampicillin (94.74%), tetracycline (96.84%), oxytetracycline
(94.74%), cefuroxime (82%), amoxicillin/clavulanate (84.2%), and trimethoprim/sulfamethazole (84.2%) were the drugs with the most resistance. The most commonly identified genes included blaampC (90%), blaCMY (70%), blaCTX-M
(65%), and blaTEM (27%). Additional genes present included tetA in 70% of isolates and strA in 80% of samples. Like the current study AMR genes were present in all samples, however mdf(A) was the most frequently isolated AMR genes. Interestingly, in a study from Thomas et al. (2017) found that regardless of administration of sub- therapeutic antimicrobials or not there was wide distribution of aminoglycoside and tetracycline AMR genes found in feedlot cattle. Thus, suggesting that resistance genes are universal in the microbial population and not necessarily induced by sub- therapeutic antimicrobial supplementation (Thomas et al., 2017).
Salmonella Twenty-two Salmonella isolates phenotypic and genotypic AMR patterns were investigated for each treatment and days 0, 56, and 140. The most common
Salmonella serovar identified were Salmonella enterica serovar Anatum (72.7%), followed by S. Montevideo (22.7%) and S. Infantis (4.5%). Each isolate was subject to susceptibility testing against 14 antimicrobials see Table 4. The only resistant
102 Texas Tech University, Andrea English, December 2019 phenotype detected was to tetracycline. On day 0 and 140, 1 isolate from the base group and 2 isolates from the control group had resistance to tetracycline. No tetracycline resistance was found on day 56. These data are similar to the NAHMS feedlot study in 2011 where the most commonly reported S. serotypes were Anatum,
Montevideo and Kentucky in feedlot cattle (APHIS, 2011). Similarly, to the current study they also found that when Salmonella isolates were resistant it was only to 1 antimicrobial, and most commonly resistant to tetracycline. However, in a study by
Khaitsa et al. (2007) only Salmonella serotype Typhimurium serovar Copenhagen was present and all Salmonella isolates were resistant to spectinomycin, sulfathiazole, tiamulin, florfenicol, ampicillin, penicillin, chlortetracycline, oxytetracycline, and clindamycin. Furthermore, Fluckey et al. (2007) found that 97% of the Salmonella isolates from feedlot cattle were resistant to at least one antimicrobial drug and the majority were resistant to sulfamethoxazole, followed by streptomycin. In the 2014
FSIS, NARMS cecal sampling program reported the most commonly isolated serotypes from cattle cecal to be S. Newport and S. Typhimurium with 50% and 71% of isolates with resistance, respectively.
Antimicrobial resistance is a significant public health concern and the capabilities of bacteria to acquire and spread AMR genes warrants continued monitoring in different environments to better understand the spread and dissemination of the AMR genes in different bacteria. Thus, the most frequently identified acquired resistance gene was aac(6’)-Iaa and was identified in all
Salmonella isolates Table 13. aac(6’) enzymes can inactivate aminoglycoside antibiotics by acetylating at the 6’ position (Salipante and Hall, 2003). There are two
103 Texas Tech University, Andrea English, December 2019 families of aac(6’) enzymes (aac(6’)-I and aac(6’)-II). Aac(6’)-I confer resistance to amikacin while aac(6’)-II typically confers resistance to gentamicin (Salipante and
Hall, 2003). In the current study aac(6’)-Iaa was commonly present however; no phenotypic resistance was detected for aminoglycosides. Two tetracycline genes were identified, tetB was found in 1 isolate on days 0 and 140 for the base treatment, and 2 isolates in the control treatment on days 0 and 140. TetJ was found in 1 isolate on day
56 in the control group. TetB and tetJ both are efflux genes that expel tetracycline out of the cell. TetB is much more frequently isolated however, less commonly identified is tetJ. Interestingly, tetB was the only present gene that had matching phenotypic resistance to the drug they confer resistance too. Another gene present in 2 isolates was cat identified in the monpro group on day 0, and control treatment on day 56. Cat genes commonly confer resistance to chloramphenicol (Davies, 1994). Isolates can have AMR genes present and express or not express phenotypic resistance either way they are important to monitor especially for genes that can induce resistance to antimicrobials of human importance, for example tetracyclines and chloramphenicol both are considered highly important by WHO (Collignon et al., 2016).
Microbiome
To assess changes in the microbiome fecal collections were done at the beginning of the feeding trail and prior to harvest. On both day 0 and 140, base, control and monpro treatments had 10, 12 and 12 samples analyzed. In Figure 12 the number of unique sequences are identified for each treatment. During analysis there was one outlier removed with fewer than 1,000 sequences from the control group.
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All phyla with a relative abundance greater than 2% are presented in Figure 13.
Firmicutes and Bacteroidetes were the most dominate in all treatments on days 0 and
140. Also, identified were Proteobacteria, Verrucomicrobia, Spirochaetes and
Tenericutes. Figure 14 shows the changes in relative abundance of the bacterial phyla on days 0 and 140 for each treatment. The only significant changes detected were
Bacteroidetes which increased over time (p=0.0059) in the base group.
On day 0 the control group had significantly greater Shannon diversity
(p=0.044) (Figure 16) compared to base and monpro. The Shannon diversity measures the microbial diversity within a local community (Thomas et al., 2017). Figure 17, shows the ß diversity comparing days 0 (p=0.111) and 140 (P=0.265) no significant differences were detected for day nor across treatment (base p=0.118; control p=0.452; monpro p=0.233). ß diversity measures the diversity between two or more local groupings, thus, higher ß diversity means a larger difference in species identified between treatment groups (Thomas et al., 2017). Abundant bacterial taxa changes from day 0 to 140 for each treatment is shown in Figure 18. The base treatment had the greatest shift from day 0 to 140 in the differential abundance. All taxa from Figure
18 are individually represented in Figure 19 comparing day 0 to 140 for each. Each of the plots shows one bacterial family with genera represented by different colors. A comparison was done between control and monpro treatments on day 140 (Figure 20).
The larger point was the only statistical significance (p<0.05). Twenty taxa were identified with 13 in the control group and 7 associated with monpro which were differentially abundant at day 140. Furthermore, in Figure 20 (B) individual plots were made for each of the statistically significant bacterial taxa from Figure 20 (A). Each of
105 Texas Tech University, Andrea English, December 2019 the plots show bacterial families including; Anaeroplasmataceae, Clostridiaceae_1,
Lachnospiraceae, Porphyromonadaceae, Prevotellaceae, Ruminococcaceae and
Verrucomicrobiaceae with genera coded by different colors.
Multiple studies have identified that the predominate phyla in the rumen of feedlot cattle were Firmicutes and Bacteroidetes (Kim and Wells, 2016; McCann et al., 2014; Clemmons et al., 2018; Thomas et al., 2017; Shin et al., 2015). One of the most significant contributing factors that effects the rumen microbiome is diet. A study conducted by Thomas et al. (2017) investigated the gut microbiome of feedlot steers feed monensin and tylosin. Similar to the current study supplementation with antibiotics had no major changes at the phylum level in the rumen microbiome. The present study also had cattle supplemented with a DFM L28, which is the first to investigate the usage of that specific DFM on feedlot cattle and the effects on the microbiome. However, there were no significant changes in the microbiome of cattle supplemented with the DFM. It is surprising that more changes were not identified specifically in the control cattle that were supplemented with tylosin and monensin. As both antimicrobials have selective pressure on gram-positive bacteria (Thomas et al.,
2017). Further investigation into the resistome could be warranted to determine if cattle in the monpro treatment would have fewer AMR determinants present compared to the cattle supplemented with antimicrobials.
Cytokine and Chemokines
Jugular blood samples were collected (n=36) and sera were analyzed at the end of the feeding trial. One steer per pen was randomly designated for collection
(n=1/pen; 12 steers/treatment). Cytokine analysis was performed from the serum
106 Texas Tech University, Andrea English, December 2019 collected using a MILLIPLEX® MAP Custom Bovine 15-Plex Magnetic Bead Panel
(Millipore Sigma; Burlington, MA) 96-well magnetic panel with specific targeted cytokines: IL-1ɑ, IL-1ß, IL-1RA, IL-6, IL-17A, IL-2, IL-4, IFN-g, IL-8, IP-10, CCL2,
CCL3, IL-10, CCL4 and TNF-ɑ.
No significant differences in cytokine or chemokine production were detected regardless of treatment prior to harvest. Further work is needed to investigate the changes in cytokine and chemokine production over time. Allowing a better understanding of potential changes that occur from cattle that have just entered the feedlot to cattle prior to harvest.
Conclusion
Generic E. coli and Enterococcus were both isolated in this study similarly to
NARMS as indicator organisms. Both organisms are frequent inhabitants of the gastrointestinal tract of ruminants and humans. Thus, they can provide valuable insight into the AMR patterns in gram-positive and gram-negative organisms. E. coli and
Enterococcus can also provide perspective into the spread and dissemination of AMR organisms isolated from cattle that have been supplemented with different feed additives. Specifically, these data show promising results in controlling the emergence and potential spread of AMR generic E. coli when cattle are supplemented with the
DFM Lactobacillus salivarius L28. Overall resistance to at least one antimicrobial was measured for each treatment and the monpro treatment had significantly less (p=
0.001) resistance. Generic E. coli was most commonly resistance to tetracycline which is classified as highly important by the WHO and streptomycin which is critically
107 Texas Tech University, Andrea English, December 2019 important for human medicine. Thus, it is important to continue to monitor the AMR patterns of E. coli. Literature has shown that organisms such as E. coli and
Enterococcus can pass their AMR genes to other organisms that could be more of a direct foodborne illness concern for humans.
However, for Enterococcus the same results were not identified. Resistance to at least one antimicrobial was high for the entire feeding study even on day 0.
Enterococcus resistance was found in 65.7%, 77.1%, and 56.3% of isolates from base, control and monpro treatments, respectively. The percent of resistant Enterococcus isolates increased on days 56 and 140 to greater than 90% of isolates having resistance to at least one antimicrobial. Furthermore, all the antimicrobials that Enterococcus were resistant to are considered important, highly important, or critically important for human medicine. The feed additive cattle were supplemented with, either L28 or subtherapeutic antimicrobial, had no effect on the resistance of Enterococcus during the entire feeding trial.
Phenotypic resistance is a reliable method to determine the AMR of bacteria; however, with the decreased cost and more readily available access WGS is an incredibly useful tool to utilize. WGS can provide more information and potentially better understanding on how AMR occurs and spreads from the environment to animal, animal to human, or the environment to human. Thus, resistance genes were also investigated in the confirmed E. coli O157 and Salmonella isolates. The most frequent resistant gene detected in E. coli O157 was mdf(A) which confers resistance to macrolides. In Salmonella isolates aac(6’)-Iaa was the most frequent gene detected
108 Texas Tech University, Andrea English, December 2019 which confers resistance to aminoglycosides. Also, the presence of tetB in the
Salmonella isolates matched with the phenotypic resistance to tetracycline.
Bacteroidetes and Firmicutes were two of the most dominate phyla in the gut microbial community of animals in this study regardless of diet or feed supplementation. There was no diet effect on the overall abundance in the microbial community. Finally, diet had no effect on cytokine or chemokine levels.
This study certainly demonstrates that AMR bacteria are present in the feedlot continuum. However, L28 can be used as an effective pre-harvest intervention to reduce the spread and emergence of resistance found in gram-negative organisms.
Continued investigation into effective pre-harvest interventions is important for beef producers and consumers. Inclusion of L28 can potentially provide an effective solution as tylosin can no longer be utilized in feedlot cattle diets for the purpose of growth promotion. Further work is warranted to better understand the mechanism of action that is occurring for L28 to mitigate the AMR in generic E. coli isolates. While identification of commensal organisms shed by feedlot cattle, such as generic E. coli and Enterococcus, is traditionally used to provide information about phenotypic resistance patterns. There would be valuable information gained from further characterization of these isolates. Future work should consider additional characterization such as WGS to better understand the spread and dissemination of
AMR determinants.
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100 90 80 70 a 60 a 50 44.2 39.8 40 b 30 18.3 Percent ofisolates AMR 20 10 0 Base Control Monpro
Cattle Treatments
Figure 4. Total percent of generic E. coli isolates with resistance to one or more antimicrobials tested over the entire feeding trial. Columns with different letters represent significant differences (P < 0.10).
Base Control Monpro 100 90 80 a 70 a a 60 a,b 50 b,c 40 30 d,c d,c 20 d d Percent ofisolates AMR 10 0 0 56 140 Days on Feed
Figure 5. Percent of generic E. coli isolates resistant to one or more antimicrobials tested over time. Columns with different letters represent significant differences (P < 0.10).
110 Texas Tech University, Andrea English, December 2019
100 0 1 2 >3 90 80 70 60 50 40 30 20 10
Percent of and isoaltes MDR with AMR 0 Base Monpro Control Base Monpro Control Base Monpro Control 0 56 140 0 52.8 88.9 44.4 44.4 69.4 38.9 78.3 87.5 77.1 1 8.3 2.8 30.6 5.6 16.7 30.6 6.5 8.3 16.7 2 30.6 8.3 16.7 27.8 11.1 19.4 2.2 2.1 2.1 >3 8.3 0.0 8.3 22.2 5.6 11.1 13.0 2.1 4.2
Figure 6. Percent of generic E. coli isolates with resistance to up to 3 antimicrobials collected during the feeding trial.
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Table 6. Distribution of generic E. coli AMR frequency considering percentage within each treatment, collection and number of antimicrobials with resistance. Days on Treatment n 0 1 2 3 4 5 6 7 8 Feed Base 36 52.8 8.3 30.6 8.3 0 0 0 0 0 0 Control 36 44.4 30.6 16.7 0 0 0 0 0 8.3 Monpro 36 88.9 2.8 8.3 0 0 0 0 0 0
Base 36 44.4 5.6 27.8 19.4 2.8 0 0 0 0 56 Control 36 38.9 30.6 19.4 2.8 0 0 0 0 8.3 Monpro 36 66.7 16.7 11.1 5.6 0 0 0 0 0
Base 46 78.3 6.5 2.2 10.9 2.2 0 0 0 0 140 Control 48 77.1 16.7 2.1 4.2 0 0 0 0 0 Monpro 48 89.6 8.3 2.1 2.1 0 0 0 0 0
Table 7. Minimum inhibitory concentrations and number of generic E. coli isolates with resistance to specific antimicrobials tested over the entire feeding trial. Cattle Treatments Resistant Antimicrobial Drug WHO Ranking1 Base2 Control3 Monpro3 (µg / ml) Ampicillin ≥ 32 Critically important 2b 6a 2b Amoxicillin/Clavulanic Acid ≥ 32/16 Critically important 0b 6a 0b Ceftriaxone ≥ 4 Critically important 0b 6a 0b Chloramphenicol ≥ 32 Highly important 16a 15a 1b Cefoxitin ≥ 32 Highly important 0b 6a 0b Nalidixic Acid ≥ 32 Critically important 1b 6a 0b Streptomycin ≥ 64 Critically important 39a 20b 12b Tetracycline ≥ 16 Highly important 46a 44a 21b Ceftiofur ≥ 8 Critically important 1b 6a 0b a,bDifferent letters across treatments for each antimicrobial are considered significantly different (P<0.1). 1WHO Critically important antimicrobials for human medicine 6th Revision 2018. (WHO, 2018). 2The total number of isolates tested against each antimicrobial was 118. 3The total number of isolates tested against each antimicrobial was 120.
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Monpro Control Base
Tetracyclines
Aminoglycosides
Cephems
Quinolones
Phenicols
β-Lactam
Aminopenicillins
0 20 40 60 80 100
NUMBER OF E. COLI RESISTANT
Figure 7. Number of AMR generic E. coli to specific antimicrobial classes by treatment.
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Table 8. Patterns of AMR for generic E. coli isolates from feedlot cattle. Day n= Pattern* # % 0 108 TETRA 12 11.1 NALAC 3 2.8 CHLORA-TETRA 3 2.8 STREPT-TETRA 17 15.7 CHLORA-STREPT-TETRA 3 2.8 AMOCLA-AMPICI-CEFOXI-CEFTIF-CEFTRI-CHLORA- STREPT-TETRA 3 2.8 56 108 STREPT 2 1.9 TETRA 14 13.0 NALAC 3 2.8 CHLORA-TETRA 3 2.8 STREPT-TETRA 18 16.7 CHLORA-STREPT-TETRA 7 6.5 AMPICI-STREPT-TETRA 3 2.8 CEFTRI-CHLORA-STREPT 1 0.9 AMOCLA-AMPICI-CEFOXI-CEFTIF-CEFTRI-CHLORA- STREPT-TETRA 3 2.8 140 142 NALAC 1 0.7 STREPT 2 1.4 TETRA 12 8.5 STREPT-TETRA 5 3.5 CHLORA-STREPT-TETRA 6 4.2 AMPICI-CHLORA-STREPT-TETRA 1 0.7 *Abbreviation definitions found in Table 4.
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a,b a b 100 93 87.8 90 83.3 80 70 60 50 40 30 20 10 0 Percent of resistant isolates Base Control Monpro Cattle Treatments
Figure 8. Total percentage of Enterococcus isolates with resistance to one or more antimicrobial tested over the entire feeding trial. Columns with different letters represent significant differences (P < 0.10).
Base Control Monpro
a a a a a a 97.1 100 97.8 100 100 b 94.4 95 90 80 b,c 77.1 c 70 65.7 60 56.3 50 40 30 Percent of resistant isolates 20 10 0 0 56 140 Days on Feed
Figure 9. Percent of Enterococcus isolates resistant to one or more antimicrobials tested over time. Columns with different letters represent significant differences (P < 0.10).
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100 0 1 2 >3 90
80
70
60
50
40
30
Percent of isolates with and AMR MDR 20
10
0 Base Monpro Control Base Monpro Control Base Monpro Control 0 56 140 0 34.3 43.8 22.9 2.9 5.6 0.0 2.2 5.0 0.0 1 17.1 12.5 14.3 45.7 47.2 33.3 22.2 15.0 16.3 2 17.1 18.8 17.1 40.0 19.4 22.2 13.3 7.5 20.9 >3 31.4 25.0 45.7 11.4 27.8 44.4 62.2 72.5 62.8
Figure 10. Percent of Enterococcus isolates with resistance to up to 3 antimicrobials collected during the feeding trial.
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Table 9. Distribution of Enterococcus AMR frequency considering percentage within each treatment, collection and number of antimicrobials with resistance. Day on Feed Treatment n 0 1 2 3 4 5 6 7 8 9
Base 35 34.3 17.1 17.1 20 11.4 0 0 0 0 0 0 Control 35 22.9 14.3 17.1 37.1 2.9 5.7 0 0 0 0 Monpro 32 43.8 12.5 18.8 18.8 6.3 0 0 0 0 0
Base 35 2.9 45.7 40 11.4 0 0 0 0 0 0 56 Control 36 0 33.3 22.2 25 19.4 0 0 0 0 0 Monpro 36 5.6 47.2 19.4 22.2 5.6 0 0 0 0 0
Base 45 2.2 22.2 13.3 31.1 4.4 2.2 0 2.2 20 2.2 140 Control 43 0 16.3 20.9 34.9 4.7 4.7 0 0 18.6 0 Monpro 40 5 15 7.5 40 7.5 2.5 0 0 20 2.5
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Table 10. Minimum inhibitory concentrations and number of Enterococcus isolates with resistance to specific antimicrobials tested over the entire feeding trial.
Cattle Treatments Resistant Antimicrobial Drug WHO Ranking1 Base2 Control3 Monpro4 (µg / ml) Ciprofloxacin ≥ 4 Critically important 2 1 2 Daptomycin n/a Critically important 23 22 20 Erythromycin ≥ 8 Critically important 24 28 23 Lincomycin ≥ 8 Highly important 87b 102a 85b Linezolid ≥ 8 Critically important 11 8 9 Nitrofurantoin ≥ 128 Important 1 4 1 Penicillin ≥ 16 Critically important 11 8 9 Quinupristin/dalfopristin ≥ 4 Highly important 11 9 9 Tetracycline ≥ 16 Highly important 51b 65a 47b Tylosin Tartrate ≥ 32 Critically important 44 50 41 Vancomycin ≥ 32 Critically important 11 8 9 a,bDifferent letters across treatments for each antimicrobial are considered significantly different. No letters mean no significant differences. 1WHO Critically important antimicrobials for human medicine 6th Revision 2018 (WHO, 2018). 2Base treatment for each antimicrobial had 115 isolates tested for resistance. 3Control treatment for each antimicrobial had 114 isolates tested for resistance. 4Monpro treatment for each antimicrobial had 108 isolates tested for resistance.
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Monpro Control Base
Glycopeptides
Tetracyclines
Streptogramins
ß-Lactams
Nitrofurantions
Oxazolidinones
Lincosamide
Macrolides
Lipopeptides
Quinolones
0 20 40 60 80 100 120 Number of Enterococcus Isolates Resistant
Figure 11. Number of AMR Enterococcus to specific antimicrobial classes.
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Table 11. Antibiotic resistance patterns of Enterococcus isolates from feedlot cattle. Day n= Pattern* # % 0 102 CIPROF 2 2.0 LINCOM 6 5.9 TETRA 5 4.9 LINCOM-TYLO 12 11.8 LINCOM-TETRA 2 2.0 ERYTH-TYLO 1 1.0 DAPTOM-LINCOM 3 2.9 LINCOM-TETRA-TYLO 21 20.6 CIPROF-PENICI-TETRA 1 1.0 CIPROF-TETRA-TYLO 1 1.0 DAPTOM-LINCOM-TETRA 3 2.9 ERYTH-LINCOM-TETRA-TYLO 3 2.9 DAPTOM-ERYTH-TETRA-TYLO 4 3.9 DAPTOM-ERYTH-LINCOM-TETRA-TYLO 2 2.0 56 108 LINCOM 44 40.7 DAPTOM 1 0.9 LINCOM-TETRA 22 20.4 LINCOM-NITRO 1 0.9 DAPTOM-LINCOM 1 0.9 ERYTH-LINCOM 4 3.7 LINCOM-SYNERC 1 0.9 LINCOM-NITRO-TETRA 5 4.6 DAPTOM-LINCOM-TETRA 9 8.3 ERYTH-LINCOM-TETRA 7 6.5 DAPTOM-ERYTH-LINCOM-TETRA 9 8.3 140 128 TETRA 4 3.1 CIPROF 1 0.8 LINCOM 18 14.1 LINCOM-TETRA 11 8.6 LINCOM-TYLO 7 5.5 LINCOM-TETRA-TYLO 37 28.9 ERYTH-TETRA-TYLO 1 0.8 ERYTH-LINCOM-TYLO 5 3.9 ERYTH-LINCOM-SYNERC-TETRA-TYLO 1 0.8 ERYTH-LINCOM-TETRA-TYLO 10 7.8 DAPTOM-ERYTH-LINCOM-TETRA-TYLO 3 2.3 DAPTOM-ERYTH-LINCOM-LINEZO-SYNERC-TYLO-VANCOM 1 0.8 DAPTOM-ERYTH-LINCOM-LINEZO-PENICI-SYNERC-TYLO- VANCOM 25 19.5 DAPTOM-ERYTH-LINCOM-LINEZO-PENICI-SYNERC-TETRA- TYLO-VANCOM 2 1.6 *Abbreviation definitions found in Table 5.
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Table 12. Antimicrobial resistance genes and the drug classes they confer resistance to present in E. coli O157 isolates collected from feedlot cattle.
Acquired Resistance Genes Beta- Macrolides Aminoglycoside Fosfomycin Phenicol Sulphnoamide Tetracycline lactam Day Treatment n mdf (A) aph(3')-Ib aac(2')IIa aac(6')-Iaa aadA1b aph(6')-Id fosA7 floR sul2 tetA blaOXA-2 Base 6 6 1 0 0 0 1 0 1 1 1 0 0 Control 7 6 0 1 1 0 0 1 0 0 0 0 Monpro 5 5 0 0 0 0 0 0 0 0 0 0 Base 1 1 0 0 0 0 0 0 0 0 0 0 56 Control 5 5 1 0 0 0 1 0 1 1 1 0 Monpro 4 4 1 0 0 0 1 0 1 1 1 0 Base 2 2 0 0 0 0 0 0 0 0 0 0 140 Control 2 2 0 0 0 1 0 0 0 1 1 1 Monpro 0 ------
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Table 13 Antimicrobial resistance genes and phenotypic resistance present in Salmonella isolates collected from feedlot cattle.
Acquired Resistance Genes Phenotype Resistance
Tetracycline Aminoglycoside Chloramphenicol Tetracycline
Day Treatment n tetB tetJ aac(6')-Iaa cat
Base 4 1 0 4 0 1 0 Control 5 2 0 5 0 2 Monpro 4 0 0 4 1 0 Base 0 - - - - - 56 Control 1 0 1 1 1 0 Monpro 1 0 0 1 0 0 Base 3 1 0 3 0 1 140 Control 3 2 0 3 0 2 Monpro 1 0 0 1 0 0
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Figure 12. The number of unique sequences identified for each treatment. Boxplots depict the median and quartile range.
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Figure 13. Bacterial phylum for each treatment at the beginning of feeding trail and at the end of feeding.
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Figure 14. Comparisons of the relative abundances of the bacterial phyla at day 0 and 140 for each treatment. Bacteroidetes increased over time with the overall reduction in relative abundances of all other bacteria. Treatment changes were not observed following application of either control or monpro diets. Significance was determined using the Wilcoxon signed-rank test.
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Figure 15. Alpha diversity (Richness (number of bacterial taxa) and Shannon-diversity) of treatments on days 0 and 140. There were no significant differences on day 0 or 140. Significance was determined using the Wilcoxon signed-rank test.
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Figure 16. Alpha diversity comparisons between treatments for day 0 and 140.
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Figure 17. Principle coordinate analysis of weighted UniFrac (wUniFrac) distances showing the beta diversity comparing treatments and days 0 and 140. Groups were not significantly different (Day 0: p = 0.111, Day 140: p = 0.265) and within treatments base, control and monpro at each sampled time-point (base: p = 0.118, control: p = 0.452, monpro: p = 0.233). Significance determined using permutational multivariate analysis of variance (permanova) with significance defined as a p < 0.05.
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Figure 18. Bacterial taxa differentially abundant by treatments. Fold-change (x-axis, log2) of differentially abundant taxa for each treatment. Each point represents taxa associated with day 0 (left of the vertical dashed line) or day 140 (to the right of the vertical dashed line).
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Figure 19. Individual abundance plots for all taxa from Figure 18 comparing day 0 and 140 for each treatment. Y-axis represents rlog normalized abundance and each plot represents one bacterial family with genera encoded using color.
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A. B.
Figure 20. (A) Bacterial taxa differentially abundant between control and monpro on day 140. Taxa to the left represent control treatments and to the right monpro. (B) Individual plots of each of the statistically significant bacterial taxa from figure (A). Y-axis is rlog normalized abundance with each plot representing one bacterial family with genera encoded using color.
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Table 14 Standard curves for cytokine and chemokines analyzed. Analyte Chi R² CV MinDC MaxDC IL-1a 0.075% 0.999 2.30% 0.36 17216 IL-1b 0.034% 1 1.15% 1.71 77176 IL-1RA 0.054% 0.999 1.70% 4.04 127916 IL-6 0.050% 0.999 1.61% 0.41 18082 IL-17A 0.059% 0.999 1.63% 0.17 8886 IL-2 0.064% 0.999 2.09% 8.2 357117 IL-4 0.037% 1 1.18% 5.52 121137 IFNƴ 0.070% 0.999 2.23% 0.055 4298 IL-8 0.030% 1 0.87% 6.62 40533 IP-10 0.041% 1 1.27% 0.72 40439 CCL2 0.029% 1 0.86% 1.83 41888 CCL3 0.043% 1 1.23% 8.08 793109 IL-10 0.058% 0.999 1.70% 1.75 86331 CCL4 0.022% 1 0.65% 0.99 152101 TNFa 0.060% 0.999 1.82% 7.23 360841 Chi = The chi-square test statistic of the distance between observed concentrations with expected concentrations. R2 = measure of model fitness CV = The coefficient of variation of standard curve replicates at each dilution level. MinDC = minimum detectable concentration MaxDC = maximum detectable concentration
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Table 15. Cytokines mean concentrations (pg/ml), standard deviation, 95% confidence limit and p-values (P<0.1) for each treatment at the end of the feeding period.
Lower 95% Upper 95% Treatment n Mean Std Dev P value CL for Mean CL for Mean Base 9 0.3 1.0 0.6 0.5 IFNƴ Control 11 0.3 1.2 0.7 0.6 0.95 Monpro 12 -0.3 1.9 0.8 1.7 Base 8 101.4 666.2 383.8 337.8 TNFɑ Control 7 142.7 527.5 335.1 208.0 0.71 Monpro 10 176.5 399.3 287.9 155.7 Base 8 452.8 1264.5 858.7 485.4 IL-2 Control 5 -20.3 1286.0 632.8 526.0 0.68 Monpro 9 502.3 1163.8 833.0 430.3 Base 8 22.7 116.1 69.4 55.9 IL-4 Control 10 40.0 262.9 151.4 155.8 0.36 Monpro 12 54.5 215.0 134.7 126.3 Base 9 0.7 5.8 3.2 3.3 IL-6 Control 9 -2.4 30.7 14.2 21.5 0.16 Monpro 12 3.6 8.7 6.2 4.1 Base 8 23.3 183.9 103.6 96.1 IL-10 Control 10 -237.0 1056.4 409.7 904.0 0.42 Monpro 11 61.2 240.6 150.9 133.5 Base 8 10.7 26.6 18.7 9.6 IL-1a Control 8 8.5 37.6 23.1 17.4 0.79 Monpro 10 12.5 28.1 20.3 10.9 Base 9 5.8 17.7 11.7 7.8 IL-1b Control 11 1.4 35.3 18.3 25.3 0.78 Monpro 12 0.2 35.4 17.8 27.7 Base 9 18.5 117.3 67.9 64.3 IL-1RA Control 9 15.7 227.8 121.8 137.9 0.31 Monpro 12 28.3 100.1 64.2 56.4 Base 5 0.2 0.5 0.4 0.1 IL-17A Control 4 0.1 1.2 0.6 0.3 0.64 Monpro 4 -0.9 2.4 0.7 1.0 Base 8 206.8 367.0 286.9 95.8 IP-10 Control 9 205.0 366.5 285.8 105.1 0.95 Monpro 10 198.9 347.2 273.1 103.6
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Table 16. Chemokine mean concentrations (pg/ml), standard deviation, 95% confidence limit and P-values (P<0.1) for each treatment at the end of the feeding period.
Lower 95% Upper 95% Treatment n Mean Std Dev P value CL for Mean CL for Mean Base 11 290.4 1110.3 700.4 610.2 IL-8 Control 12 383.0 4546.4 2464.7 3276.3 0.16 Monpro 14 -2124.7 9117.2 3496.2 9735.2 Base 11 187.9 335.8 261.8 110.1 CCL2 Control 13 77.7 856.7 467.2 644.6 0.60 Monpro 14 199.0 513.1 356.1 272.0 Base 11 81.7 255.8 168.8 129.6 CCL3 Control 13 -62720.0 169888.2 53584.1 192462.8 0.60 Monpro 14 71.6 442.4 257.0 321.1 Base 11 39.4 83.1 61.3 32.6 CCL4 Control 13 -33.8 339.0 152.6 308.4 0.70 Monpro 14 39.3 72.5 55.9 28.7
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APPENDIX A
Sensitire™ Plate schematics
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