© 2011 Cristiane San Miguel ALL RIGHTS RESERVED

© 2011

Cristiane San Miguel

ALL RIGHTS RESERVED

FACTORS INFLUENCING THE PROLIFERATION OF ANTIBIOTIC RESISTANCE

GENES IN THE SOIL ENVIRONMENT: IMPACT OF ORGANIC WASTES, NATIVE

OCCURRENCE OF ANTIBIOTIC RESISTANCE GENES AND EFFECT OF

MANURE ON THE RATE OF CHLORTETRACYCLINE RESISTANCE

by

CRISTIANE SAN MIGUEL

A Dissertation submitted to the

Graduate School-New Brunswick

Rutgers, The State University of New Jersey

in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

Graduate Program in Environmental Sciences

Written under the direction of

Professor Robert L. Tate, III

and approved by

_________________________________________

_________________________________________

_________________________________________

_________________________________________

New Brunswick, New Jersey

January, 2011

ABSTRACT OF THE DISSERTATION

Factors Influencing the Proliferation of Antibiotic Resistance

Genes in the Soil Enviornment: Impact of Organic Wastes, Native

Occurrence of Antibiotic Resistance Genes And Effect of Manure

on the Rate of Chlortetracycline Resistance

by CRISTIANE SAN MIGUEL

Dissertation Director:

Professor Robert L. Tate, III

Manure is increasingly land applied as the organic industry booms. At

the same time, antibiotic resistance (AR) in pathogenic bacteria is rendering

many antibiotics useless and there is growing concern regarding AR in

environmental organisms. It has been suggested that soils contain a vast and

mobile reservoir of AR determinants, dubbed the soil resistome. Studies have

shown the transfer of AR genes in soils, which may be enhanced by a variety

of soil properties, which may, themselves, be impacted by manure

amendment. Additionally, manure may contain antibiotic residues and AR

enteric organisms that are introduced to soil through land application. Thus,

environmental bacteria may acquire AR genes from introduced enterics or

may contribute AR to these introduced species.

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Chapter 1 describes a study of the impact of both manure and an acidic

food processing waste on a varitey of soil properties in an attempt to

determine the role waste amendment may play in enhancing AR proliferation.

Results indicate that manure amendment enhanced several properties

associated with genetic transfer. Conversely, amendment with cranberry

processing waste lowered the likelihood of AR dissemination. Analysis of

metabolic diversity data revealed the limitation of Principal Component

Analysis (PCA) with these datasets and a new, direct comparison analysis was

developed, as outlined in Chapter 2.

To investigate the possible origins of AR, a soil bacterium, isolated in

1963, was assayed for its ability to resist multiple antibiotics, including two

introduced after 1963, as described in Chapter 3. It was determined that this

organism was highly resistant to all drugs tested, including ciprofloxacin

(CIP), a fully-synthetic compound introduced 25 years after isolation. Several

resistance mechanisms, including those most common for CIP, were tested

for, unsuccessfully. Thus, the mechanism(s) utilized by this organism remain

unknown but appears to be novel.

Chapter 4 describes a microcosm study, undertaken to evaluate the

impact of manure free of antibiotics and enterics on AR proliferation. The

findings indicate that “clean” manure may actually limit the occurrence of AR

in the soil environment by reducing stress response in indigenous bacteria.

Furthermore, the data suggest stress response can protect against therapeutic

levels of antibiotic.

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ACKNOWLEDGEMENTS

There are so many people who deserve credit for the completion of this project. First and

foremost is my spouse, Xandra, who has supported me always, encouraged me often and loved

me through it all. The arrival of our daughter, Izzy, spurred me to finally finish and I love her

dearly for it. The rest of my family: parents, cousins and in-laws, while maybe not understanding

my motivation, never questioned my insistence on completing my Ph.D. and for that I am very

grateful.

My friends, fellow students and post-doc’s, have been invaluable to me over the years.

For research guidance, venting sessions and much needed diversions, many thanks go to Jen Kist,

Melissa Dulinski, Ula Filipowicz, Robyn Mikita, Adam Mumford, Amita Oka, Elise Rodgers-

Viera and Drs. Amy Callaghan, Jose Pérez-Jiménez, Danielle Rhine, Sinéad Ní Chadhain, R.

Sean Normand, John Kelly, Eric Gaulin, and Scott Mittman.

For lab usage, guidance and often needed direction, my thanks go to Drs. Craig Phelps,

Jerry Kukor, Gerben Zylstra, Uta Krogmann, Lee Kerkhof and my committee, Drs. Lily Young,

Daniel Gimenez and Boris Wawrik, in addition to my advisor, Dr. Robert Tate.

Finally, I must acknowledge the publication of Chapter 2, in part, in Soil Biology and

Biochemistry (2007, 39:1870-1877).

TABLE OF CONTENTS

Page

Abstract of the Dissertation……………………………………………………………… ii

Acknowledgements………………………………………………………………………iv

List of Tables…………………………………………………………………………… vii

List of Figures…………………………………………………………………………..viii

Introduction……………………………………………………………………………… 1

Chapter 1: Impact of Land Applied Organic Wastes on Soil Properties Related

to the Proliferation of Antibiotic Resistance in Soil

1. Abstract……………………………………………………………. 9

2. Introduction………………………………………………………. 11

3. Materials and Methods………………………………………….... 14

4. Results and Discussion…………………………………………… 21

5. Conclusions………………………………………………………. 46

6. References………………………………………………………... 48

Chapter 2: Direct Comparison of Individual Substrate Utilization from a

CLPP Study: A New Analysis for Metabolic Diversity Data

1. Abstract……………………………………………………………54

2. Introduction……………………………………………………..... 55

3. Materials and Methods…………………………………………… 57


5. Conclusions………………………………………………………. 79

6. References………………………………………………………... 81

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Chapter 3: Multidrug Resistance in an Archived Soil Bacterium

1. Abstract……………………………………………………………83

2. Introduction………………………………………………………. 84

3. Materials and Methods…………………………………………… 87


5. Conclusions……………………………………………………... 102

6. References………………………………………………………. 105

Chapter 4: Manure as a selective pressure for the proliferation of antibiotic

resistance in the soil environment

1. Abstract…………………………………………………………. 110

2. Introduction……………………………………………………... 111

3. Materials and Methods…………………………………………. 114

4. Results and Discussion…………………………………………. 118

5. Conclusions……………………………………………………... 142

6. References………………………………………………………. 144

7. Appendices……………………………………………………… 148

Conclusions…………………………………………………………………………… 152

Curriculum Vita……………………………………………………………………….. 157

LIST OF TABLES Page Chapter 1:

Table 1 - Average Soil Characteristics – Manure Amended and Control…………. 22

Table 2 - Correlations of Soil Properties to PC1 for All Data – Manure

Amended and Control Soils……………………………………………… 33

Table 3 - Average Soil Characteristics – Cranberry Amended Soils………………. 35

Table 4 - Gravel and Sand Distribution – Cranberry Amended Soils……………… 36

Table 5 - Correlations of Soil Properties to PC1 for All Data – Cranberry

Amended Soils …………………………………………………………... 45

Chapter 2:

Table 1 - EcoPlate Well Number, Name of Substrate and Substrate Type of

12 Substrates that Deviated from the One-to-One Line with n = 40

Samples/Soil Depth (San Miguel et al., 2007)…………………………… 65

Table 2 - Comparison of Smaller Groupings, when Plotted Against One-to-One

Line, Versus n = 40 Dataset Results (San Miguel et al., 2007)………….. 67

Chapter 3:

Table 1 - Properties of Antibiotic Utilized…………………………………………. 89

Table 2 - Primer Sequences and PCR Conditions…………………………………. 91

Table 3 - Antibiotic Tolerance of Achromobacter xylosoxidans ATCC 15446

Compared to Minimum Inhibitory Concentration Standards for

Resistance (MIC90 ) for Enterobacteriaceae spp………………………… 94

Chapter 4:

Table 1 – MANOVA Results……………………………………………………... 134

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LIST OF FIGURES

Page

Chapter 1

Fig. 1- Water Retention Curve of Disturbed Samples (MA=□, MC=■)

and Undisturbed Soils (MA=○, MC=●). ………………………………….24

Fig. 2 - Pore Size Distributions of Manure Amended and Control Soils as

Estimated from Water Retention Data Using Eq. 5……………………….27

Fig. 3 - PCA of Metabolic Diversity Data from Manure Amended (MA=○)

and Control (MC=●) Soils……………………………………………….. 29

Fig. 4 - PCA of All Data – Manure Amended and Control Soil…………………. 32

Fig. 5 - Water Retention Curve of Disturbed Samples (CH=Δ, CL=▼) and

Undisturbed Soils (CH=○, CL=●)……………………………………….. 39

Fig. 6 - Pore Size Distributions of Cranberry Amended Soils as Estimated from

Water Retention Data Using Eq. 5……………………………………….. 40

Fig. 7 - PCA Of Metabolic Diversity Data from Soil Amended with Cranberry

Waste at High (CH=○) and Low (CL=●) Rates…………………………. 41

Fig. 8 - PCA of All Data – Cranberry Amended Soils (CH=○, CL=●)…………... 43

Chapter 2

Fig. 1 - Principal Component Analysis of All Data (n = 40) (San Miguel et al.,

2007)…………………………………………………………………….... 63

Fig. 2 - One-to-One Comparison of Optical Density Data for Complete

Dataset (n = 40 Samples/Soil Depth) (San Miguel et al., 2007)…………. 64

viii

Fig. 3 - One-to-One Comparison of Optical Density Data for Grouping

of 3 Samples/Soil Depth (San Miguel et al., 2007)………………………. 68

Fig. 4 - One-to-One Comparison of Optical Density Data for Grouping

of 10 Samples/Soil Depth (San Miguel et al., 2007)……………………... 69

Fig. 5 - One-to-one comparison optical density data from MA and MC.

A) OD ≥ 0.25 AU, B) OD between 0.25 and 0.14 AU,

C) OD ≤ 0.14 AU. …………………………………………………………76

Fig. 6 - One-to-one comparison of optical density data from CH and CL.

A) OD ≥ 0.18 AU, B) OD < 0.18 AU. …………………………………... 78

Chapter 3

Fig. 1 - MIC for Ciprofloxacin (CIP) With and Without the Efflux Inhibitor

Phenylalanine Arginine β-Naphthylamide (PAβN)………………………100

Fig. 2 - Differences in Sample acrA RNA as Measured by Q-RT-PCR Using

0 μg CIP/mL Sample as Calibrator……………………………………... 101

Chapter 4

Fig. 1 - Schematic of Microcosm Setup for One Replicate of One Sampling Time

and Notation of Treatments. ……………………………………………. 115

Fig. 2 - Total biomass counts from SE plates in log scale. Straight lines

indicate linear regression of no-cTc control counts. A) soil only;

B) soil + manure………………………………………………………….119

Fig. 3 - Comparison of total biomass counts within a cTc concentration

in log scale. A) 1 μg/g soil; B) 10 μg/g soil; C) 25 μg/g soil…………….121

ix

x

Fig. 4 - cTc10R counts in log scale. Straight lines indicate linear regression

of no-cTc control counts. A) soil only; B) soil + manure……………….122

Fig. 5 - Comparison of cTc10R counts within a cTc concentration in log

scale. A) 1 μg/g soil; B) 10 μg/g soil; C) 25 μg/g soil…………………..124




scale. A) 1 μg/g soil; B) 10 μg/g soil; C) 25 μg/g soil………………….. 127




scale. A) 1 μg/g soil; B) 10 μg/g soil; C) 25 μg/g soil…………………...130

Fig. 10 - Percent of Total Cultivable Bacteria Count Resistant to cTc.

A) cTc10R; B) cTc25R; C) cTc50R……………………………………….132

Fig. 11 - Denaturing Gradient Gel Electrophoresis Images. A) 0 μg/g soil;

B) 1 μg/g soil; C) 10 μg/g soil; D) 25 μg/g soil…………………………..136

Fig. 12 - Phylogenetic Tree of DGGE band sequences…………………………….141

1

INTRODUCTION

Bacterial resistance to antibiotics has been problematic almost since the discovery

of these compounds more than 70 years ago. One of the first reports of antibiotic

resistance was made in 1948 by Gezon who noted a laboratory strain of Streptococcus

pyogenes resistant to benzylpenicillin (as cited in Bryskier, 2005). More recently,

infection due to methicillin-resistant Staphylococcus aureus (MRSA) strains have

become frequent evening news items. The majority of effective antibiotic resistance

(AR) mechanisms in human pathogens are believed to have been acquired by genetic

exchange (Tomasz, 2006, Aminov, 2009), since these genes were not present in human or

animal flora in the pre-antibiotic era (Mazel and Davies, 1999). Although much of the

research into AR has focused on clinically relevant strains, environmental isolates have

also been found to harbor AR genes, particularly soil organisms (Nwosu, 2001, Séveno et

al., 2002).

More than 80% of antibiotic (AB) compounds in clinical use are derived from soil

bacteria (D’Costa et al., 2007), mainly members of the bacterial order Actinomycetales,

which are ubiquitous in the soil environment. One study of soil dwelling actinomycetes,

specifically from the genus Streptomyces, found that 60% of isolates were resistant to 6-

8 different antibiotic compounds (D’Costa et al., 2006). Antibiotics are produced for a

number of reasons in natural environments including self-protection against competing

organisms and intercellular signaling (Linares et al., 2006, Martínez, 2008, Aminov,

2009). However, a recent phylogenetic review of resistance genes found that it was the

non-AB producing bacteria that harbored a large, diverse and readily available pool of

2

AR genes (Aminov and Macki, 2007). AR genes, either intrinsic or acquired, may

provide benefits to non-AB producers without any fitness cost or the cost may be

ameliorated by compensatory mutation (Andersson and Levin, 1999). Moreover, some

indigenous soil organisms can metabolize AB compounds for growth. A recent study

found that all soils tested harbored bacteria that were capable of utilizing a variety of

antibiotics as sole carbon sources in addition to being resistant to multiple AB at clinical

concentrations (Dantas et al., 2008).

It is widely believed that overuse of antibiotics has, at least in part, been the cause

of widespread antibiotic resistance. In a Swedish study, one 7-day course of clindamycin

was found to select for antibiotic resistant organisms in the human gut microbiota up to

two years after administration with no further AB use (Jernberg et al., 2007), indicating

that the utilization of antibiotics will lead to an increased incidence of antibiotic resistant

bacteria that will persist. Recently, environmental contamination with antibiotics, as well

as other pharmaceutical compounds, has been of great concern (see, for example,

Daughton and Ternes, 1999, Calza et al., 2010, Camacho-Muñoz et al., 2010, Snyder and

Benotti, 2010). In agricultural soils, antibiotics may be introduced through direct

application, such as foliar spraying of streptomycin on plants (Vidaver, 2002), or

indirectly through AB contaminated manures (Aust et al., 2008), resulting from the use of

antibiotics in animal husbandry.

For nearly four decades, more than half of all antibiotics produced in the United

States has been used in animal husbandry (Huber, 1971), in part for prophylactic and

growth promotion purposes. Millions of pounds of antibiotics are fed to livestock each

year at subtherapeutic levels (Chee-Sanford et al., 2009). Multiple studies have found

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that conjugative transfer of resistance genes takes place in the digestive systems of

animals, including transfer between resistant and susceptible strains of the same species

(Mater et al., 2005) and between different species of potential pathogens (McCuddin et

al., 2006). Thus, the use of subtherapeutic regimes has the potential to result in antibiotic

resistant enteric bacteria (Huber, 1971), which are then excreted in the animals’ wastes,

along with, on average, 75% of the administered antibiotics (Chee-Sanford et al., 2009).

Not only has dairy farm corral soil been shown to harbor these bacteria (Burgos et al.,

2005) but land application of farmyard manure has also been shown to spread these

potential pathogens across the soil environment (Sengeløv et al., 2003).

Once introduced to the soil environment, these enteric bacteria can survive for a

period of time sufficient for the spread AR genes to environmental strains. Franz et al.

(2008) found that Escherichia coli O157:H7 survived approximately 70 days in farmyard

manure-amended soils, enough time for transfer of both AR and pathogenicity genes.

Onan and LaPara (2003) found that subtherapeutic doses of antibiotics in animal

husbandry affected the abundance and types of resistant bacteria in nearby soil.

Furthermore, multiple studies have found that excessive land application of manure leads

to the persistence of AR genes in soil bacteria (Sengeløv et al., 2003, Ghosh and LaPara,

2007). While the incidence of the sulfadiazine resistance gene, sul1, present in land

applied manure, was found to decrease initially after manure application, it remained

stable thereafter with no additional selective pressure (Heuer et al., 2008). Resistance

may have no cost or the cost may be ameliorated by compensatory mutations (Andersson

and Levin, 1999). Thus, once introduced, these genetic elements can persist in the soil

metagenome.

4

Selective pressure due to the presence of AB in land applied manure may drive

horizontal gene transfer (HGT) in soils, particularly in soils that regularly receive

manure. It has been known for some time that, in gram-negative enterics, AB

concentrations as low as 2 parts per million can result in resistant phenotypes (Huber,

1971). Additionally, Kelch and Lee (1978) reported that bacteria within a given

ecosystem could and would share AR genes. Since naked DNA can persist in soils,

especially those high in clay content or humic substances (Levy-Booth et al., 2007),

conjugative HGT is not the only mechanism for acquisition of AR genes in the soil

environment. In competent cells, soil-bound DNA can be salvaged and transformed

(Crecchio and Stotzky, 1998, Stotzky, 2000). Competence can be induced in ecosystems

with high nutrient availability, as manured soil would be (Levy-Booth et al., 2007) and

transformation can be stimulated in the rhizosphere by plant root exudates (Nielsen and

van Elsas, 2001).

The issue of enhanced mobility of AR determinants with manure amendment may

become a much larger one in the future as more farms, both family and agribusiness, turn

to organic practices. The USDA’s National Organic Program (NOP) allows the use of

animal materials for the maintenance of crop nutrients and soil fertility (USDA, 2000).

While there are strict guidelines regarding the timing of applying raw manure to fields

(USDA, 2000), there are no restrictions on the source of manure.

The primary objective of this research project is to determine the factors

influencing the proliferation of antibiotic resistance genes in the soil environment. First,

soil properties associated with increased residence time and mobility of genetic material

were assessed to determine the long-term impact of organic waste amendment, including

5

manure and cranberry wastes. This included the assessment of multiple physical,

chemical and biological soil parameters, as detailed in Chapter 1. Statistical analysis of

the metabolic diversity data obtained in Chapter 1 indicated a need for a more descriptive

analysis, the development of which is described in Chapter 2. Given the question of

source of AR determinants, an archived soil bacterium, isolated prior to the extensive use

of antibiotics in agriculture, was assessed for its antibiotic resistance capabilities, as

outlined in Chapter 3. Finally, a soil microcosm study was undertaken to assess the

proliferation of antibiotic resistance following a single low dose application of a common

antibiotic feed additive, chlortetracycline.

6

References

Aminov, R.I. 2009. The role of antibiotics and antibiotic resistance in nature. Environ. Microbiol. 11(12):2970-2988.

Aminov, R.I., Mackie, R.I. 2007. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol. Lett. 271:147-161.

Andersson, D.I., Levin, B.R. 1999. The biological cost of antibiotic resistance. Curr. Opin. Microbiol. 2:489-493.

Aust, M.-O. Godlinski, F., Travis, G.R., Hao, X., McAllister, T.A., Leinweber, P., Thiele-Bruhn, S. 2008. Distribution of sulfamethazine, chlortetracycline and tylosin in manure and soil of Canadian feedlots after subtherapeutic use in cattle. Environ. Poll. 156:1243-1251.

Bryskier, A. 2005. Epidemiology of resistance to antibacterial agents. p. 39. In A. Bryskier (ed.), Antimicrobial Agents: Antibacterials and Antifungals.ASM Press, Washington, DC.

Burgos, J.M., Ellington, B.A., Varela, M.F. 2005. Presence of multidrug-resistant enteric bacteria in dairy farm topsoil. J. Dairy Sci. 88:1391-1398.

Calza, P., Marchisio, S., Medana, C., Baiocchi, C. 2010. Fate of antibacterial spiramycin in river waters. Anal. Bioanal. Chem. 396:1539-1550.

Camacho-Muñoz, M.D., Santos, J.L., Aparicio, I., Alonso, E. 2010. Presence of pharmaceutically active compounds in Doñana Park (Spain) main watersheds. J. Hazard. Mater. 177:1159-1162.

Chee-Sanford, J.C., Mackie, R.I., Koike, S., Krapac, I.G., Lin, Y.-F., Yannarell, A.C., Maxewell, S., Aminov, R.I. 2009. Fate and transport of antibiotic residues and antibiotic resistance genes following land application of manure waste. J. Environ. Qual. 38:1086-1108.

Crecchio, C., Stotzky, G. 1998. Binding of DNA on humic acids: effect on transformation of Bacillus subtilis and resistance to DNase. Soil Biol. Biochem. 30(8/9):1061-1067.

Dantas, G., Sommer, M.O.A., Oluwasegun, R.D., Church, G.M. 2008. Bacteria subsisting on antibiotics. Science 320:100-103.

Daughton, C.G., Ternes, T.A. 1999. Pharmaceuticals and personal care products in the environment: agents of subtle change? Environ. Health Persp. 107(Suppl. 6):907-938.

D’Costa, V.M., Griffiths, E., Wright, G.D. 2007. Expanding the soil antibiotic resistome: exploring environmental diversity. Curr. Opin. Microbiol. 10:481-189.

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D’Costa, V.M., McGrann, K.M., Hughes, D.W., Wright, G.D. 2006. Sampling the antibiotic resistome. Science. 311:374-377.

Franz, E., Semenov, A.V., Termorshuizen, A.J., de Vos, O.J., Bokhorst, J.G., van Bruggen, A.H.C. 2008. Manure-amended soil characteristics affecting the survival of E. coli O157:H7 in 36 Dutch soils. Environ. Microbiol. 10(2):313-327.

Ghosh, S., LaPara, T.M. 2007. The effects of subtherapeutic antibiotic use in farm animals on the proliferation and persistence of antibiotic resistance among soil bacteria. The ISME J. 1:191-203.

Heuer, H., Focks, A., Lamshöft, M. Smalla, K., Matthies, M., Spiteller, M. 2008. Fate of sulfadiazine administered to pigs and its quantitative effect on the dynamics of bacterial resistance genes in manure and manured soils. Soil Biol. Biochem. 40:1892-1900.

Huber, W.G. 1971. The impact of antibiotic drugs and their residues. In C.E. Cornelius (ed.), Advances in Veterinary Science and Comparative Medicine, v. 15.Academic Press, New York, NY. p. 101-132.

Jernberg, C., Löfmark, S., Edlund, C., Jansson, J.K. 2007. Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. ISME J. 1:56-66.

Kelch, W.J., Lee, J.S. 1978. Antibiotic resistance patterns of Gram-negative bacteria isolated from environmental sources. Appl. Environ. Microbiol. 36(3):450-456.

Levy-Booth, D.J., Campbell, R.G., Gulden, R.H., Hart, M.M., Powell, J.R., Klironomos, J.N., Pauls, K.P., Swanton, C.J., Trevors, J.T., Dunfield, K.E. 2007. Cycling of extracellular DNA in the soil environment. Soil Biol. Biochem. 39:2977-2991.

Linares, J.F., Gustafsson, I., Baquero, F., Martinez, J.L. 2006. Antibiotics as intermicrobial signaling agents instead of weapons. PNAS 103(51):19484-19489.

Martínez, J.L. 2008. Antibiotics and antibiotic resistance genes in natural environments. Science 321:365-367.

Mater, D.D.G., P. Langella, G. Corthier and M.J. Flores. 2005. Evidence of vancomycin resistance gene transfer between enterococci of human origin in the gut of mice harbouring human microbiota. J. Antimicrob. Chemoth. 56:975-978.

Mazel, D., Davies, J. 1999. Antibiotic resistance in microbes. Cell. Mol. Life Sci. 56:742-754.

McCuddin, Z.P., Carlson, S.A., Rasmussen, M.A., Franklin, S.K. 2006. Klebsiella to Salmonella gene transfer within rumen protozoa: Implications for antibiotic resistance and rumen defaunation. Vet. Microbiol. 114:275-284.

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Nielsen, K.M., van Elsas, J.D. 2001. Stimulatory effects of compounds present in the rhizosphere on natural transformation of Acinetobacter sp. BD413 in soil. Soil Biol. Biochem. 33:345-357.

Nwosu, V.C. 2001. Antibiotic resistance with particular reference to soil microorganisms. Res. Microbiol. 152:421-430.

Onan, L.J., LaPara, T.M. 2003. Tylosin-resistant bacteria cultivated from agricultural soil. FEMS Microbiol. Lett. 220:15-20.

Sengeløv, G., Agersø, Y., Halling-Sørensen, B., Baloda, S.B., Andersen, J.S., Jensen, L.B. 2003. Bacterial antibiotic resistance levels in Danish farmland as a result of treatment with pig manure slurry. Environ. Int. 28:587-595.

Séveno, N.A., Kallifidas, D., Smalla, K., van Elsas, J.D., Collard, J-M, Karagouni, A.D., Wellington, E.M.H. 2002. Occurrence and reservoirs of antibiotic resistance genes in the environment. Rev. Med. Microbiol. 13:15-27.

Snyder, S.A., Benotti, M.J. 2010. Endocrine disruptors and pharmaceuticals: implication of water sustainability. Water Sci. Technol. 61(1):145-154.

Stotzky, G., 2000. Persistence and biological activity in soil of insecticidal proteins from Bacillus thuringiensis and of bacterial DNA bound on clays and humic acids. J. Environ. Qual. 29:691-705.

Tomasz, A. 2006. Weapons of microbial drug resistance abound in soil flora. Science. 311:342-343.

USDA. 2000. National Organic Program. § 205.203 Soil fertility and crop nutrient management practice standard. http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&sid=656dcbfeadfbdcefa834937bcc8e5379&rgn=div8&view=text&node=7:3.1.1.9.32.3.354.4&idno=7 (accessed 11/17/10).

Vidaver, A.K. 2002. Uses of antimicrobials in plant agriculture. Clin. Infect. Dis. 34(Suppl. 3):S107-S110.

9

Chapter 1

Impact of Land Applied Organic Wastes on Soil Properties Related to the Proliferation of Antibiotic Resistance in Soil

ABSTRACT

With the trend towards organic farming, a wide variety of wastes are increasingly

applied to land without information regarding their lasting effects on the soil ecosystem.

The most commonly utilized waste is animal manure, although many non-traditional

organic waste amendments are currently also in use. The effects of these wastes vary and

may alter soil properties associated with antibiotic resistance (AR) gene proliferation in

soil, such as organic matter content and pH. This study evaluates the residual effects of

dairy manure applied at moderate rate and the impact of long-term application of a non-

traditional, acidic waste on a suite of physical, chemical and biological soil properties.

Treatments sampled included manure amended (MA), which received manure for four

years ending two years prior to sampling, and unamended control (MC), and soil

amended with cranberry wastes at high (CH) and low (CL, ~10% CH) rates.

Manure amendment resulted in significantly (p < 0.05) higher values for water

retention, cation exchange capacity (CEC), soil organic matter (SOM), P, K, Mg, and

NO3-N versus the control. While dehydrogenase activity followed a similar trend,

principal component analysis (PCA) of metabolic diversity data did not discern between

the two treatments. PCA of all data showed significant separation, with undisturbed

water retention and a diversity index, H’, most highly correlated to PC1, indicating the

physical characteristics of these soils largely dictate the biology. Several soil properties,

including CEC, SOM, water retention, pore space distribution, and P indicate the

10

residence time of naked DNA in the MA soil would likely increase, thereby increasing

the likelihood of transformation in this soil. Additionally, increased dehydrogenase

activity in the MA soil may signify an enhanced possibility of lateral gene transfer via

conjugation as a result of greater interaction between cells.

Conversely, the CH soil showed negative impacts of high rate amendment with

highly significantly lower pH (p<0.00005), as well as significantly lower (p<0.05)

extractable nutrients, water content at various pressure potentials, and dehydrogenase

activity versus low rate amendment. PCA of all data indicated that low pH and soil

texture, as reflected in disturbed water content, were likely determining the biology of the

CH soil. Since low soil pH has been shown to increase the binding of naked DNA to soil,

these findings could have implications in the spread of antibiotic resistance genes in the

soil environment; however, overall, the data suggest high rate amendment would likely

not promote AR proliferation.

These data together indicate that organic amendment of soil may have a lasting

impact on factors contributing to the rate of horizontal gene transfer and the persistence

of naked DNA in soil and may, therefore, increase the likelihood of spread of AR genes

in the soil environment. At the same time, the correlation of H’ to PC1 in the analysis of

all MA/MC data despite the lack of significant difference in any individual measure of

microbial diversity shows the need for an improved method of analysis of metabolic

diversity data.

11

INTRODUCTION

The popularity of certified organic foods is increasing dramatically with

organically managed land use increasing 15% annually from 2002 to 2008 (USDA,

2010). As a result, a variety of waste products, including manures and food processing

wastes, are increasingly being utilized as plant nutrient sources and soil conditioners.

Proper management of these wastes maximizes their economic benefits by supplying

nutrients and organic matter to the soil, maintaining soil fertility, and improving soil

structure while minimizing adverse environmental impacts (Loehr, 1977; Unger, 1994).

The effects of these waste amendments can be seen in a variety of physical, chemical and

biological soil properties. Wastes such as manure often supply large amounts of organic

matter to the soil which may impact water retention capabilities (Arriaga and Lowery,

2003), wet aggregate stability (Wortmann and Shapiro, 2008), nutrient status

(Habteselassie et al., 2006), pH and buffering capacity as well as microbial biomass

(Peacock et al., 2001) and activity (Garcia-Gil et al., 2000).

Due to their varying chemical and physical characteristics, the impacts of acidic

food processing wastes on the soil system are not as well understood as those of manures.

Areas across the globe produce a variety of acidic wastes, which include plant materials

(citrus, sugar beet, sugarcane, pineapple, cranberry) as well as processing wastes (from

the production of olive/vegetable oil, wine, cottage cheese). Land application of these

low pH waste products could negatively affect the soil pH and related soil properties such

as nutrient availability and microbial activity (Croker et al., 2004, Bustamante et al.,

2007, López-Piñeiro et al., 2008).

12

The practice of land application of wastes to improve soil quality may also impact

the incidence of antibiotic resistance (AR) in the soil bacterial community. Soils have

been found to harbor abundant and diverse antibiotic resistance (AR) gene pools

(D’Costa et al., 2006). Animal husbandry practices that include prophylactic and growth

promoting use of antibiotics could increase the incidence of resistant enteric species in

livestock, which would also be present in the wastes these animals produce. Thus,

through the land application of manures, there is the possibility of spread of AR genes

from resistant enteric bacteria to environmental strains. Any unmetabolized antibiotics

present in the wastes also enters the soil environment. This localization of both resistance

genes and selective pressure encourages the dissemination and acquisition of AR genes

(Aminov & Mackie, 2007). Subtherapeutic doses of antibiotics in animal husbandry

affect the abundance and types of resistant bacteria in nearby soil (Onan and LaPara,

2003). Additionally, once introduced, there may be little or no cost to maintaining

resistance genes within the new host cell (Andersson and Levin, 1999).

Land application of wastes may also lead to the proliferation of AR genes in the

soil environment through non-inherited or horizontal gene transfer (HGT). Multiple

studies have found that excessive land application of manure leads to the spread and

possible persistence of AR genes in soil bacteria (Sengeløv et al., 2003, Ghosh and

LaPara, 2007). Transfer of genes in soils have been found to depend upon soil physical

and chemical properties including soil pH (Levy-Booth et al., 2007), available water (Lee

and Stotzky, 1999), plant exudates present (Nielsen and van Elsas, 2001) and soil

geometry (Massoudieh et al., 2007). It is known that soil-bound DNA is not only

biologically active but also protected from DNases (Stotzky, 2000, Demanèche et al.,

13

2001) and, therefore, has a longer soil residence time. Additionally, any antibiotics

introduced to soil via contaminated manure could, likewise, be bound yet retain

bioactivity (Chander et al., 2005), adding selective pressure. Heavy metal contamination,

associated with both manure and sewage sludge (biosolids) amendment (Nicholson et al.,

2003), can also apply selective pressure, as metal and antibiotic resistance genes are often

found on the same genetic elements. Acidification of soil that could result from land

application of acidic wastes could alter the mobility of heavy metals as well as increase

the binding of naked DNA to the soil matrix, thereby increasing the likelihood of

transformation of AR genes (Levy-Booth et al., 2007).

Factors affecting AR transfer in soils may be impacted long after land application

of the waste ends. Few studies exist in the literature that report on the residual effects of

manure application after such application had ceased and those that do include limited

soil parameters. For example, Freschet et al. (2008) reported that goat and sheep manure

significantly increased basal respiration and microbial biomass in corral soil four years

after manure application but insignificantly by six years. Conversely, Indraratne et al.

(2009) modeled the recovery time (to pre-manure levels) for a variety of chemical

properties in soil receiving 14 years of manure application and reported a 17 to 99 year

recovery time for total nitrogen, total phosphorous and extractable P, while NO3-N and

electrical conductivity were estimated to take up to 297 years to return to pre-manure

levels in rain-fed fields. Although it is expected that soil biological properties will return

to some status quo more quickly than other properties, it is these physical or chemical

properties that may be determining the propagation of antibiotic resistance in the soil

ecosystem.

14

The objective of this study was to assess the residual impact of dairy manure

amendment two years after amendment ceased and the impact of long-term application of

acidic cranberry wastes on a variety of soil physical, chemical, and biological properties

in order to provide a background for the assessment of the potential impact of these soil

amendments on the proliferation of antibiotic resistance in the soil ecosystem.

MATERIALS AND METHODS

Site Descriptions

The surface 10 cm of soil at the Snyder Research and Extension Farm (Pittstown,

New Jersey) were sampled on May 10, 2000. The soil at this site is a silt loam. The

sampled plots were under chisel tillage from 1991 to 1999, and planted to a rye cover

crop in 1994. In 1995, a randomized complete block design experiment was established

with four replicates of four treatments including: 1) control (MC); 2) dairy manure

amendment (MA); 3) nitrogen sidedress amendment; and, 4) amendment with both

manure and sidedress nitrogen (Singer et al., 2000). For the current study, the first two

treatments (MC and MA) were sampled. Amended (MA) plots received 22.6, 13.3, 15.0

and 5.5 Mg ha-1 dry matter of dairy manure in 1995, 1996, 1997, and 1998, respectively.

Manure was incorporated with a chisel plow within a day of application. At planting, all

plots were fertilized with 23 kg ha-1 nitrogen, 10 kg ha-1 phosphorous, and 19 kg ha-1

potassium. In 1999, manure amendment ceased and a no-till system was adopted. The

plots were planted to corn from 1995 through 1998, the years of manure amendment, and

to soybean in 1999 (Singer et al., 2000; Singer and Heckman, 2003). Corn stover was

15

returned to all plots in 1998 only and was still visible on the soil surface at the time of

sampling.

On May 11, 2000, a fine sandy loam soil was sampled from a small commercial

farm, located near Chesterfield in central New Jersey. Since 1988, cranberry processing

residuals (skins, seeds and discarded whole fruit) were applied to fields on the farm, in

varying amounts. The farmer received the waste from a food-processing facility during

the months January through March and July through September. Occasionally, sludge

from an aerated lagoon at the processing facility was also applied. Spreading of these

amendments on the fields was done using a manure spreader and typically occurred

within one day of receipt. However, the amendment was only incorporated into the soil

during pre-planting or post-harvest tilling in Spring and Fall. Additionally, other wastes

had been applied to the fields in varying amounts, including municipally-collected leaves

and, most recently, pelletized biosolids from a county utility authority. The pelletized

biosolids had been applied to the fields for approximately two years prior to sampling.

Cranberry waste was applied in a specific field order, starting at the same field each

delivery and continued until all waste was applied. The two fields sampled included the

first and last fields in the order of application. Since the amount of waste per delivery

varied, two amendment rates were represented: high (CH), which received waste at each

delivery time, and low (CL), which received approximately 10% of the waste applied to

CH, as per the farmer. The municipal leaves and pelletized biosolids were applied in the

opposite direction to the cranberry wastes. Hence, the CL treatment received the majority

of the leaves and biosolids. Chemical fertilizer was applied to both fields in accordance

with pre-planting soil test recommendations each year. No lime was applied to either

16

field, as per soil test results obtained by the farmer. Both fields were planted in a corn-

soybean-wheat rotation and were planted to wheat at the time of sampling.

Sampling Procedure, Sample Storage and Preparation

Sampling occurred prior to planting at the Snyder Farm while the plots were

fallow and when wheat plants were approximately 1.2 m tall on the Chesterfield farm. At

the Snyder Farm, six 3-liter samples were taken at random from each of the four replicate

plots of the control (MC) and manure amended (MA) treatments. From each plot, three

fist-sized clods were also collected, tagged and dipped in a 1:4 dilution of Saran (Dow

Chemical, Midland, MI) in the field. At the Chesterfield farm, samples were taken along

a diagonal transect of each field. Eight 1-m2 areas, spaced at least 3 m apart, were

selected and sampled along each transect. In each area, three 2-liter random samples were

collected along with clods as described above.

All samples were stored on ice for transport to the laboratory, where the three

replicate samples from each area were combined into composite samples (eight per

treatment per site), then stored at 4 °C. All biological assays were performed within two

days of soil collection. All physical and chemical properties were assayed within five

months of collection, except for disturbed water content. Soils were passed through a 2

mm sieve prior to each assay. In the laboratory, soil clods were again dipped in a 1:4

dilution of Saran, then twice in a 1:7 dilution before refrigeration.

17

Physical Properties

The Saran coating method was used to determine bulk density (ρb) of 12 soil

clods (National Soil Survey Center, 1996). After final Saran coat, clods were allowed to

air dry at least two weeks until constant mass was reached. Dry clods were weighed in air

and in water. Rock fragments were then removed from the clods and weighed to correct

the measured ρb, which was calculated using the equation:

)3.1MPC()PDRF(WMCW2CCTAGMPCRF2CC

b −−−−−−

=ρ (1)

where RF and PD are the weight and density of rock fragments, respectively; and CC2,

MPC, and TAG, are the weights of a coated clod in air, of Saran coats, and of tag + wire,

respectively, and WMCW is the weight of coated clod in water.

Following measurements of ρb, soil water retention was measured in 8 randomly

selected clods, using a combination of closed chambers, lined with a 35 μm nylon mesh,

connected to hanging columns (-0.1, -0.5, -1.0, -2.0, -5.0 kPa), and pressure plate

extractors (-10, -30, -100 kPa) (Soil Moisture, Santa Barbara, CA). Water retained at a

pressure potential of -0.1 kPa was assumed to represent saturated water content. Saturated

clods were then equilibrated at increasingly lower pressure potentials and the volume of

water released from the clods (hanging column) or change in clod weight (pressure plate

extractor) was recorded. After the -100 kPa measurement, clods were oven-dried and

massed. The gravimetric water contents (kg kg-1) at equilibrium with the various pressure

potentials were then back calculated.

Water retention was also measured on disturbed soil (sieved through a 2 mm sieve

and packed in rings 5 cm ID X 0.5 cm H) using the same combination of hanging

18

columns (-0.5, -1.0, -2.0, -5.0 kPa) and pressure plate extractors (-10, -30, -60, -100, -

300, -700, -1500 kPa). Four and eight replicates of disturbed soil were measured with the

hanging columns and pressure plate extractor, respectively. Particle size distributions

were determined in triplicate on pairs of samples (4 unique samples per treatment per

site) using dry sieving (2, 1, 0.5, 0.25, 0.106 mm) and the pipette method (0.053, 0.025,

and 0.002 mm) (ASTM, 1999).

Chemical Properties

Soil samples were sent to the Rutgers Soil Testing Laboratory for measurement of

chemical properties including pH (McLean, 1982), Mehlich-3 extractable nutrients

(Mehlich, 1984), organic carbon using a modified Walkley-Black method (Bear, 1955,

Storer, 1984) from which soil organic matter was calculated assuming 58% organic

carbon, cation exchange capacity (Jackson, 1958), total Kjeldahl nitrogen (Bremmer,

1965) and inorganic nitrogen (Kamphake et al., 1967, Bolleter et al., 1961).

Biological Properties

Potential dehydrogenase activity was measured according to the method of Tate

and Terry (1980). Briefly, 2, 3, 5-triphenyltetrazolium chloride (TTC) was add to soil in

test tubes, which was then submerged using sterile DI water, mixed thoroughly and

incubated for 16 hours at 30°C. Reduced TTC (triphenyl formazan (TPF)) was filter

extracted from each test tube using methanol. The color of the filtrates, including blanks,

were then measured at 485 nm using a Lambda 3 UV/VIS spectrophotometer (Perkins-

Elmer, Norwalk, CT) and the amount of TPF calculated using a standard curve.

19

Metabolic diversity was assessed with the BiOLOG method using the EcoPlate™

(San Miguel et al., 2007) using 10 mM phosphate buffer. This plate was designed for

microbial community analysis through sole carbon source utilization patterns, using 31

substrates including carboxylic acids, carbohydrates, amino acids, polymers,

phosphorylated compounds and amines. Of the 31 substrates, 25 are found on the

BiOLOG GN plate, specific for gram-negative bacteria,19 are found on the GP (gram

positive) plate, 19 are found on the AN (anaerobe) plate and 18 are found on the FF

(filamentous fungi) plate. Only three substrates are unique to the EcoPlate™. Plates were

read every 2-4 hours beginning after 16 hours of incubation. The optical density (OD) of

the control well (no substrate) was subtracted from the other wells to correct for any

organic matter in the inoculum that might cause color development in the wells. Negative

corrected values were assumed to indicate no usage and were zeroed. Additionally,

values ≤ 0.02 absorbance units (AU) were also assumed to be zero, based on the accuracy

of the plate reader. The average well color development (AWCD) was then calculated for

each plate (Garland and Mills, 1991). An AWCD of 0.25 AU was selected for

comparison purposes. All 8 field replicates per treatment were assayed, in triplicate;

hence, 24 sets of OD data were obtained for each treatment.

Data Analysis - Metabolic Diversity

Optical density data were analyzed with indices used to characterize microbial

diversity, and species richness and evenness. The Shannon-Weaver index of diversity

(H’) was calculated as (Sharma et al., 1998):

∑−= )plog(pH ii' (2)

20

where pi is the ratio of the corrected optical density of a well to the sum of optical

densities of all wells. This index was calculated across each plate and for each of the six

substrate types, resulting in seven unique values for each sample. Richness, S, defined as

the number of different substrates that were used by the microbial community (an

estimate of the number of species present in a community), was determined by counting

the number of wells with positive OD. Evenness, E, a measure of the equitability of

activity across all substrates (an estimate of how equally abundant the species are in a

soil) was calculated as:

SHE ln/'= (3)

In addition, treatments at each site were analyzed for similarity using the

Sorensen similarity index estimated as (Nakatsu et al., 2000):

)+Sj/(S = C bas 2 (4)

where Cs is the Sorensen similarity index, j is the number of common substrates used by

the microbial communities of both treatments, Sa and Sb are the average richness for

samples from treatment A and B, respectively.

Statistical Analyses

One-way analysis of variance (ANOVA) was used to investigate treatment effects

on physical, chemical and biological properties. Significance was determined at α=0.05,

unless otherwise noted. Optical density (OD) values were also compared using ANOVA.

Principal component analysis (PCA) was used to simultaneously analyze the OD

data. Visual comparison of the principal component (PC) scores was performed to

21

determine separation between treatments. ANOVA was run to confirm the significance

of differences in the principal component scores.

PCA was also employed to assess overall differences between the two treatments.

All measured data and calculated indices were input to PCA except undisturbed water

retention at -0.5, -1, -2 and -5 kPa, since measurements were not obtained for all 8

samples per treatment. The factor pattern obtained from PCA was used to determine

which properties were most correlated to the axis of separation. Statistical analyses were

run with the software package SPSS v17.0 (SPSS Inc., Chicago, IL).

RESULTS AND DISCUSSION

Manure Amendment

Average soil characteristics are presented in Table 1. Soil organic matter (SOM) was

significantly higher (11%) in MA vs. MC, although undisturbed bulk density (ρb) did not

differ between the two. This discrepancy is likely due to the adoption of a no-till system.

It has been previously shown that SOM can remain elevated after cessation of manure

amendment (Maryniuk et al. 2002). Cation exchange capacity (CEC), a property related

to SOM, was likewise significantly higher in MA. Despite the significant difference in

SOM between the treatments, there was no difference in C/N ratio. Additionally, no

difference was seen in pH.

The amounts of available P, K and Mg in MA increased between 36% and 67%

over MC (Table 1), with phosphorous the most increased. Our findings are in agreement

with those of Hao et al. (2008), that found elevated P levels 16 years after long-term

22

Table 1 – Average Soil Characteristics – Manure Amended and Control

MA MC Significance α = 0.05 (*), 0.005 (**),

0.0005 (***)

Physical Bulk Density- (g/cm3)

Disturbed 1.11 1.14 Undisturbed 1.50 1.50

Texture† % Sand 21.46 20.84 % Silt 60.54 62.83 % Clay 15.81 15.55

Chemical

pH 7.43 7.40 CEC (mequiv/100 g soil) 10.48^ 10.07 *** Nutrients (mg/kg soil)

P 145 86 *** K 416 295 *** Mg 256 186 *** Ca 2170 2261 Cu 3.75 4.40 Mn 98.43 129.54 Zn 7.83 6.20 B 1.43 1.43 NO3-N 26.25 20.25 *** NH4-N 9.38 10.38

TKN (%) 0.27 0.25 SOM (%) 4.12 3.70 * C/N ratio 8.70 8.82

Biological H' (across the entire EcoPlate™) 1.271 1.283 H'carb 0.681 0.687 S 26 26 E 0.386 0.394 Cs 0.964 Dehydrogenase (μg TPF/g soil) 476.90 345.95 **

† Pairs of samples were combined for particle size distribution measurements ^Represents the average of 7 measurements

23

manure amendment had ceased. These results may be accounted for by large inputs of

these nutrients with amendment or by higher CEC value for the amended soil indicates

an increased capacity of the MA soil to retain and exchange cations.

Potential dehydrogenase activity, a surrogate measure of microbial respiration,

was significantly elevated in MA (Table 1), indicating either a larger or more active

microbial community in MA compared to MC. Long-term manure application has been

shown to increase both microbial biomass and dehydrogenase activity (Garcia-Gil et al.,

2000, Chu et al., 2007). Such an effect can be found years after application ceased

(Martyniuk et al., 2002). Enwall et al. (2007) found a strong correlation between basal

respiration, a measure of biomass, and organic C content. Thus, one explanation for our

findings is the higher SOM in MA (Table 1).

Gravimetric water contents were significantly higher at the same pressure

potentials in the undisturbed samples of MA than those of MC (Fig. 1). This was true at

all pressure potentials from -0.1 kPa to -100 kPa. The level of significance increased with

decreasing pressure potential: p = 0.028 for -0.1 kPa (assumed to be saturation); p ≤ 0.01

in the range from -0.5 to -2 kPa; and p ≤ 0.001 in the range from -5 to -100 kPa. This

finding is in agreement with that of Nyamangara et al. (2001), who reported that manure

amendment had both immediate and residual effects on soil structure, increasing water

retention at low suctions. Furthermore, Kirchmann and Gerzabek (1999), working in a

long term manure addition experiment, found that organic carbon was positively

correlated to the amount of water retained between pressure potentials of -5 and -300

kPa.

24

Log of Potential Pressure, kPa

100 101 102 103

Wat

er C

onte

nt, g

/g

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Fig. 1 - Water Retention Curve of Disturbed Samples (MA=Δ, MC=▼) and Undisturbed Soils (MA=○, MC=●).

25

Disturbed water retention is a measure of soil texture and should be unaffected by

land management practices. However, disturbed soil from MA treatment tended to retain

between 3% and 8% more water at low suctions than MC soil (Fig. 1), despite a lack of

difference in textural properties between the two treatments (Table 1). Differences were

close to statistical significance at pressure potentials -0.5 kPa (p=0.052) and -1 kPa

(p=0.05) and was statistically significant at -5 kPa (p=0.043) and -10 kPa (p=0.038).

Hudson (1994) showed that water retention increases with soil organic matter,

particularly for soils rich in silt. Given the high silt content of these soils, the tendency of

greater water retention by MA samples could be related to a significantly greater level of

SOM in the manure treatment (Table 1).

Soil structure can be informative for soil microbial analyses because soil pores are

the site of most biogeochemical processes. As little as two years of dairy manure

application has been found to increase total porosity (Fares et al., 2008). For the

biological functioning of a soil, the habitable pore space (pores with diameters between

0.8 and 30 μm) and protective pore space (pores with diameters between 0.8 and 3 μm)

are the most important. Habitable pore space promotes the establishment and survival of

bacterial cells whereas protective pore space is accessible to bacteria but not their

predators. Thus, protective pore space is a subset of habitable pore space. Bacterial

biomass has been positively correlated to protective pore space (Hassink et al., 1993,

Schjønning et al., 2002). Postma and van Veen (1990) suggested that water retention data

be used to determine the volume of protective and habitable pore space in soils. Organic

carbon content has been positively correlated to the amount of water retained between

pressure potentials of -5 and -300 kPa (pores with diameters between 1 μm and 60 μm),

26

with the best correlation in the range from -60 to -300 kPa (pores diameters between 1

μm and 5 μm) (Kirchmann and Gerzabek, 1999), suggesting organic matter increases the

amount of protective pore space.

Pore size distributions of structured soils are the result of both the structural and

textural characteristics of a soil. Thus, proper assessment of the impact of organic

amendments on pore size distribution requires isolating structural (mineral +

management) and textural (mineral only) effects. This was done by measuring water

retention properties of soil clods (undisturbed soil), representing structural effects, and on

disturbed (sieved and re-packed) samples, representing textural effects on water retention.

These data were input into the equation for frequency of radius (f(r)) (Daniel Gimenez,

personal communication):

)2

)]/[ln(exp(2

)( 2

2

σπσmrr

rrf −

Φ=

(5)

to estimate pore size distributions for the undisturbed soils separately and for the

disturbed soils combined (Fig. 2).

Greater porosity in the disturbed soil throughout the range of habitable pore space

is mainly the result of soil being packed to a bulk density lower than field values. Soil

that was amended with manure had a greater percentage of habitable pore space and, in

particular, protective pore space, which is likely the result of greater SOM content

(Kirchmann and Gerzabek, 1999). The significant differences in undisturbed water

content between the two treatments confirm the results of the pore space modeling.

Increased habitable and protective pore spaces in the MA soil may explain the greater

27

Fig. 2 – Pore Size Distributions of Manure Amended and Control Soils as Estimated from Water Retention Data Using Eq. 5.

28

microbial activity and/or biomass associated with this treatment, as suggested by

potential dehydrogenase activity (Table 1).

Metabolic diversity was assessed using the BiOLOG EcoPlate™ and compared at

an average well color development (AWCD) of 0.25 AU. When the optical density (OD)

data were analyzed in ANOVA, utilization of the substrates were not found to differ

significantly between the microbial communities associated with the manure amended

and control soils. This is in contrast to the findings of Zhong et al. (2010) who reported

significant substrate utilization pattern differences between soil receiving long- term

composted pig manure amendment and an unamended control. Several variables between

the two studies may account for the contrasting results including: type of manure, length

of time of amendment, cessation of amendment (in the case of the current study), as well

as differences in the metabolic diversity analysis (single time point vs. single AWCD

point).

The OD data obtained from the metabolic diversity assay were input to Principal

Component Analysis (PCA). Graphing the resulting first and second principal

components (PC1 and PC2) showed separation of the MA and MC samples along PC1

(Fig. 3); however, PCA provides no information on how the communities differ. At the

same time, no significant difference was seen between the calculated indices for MA and

MC including substrate richness (S) and evenness (E). Additionally, the Cs value for

these treatments was 0.964, with 1.0 being perfect identity (Table 1). Thus, the cause of

separation along PC1 is unknown from these analyses.

The literature contains contradictory findings regarding bacterial community

diversity and manure amendment. While Bucher and Lanyon (2005) found clear

29

Fig. 3 – PCA of Metabolic Diversity Data from Manure Amended (MA=○) and Control (MC=●) Soils.

30

separation of metabolic diversity data from manured and non-manured Pennsylvania farm

soils, and Toyota and Kuninaga (2006) reported a shift in the microbial community

structure in response to 10 years of manure inputs, Chu et al. (2007) reported that long-

term manure application did not result in a shift in the microbial community through the

introduction of new species rather it enhanced segments of the indigenous bacterial

population. Our results are not surprising considering that, in the two years since

amendment ceased, both treatment plots were converted to a no-till system and had

received the same carbon inputs. Since bacteria are often metabolically diverse and the

soil communities show functional redundancy, metabolic diversity may not show changes

in bacterial community structure. While the communities associated with MA and MC

may be functionally similar, they may differ structurally.

In order to evaluate the overall impact of waste amendment on the soil ecosystem,

all data were input to Principal Component Analysis (PCA). The properties used included

the physical variables: undisturbed water retention at pressure potentials between -0.1 and

-100 kPa, disturbed water retention at pressure potentials between -10 and -1500 kPa, and

ρb; the chemical variables: pH, soil organic matter (SOM) content, C/N ratio, cation

exchange capacity (CEC), available nutrients as listed in Table 1, and inorganic and total

nitrogen levels; and the biological variables: dehydrogenase activity, H’, S, E and the

optical densities of all substrate wells at the time point when the average well color

development (AWCD) was closest to 0.25 absorbance units. A total of 65 variables were

used for the analysis (one BiOLOG substrate was not utilized by either the MA or MC

community and was excluded).

31

The variance accounted for by the first two principal components totaled 40.40%.

Although some overlap exists, significant separation of the treatments occurred along

PC1 (p=0.003), which accounted for 25.69% of the variance within and between the data

(Fig. 4). All undisturbed water retention measurements were highly correlated (> |0.750|)

to PC1, as were disturbed water retention at -700 kPa and H’ (Table 2), with many of the

nutrients measures somewhat less correlated (<|0.750| - |0.650|). Only the biological

properties were negatively correlated to PC1, indicating an inverse relationship with

those properties highly positively correlated to PC1. Thus, as water retention and

nutrients increase, the Shannon-Weaver index of diversity would decrease, as expected.

Plentiful water and nutrients would favor a few fast growing generalist members of the

microbial community, which would outcompete slower growing specialists. Thus, in a

stressed situation of low water and nutrient availability, greater diversity would be seen.

The variance accounted for by PC2 was 14.72%; however, no significant separation

occurred along this PC (p=0.280). The results of this analysis confirm that the physical

structure of the manure amended soil, with its increased habitable and protective pore

space, was determining the size and/or activity of the microbial community.

These findings, overall, imply manure amendment could enhance the occurrence

of antibiotic resistance in receiving soils. Antibiotics can diffuse into organic matrices

where it is protected from degradation (Sithole and Guy, 1987). Likewise, DNA is known

to bind to humic acids (Crecchio and Stotzky, 1998). Hence, both antibiotics and DNA

would have longer residence times in MA due to higher SOM. Residence time of

antibiotics in soils have been found to be dependent on CEC, which can affect sorption

32

Fig. 4 – PCA of All Data – Manure Amended and Control Soil (MA=○, MC=●).

33

Table 2 – Correlations of soil properties to PC1 for all data – manure amended and

control soils

Correlation Range Variables correlated to PC1

≥ |0.900| undisturbed water retention at -100 kPa

< |0.900| – |0.800| undisturbed water retention at-0.5, -1, -2, -5 and -30 kPa

< |0.800| – |0.750| undisturbed water retention at-10 kPa, disturbed water retention at -700 kPa, H’

< |0.750| – |0. 700| Zn, Mg , NO3-N, L-phenylalanine

< |0.700| – |0. 650| P, TKN, SOM, disturbed water retention at -100 kPa, E

Bolded variables are significantly different between treatments (α = 0.05). Italicized variables are negatively correlated to PC1

34

and pore water concentrations of these compounds (Boxall et al., 2002, Heuer et al.,

2008). DNA can account for a large proportion of extractable P, with one study finding

up to55%of extractable P from surface soil in the form of DNA (Turner and Newman,

2005). Thus, DNA in manure or bound by organic matter could also account for the

elevated P in MA. Thus, the elevated dehydrogenase activity in MA may also explain the

higher extractable P present. These findings together suggest greater interaction between

bacteria is likely in the MA soil and that more naked DNA may be present in this soil;

hence, a greater potential exists for genetic exchange. Pore size distribution may also play

a role in the exchange of genetic material in soils by concentrating microbial activity in

protective pores, thereby increasing interaction between cells. Thus, while little

difference in metabolic diversity was seen between MA and MC, the AR profiles of the

two soils may also differ, although this was not actually determined.

Cranberry Amendment

Although the sand, silt and clay fractions did not differ significantly between the

two cranberry application rates (Table 3), texture was the overriding determinant of

physical properties in both CH and CL. The CL soil had a greater percentage of gravel (>

2.0 mm, p=0.004) and coarse sand (between 0.5and 2.0 mm, p=0.027) while CH had a

greater percentage of very fine sand (between 0.05 and 0.15 mm, p=0.020) (Table 4).

Bulk density (ρb) was significantly lower in the undisturbed CH samples when compared

to the undisturbed CL (Table 3), likely due to the particle size differences between the

two treatments.

35

Table 3 - Average Soil Characteristics – Cranberry Amended Soils

Cranberry Waste Significance α = 0.05 (*), 0.005 (**),

0.0005 (***), 0.00005 (****)

CH CL

Physical

Bulk Density- (g/cm3) Disturbed 1.02 1.13 Undisturbed 1.35 1.48 **

Texture† % Sand 60.69 65.00 % Silt 20.18 20.19 % Clay 19.13 14.81

Chemical pH 5.51 6.58 **** CEC (mequiv/100 g soil) 25.78 26.52 Nutrients (mg/kg soil)

P 98 141 * K 290 369 * Mg 550 726 *** Ca 1868 2554 *** Cu 1.49 1.91 ** Mn 7.04 7.29 Zn 6.38 8.96 ** B 0.98 1.19 * NO3-N 11.63 15.00 NH4-N 8.88 9.00

TKN (%) 0.22 0.21 SOM (%) 3.22 3.40 C/N ratio 9.85 8.33

Biological H' (across the entire EcoPlate™)

1.281 1.302

H'carb 0.701 0.661 *

S 28 25 E 0.388 0.404 * Cs 0.912 Dehydrogenase (μg TPF/g soil)

16.31^ 29.25 ***

† Pairs of samples were combined for particle size distribution measurements ^ Represents the average of 7 measurements

36

Table 4 – Gravel and Sand Distribution – Cranberry Amended Soils

Cranberry Waste Significance α = 0.05 (*), 0.005 (**),

0.0005 (***), 0.00005 (****)

CH CL

% Gravel 1.54 0.007 **

% Coarse 9.38 8.43 *

% Medium 31.40 28.51

% Fine 14.79 14.56

% Very Fine 8.43 9.19 *

37

Soil pH was significantly lower in the CH field, which received the majority of

the cranberry processing residuals (pH ≈ 3) (Table 3). Although the cranberry waste

producer did provide the farmer with a liming allowance, no lime was applied based on

soil test results obtained by the farmer. The impact of land application of acidic organic

wastes on soil pH depends greatly on both the initial pH and the buffering capacity of the

soil, with little change when applied to highly buffered, acid soils (Bustamante et al.,

2007, López-Piñeiro et al., 2008). However, a variety of acidic organic wastes have been

found to lower pH from 0.5 to 1.8 pH units (Robbins and Lehrsch, 1992, Croker et al.,

2004, Soda et al, 2006, Rosabal et al., 2007).

Potential dehydrogenase activity showed significant reduction in microbial

activity and/or biomass in CH versus CL (Table 3). This reduction could be caused by

the low pH of soil from the CH field (Kirchmann and Gerzabek, 1999, Enwall et al.,

2007). Our finding supports that of Tejada et al. (2006), who reported a decrease in

microbial biomass C as well as dehydrogenase activity over time with the addition of

acidic sugar beet waste. However, the impact of acidic waste on microbial activity is not

clear, as shown by Bustamante et al., (2007), who reported that while both grape stalk

and grape marc (pH 3.97 and 4.48, respectively) decreased the metabolic quotient of a

sandy soil, exhausted grape marc and wine lee (pH 3.93 and 3.63, respectively) had little

impact or slightly increased this measure.

The bacterial community associated with CH showed greater diversity in its

ability to utilize carbohydrates (H’carb), although substrate evenness (E) was higher in CL

(Table 3), indicating a greater number of generalists in the CL community and greater

heterogeneity in the CH community. Aside from these two, no other index discerned

38

between the amendment rate and the Sorensen similarity index, Cs, was 0.912, suggesting

that the two microbial communities were functionally very similar.

Water retention was significantly greater in the soil clods from CL than those of

CH at pressure potentials close to saturation including: -0.1 (p=0.011) and -0.5 kPa

(p=0.050), and, to a lesser confidence, -1 kPa (p=0.099) (Fig. 5). No difference was seen

between the water contents of soil clods from the two treatments at any pressure potential

between -2 and -100 kPa. While undisturbed water retention reflects both textural and

management effects on soil structure, the SOM and ρb data would suggest that the effect

was mainly textural. This is also true of the differences seen in disturb soil water

retention values between -70 and -300 kPa.

The pore size distribution of the disturbed soil, as calculated by Eq. 5, is typical

of a coarse textured (sandy) soil (Fig. 6), with a protective pore fraction that is small or

absent. This corresponds to small microbial biomass and low biological activity

(Sessitsch et al., 2001), as was seen with these soils. Despite significantly different

dehydrogenase values (Table 3), differences in the range of habitable pore space between

undisturbed CH and CL soils may not be real given that gravimetric water contents were

not significantly different between treatments in the range of pressure potentials

corresponding to these pore sizes.

The metabolic diversity data of CH and CL separated along both the PC1 and PC2

axes (Fig. 7), with the high rate treatment community showing more variability than the

low rate. Thus, the bacterial communities of the soils may have been impacted by the

various waste amendments applied to each treatment. However, Sessitsch et al. (2001)

reported higher diversity associated with smaller soil particle sizes compare to coarse size

39

Log of Potential Pressure, kPa

100 101 102 103

Wat

er C

onte

nt, g

/g

0.1

0.2

0.3

0.4

0.5

0.6

Fig. 5 -Water Retention Curve of Disturbed Samples (CH=Δ, CL=▼) and Undisturbed Soils (CH=○, CL=●).

40

Disturbed

CH undisturbed

CL undisturbed

Fig. 6 - Pore Size Distribution of Cranberry Amended Soils as Estimated from Water Retention Data Using Eq. 5.

41

Fig. 7 – PCA of Metabolic Diversity Data from Soil Amended with Cranberry Waste at

High (CH=○) and Low (CL=●) Rates.

42

fractions. Given that the CL soil had a significantly greater fraction of very fine sand, the

difference in diversity seen may be due to mineralogy and not amendment rate.

Analysis of variance (ANOVA) of optical density (OD) data showed that 15

substrates were utilized significantly differently (α = 0.10). Of these 15 substrates, only

four were utilized to a greater extent by the CH microbial community, including two

carbohydrates and two carboxylic acids, confirming the H’carb results. While one

substrate was unique to the EcoPlate™ (salicylic acid), the other three were represented

on the anaerobic (AN) plate, possibly indicating that high rate amendment with this

acidic waste created a more anoxic environment. Additionally, two of the four substrates

(i-erythritol and salicylic acid) were completely unutilized by the CL community.

To assess the combined impact of the measured physical, chemical and biological

properties, the collected data were input into Principal Component Analysis (PCA) and

analyzed simultaneously. The variables input were as described above. A total of 66

variables were included. Only seven samples from CH were used due to error in one

dehydrogenase reading. All eight samples from CL were used in the analysis.

In the combined PCA, the first two principal components generated for the two

cranberry application rates accounted for 46.17% of the total variance (Fig. 8). The

treatments separated well along PC1 (p < 0.0005), which accounted for 28.35% of the

variance within and between the data. The low amendment rate (CL) soil showed greater

distribution along this PC than did the CH soil. However, the reverse pattern was seen

along PC2, which could account for the lack of significance along this axis. Only

biological properties were highly correlated to PC2.

43

Fig. 8 – PCA of All Data – Cranberry Amended Soils (CH=○, CL=●).

44

Of the properties correlated to PC1 (Table 5), only disturbed water retention,

representative of soil texture, was negatively so, indicating that this property was

inversely related to the chemical and biological properties as well as to undisturbed water

retention. Thus, soil texture, as reflected in disturbed water content, and low pH were

likely determining the biology of the CH soil. These results are in keeping with those of

Bueno et al. (2009) showing long term application of acidic winery wastewater can

negatively affect soil chemical properties.

Although not directly measured, inferences can be drawn on the impact of

cranberry waste amendment on the occurrence of AR in the soil environment. Soil pH

has been reported to control the adsorption of extracellular DNA, with binding occurring

without cation bridging in soils with pH ≤ 5 (Levy-Booth et al., 2007). Thus, the

residence time of naked DNA could be greatly enhanced across the CH field due to

decreased pH. Additionally, low pH could cause protonation of antibiotic compounds

(Chee-Sanford et al., 2009) resulting in greater adsorption and, thus, longer residence

times in soil. This combination of bound antibiotics as well as DNA, together with heavy

metals, such as Zn, which are mobilized at low pH, could potentially exert selective

pressure for the acquisition of AR genes by native soil bacteria. Saturation has been

shown to negatively impact transformation rate of chloramphenicol resistance in soil (Lee

and Stotzky, 1999); thus, transformation of these determinants would be more likely in

the CH soil since less water is retained near saturation.

45

Table 5 - Correlations of soil properties to PC1 for all data– cranberry amended soils.

Correlation Range Variables correlated to PC1

≥ |0.900| pH, Ca

< |0.900| – |0.800| Mg, disturbed water retention at -100 kPa, D-galactonic acid γ-lactone

< |0.800| – |0.750| undisturbed water retention at -0.1 and -0.5 kPa, disturbed water retention at -300 kPa, L-arginine

< |0.750| – |0. 700|

Cu, Zn, B, undisturbed water retention at -1 kPa, disturbed water retention at -70 kPa, D-galacturonic acid, glycogen, Tween 40

< |0.700| – |0. 650| undisturbed water retention at -2 kPa

Bolded variables are significantly different between treatments (α=0.05). Italicized variables are negatively correlated to PC1.

46

CONCLUSIONS

As expected, amendment with manure at moderate rate had a residual positive

impact on soil quality, as evidenced by increased SOM content, CEC, macronutrient

availability, water retention in undisturbed samples and biological activity over the

unamended control (MC) even two years after amendment had ceased. Principal

component analysis (PCA) of all data showed the Shannon-Weaver diversity index, H’,

to be highly negatively correlated to the axis of separation while undisturbed water

retention was highly positively correlated. These results suggest that soil structure is

driving the microbiology of these soils, an idea reinforce by the presence of more

habitable and protective pore space in the manure amended (MA) soil.

While soil quality improves with manure amendment, so could the opportunity for

bacterial genetic exchange in the MA soil. This is suggested by: 1) increased SOM and

CEC, which would increase antibiotic and naked DNA binding; 2) increased

dehydrogenase activity and protective pore space, which would allow greater interaction

between cells and with bound DNA, and 3) increased extractable P, which may indicate

the presence of more DNA in this soil.

Conversely, amendment with cranberry wastes at high rate had a deleterious

effect on soil chemical properties which, in turn, adversely affected soil biology. This

relationship was particularly clear in the combined PCA that showed high correlation of

soil chemical properties to the axis of separation and high correlation of only biological

properties to PC2. Whether high rate cranberry amendment might encourage or

discourage exchange of genetic material is unanswered. Our findings imply a decreased

likelihood of this occurring given the lower dehydrogenase activity, lower extractable P

47

and decreased water retention capabilities of the CH soil. However, the decreased pH

could enhance DNA binding, and mobilize heavy metals which, together with phenols

present in cranberries, could apply selective pressure to acquire resistance mechanisms

(Keweloh et al., 1989). Furthermore, given the results for pH and water retention, high

rate cranberry amendment may increase stress response, which has been linked to AR

(Aminov and Mackie, 2007) .

The findings of this study suggest that land application of organic wastes could

enhance transfer of genetic material in soils. In particular, manure amendment will have

both an immediate and residual impact on factors contributing to the proliferation and

persistence of antibiotics and DNA in the soil environment. The presence of antibiotics or

resistant organisms in the manure would simply exacerbate the potential. Additionally,

pathogenic enteric organisms could contribute not only resistance genes but also

pathogenicity islands and virulence factors (Burgos et al., 2005) to environmental

strains, potentially increasing the risk of community acquired disease. Thus, intensive

composting, which can lower both antibiotic concentrations and the incidence of

pathogens (Storteboom et al., 2007, Vinnerås, 2007), should be required to ensure that

land applied manure is free of antibiotics and resistant enterics.

A tangential finding of this study was the high correlation of microbial diversity

in the manured and control soils, as measured by H’, to PC1 in the combined analysis of

all data despite a lack of significant difference in this measure between the two

treatments. This result suggests that PCA is not sufficient for comprehensive analysis of

metabolic diversity data and that a new statistical method should be developed for this

purpose.

48

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54

Chapter 2

Direct Comparison of Individual Substrate Utilization from a CLPP Study: A New Analysis for Metabolic Diversity Data

(Published in part: San Miguel, C., Dulinski, M., Tate, R.L. 2007.

Soil Biol. Biochem. 39:1870-1877)

ABSTRACT

For over a decade, community level physiological profile (CLPP) assays, which

assess a microbial community’s capacity to metabolize specific sole carbon sources under

defined laboratory conditions, have been popular for study of environmental soil samples.

One such assay, BiOLOG™ allows for the colorimetric measurement of metabolism

through the reduction of a tetrazolium dye, which yields optical density (OD) data for

each substrate. Bacterial communities are extracted from soil and 150 μL of this extract is

inoculated directly into each well of the microtitre plate. The combined metabolic data

obtained are most often analyzed with multivariate statistical analyses, such as Principal

Component Analysis (PCA). The objectives of this study were 1) to develop a simple,

visual, statistically valid method of determining community capabilities to utilize specific

substrates in CLPP studies and 2) to test the number of samples needed for such

discrimination to be reliable. This was done by direct comparison of the OD values

obtained for two closely-related microbial communities (surface and subsurface soil),

plotted against a one-to-one (y = x) line. Due to variability in the portion of the soil

microbial community inoculated into the individual test wells, the accuracy of the method

was dependent on the number of replicates analyzed. A variety of dataset sizes were

tested, from n = 3 samples/soil depth to n = 40 samples/depth. The method was

55

statistically valid for all datasets tested. Those substrates that deviated from the one-to-

one line consistently had F values greater than 1. Additionally, datasets of n = 30, 35 and

40 samples/depth consistently allowed identification of the 8 substrates whose

metabolism varied significantly between the two test soil communities. In conclusion,

this one-to-one comparison has been shown to be a statistically valid analytical method to

compare individual substrate usage between soils.

INTRODUCTION

Due to the complexity of soil bacterial communities, methodologies for assessing

microbial populations and metabolic potential have been limited. Although molecular

techniques are now widely used to obtain “fingerprints” of soil bacterial communities,

these techniques provide little information on the functioning of these communities, such

as a community’s ability to metabolize specific pollutants. Community level

physiological profile (CLPP) assays, when applied to study of complex environmental

samples, assess the total microbial community’s capacity to metabolize specific sole

carbon sources under defined laboratory conditions. Although these assays vary in how

respiration is measured - reduction of tetrazolium dye (Garland and Mills, 1991), direct

measurement of CO2 production (Degens et al, 1997, Campbell et al., 2003), and

assessment of O2 loss (Garland et al., 2003) - all of these assays measure utilization of

sole carbon sources.

One CLPP method requires the extraction of cells from the soil sample, which is

inoculated directly onto commercially available sole-carbon source microtitre plates such

as BiOLOG™ plates. A dataset of optical density (OD) values, one per substrate, is

56

obtained from the colorimetric measure of the reduction of a tetrazolium dye. Although it

is a culture-based assay, it has been found that non-culturable cells respond to this assay

(Garland and Lehman, 1999). The method is believed not to be as biased as traditional

culturing techniques (Preston-Mafham et al., 2002). However, metabolism of a particular

substrate during any CLPP assay does not necessarily indicate that such metabolism

would occur in the field (Garland and Lehman, 1999). The reverse is also true; lack of

metabolism may not reflect the in-situ community function. As noted by Smalla et al.

(1998), CLPP assays only reflect the functional characteristics of those organisms able to

grow or be active under the assay (or pre-conditioning) conditions, and may give an

incomplete picture of the microbial community’s functional structure (Preston-Mafham et

al., 2001). Moreover, metabolic diversity does not necessarily indicate species diversity,

as in the case of a community composed of a few generalists rather than a greater number

of specialists (Garland, 1997, Konopka et al., 1998).

Despite any limitations, CLPP provides rapid, reproducible results that allow for

the discrimination of bacterial communities from diverse environmental samples. At the

same time, statistical analysis of the data is problematic. With the large number of

variables (95 substrates on the BiOLOG™ GN plate or 31 on the EcoPlate, for example),

the relatively small number of samples commonly used in CLPP studies makes

application of multivariate analyses unsound (Hitzl et al., 1997). This is especially true of

microtitre plate studies given that each microtitre well receives only a small volume of

inoculum, which results in the uneven distribution between wells of the more rare

members of the microbial community.

57

The goals of this study were 1) to develop a simple, statistically valid method of

visually presenting the data obtained from a CLPP study to discriminate between two soil

communities’ capabilities to utilize individual substrates and, in doing so, 2) to test the

number of replicate samples needed to overcome inherent variability and, thereby, allow

accurate discrimination. The simplest way to assess two systems is through direct

comparison or one-to-one analysis. With this type of analysis, if the two soils being

compared are identical, the utilization values for any specific substrate should be the

same, within one standard error (SE), for both soils. To test if this comparison would be

useful with CLPP data, we ran a large number of soil samples using the BiOLOG

method and looked at the information obtained from plotting the OD values, ± one SE,

for each of the 31 BiOLOG™ EcoPlate substrates, with the x-axis representing one soil

and the y-axis another, for two closely-related soils. We then evaluated which substrates

deviated from the one-to-one (y = x) line by more than one SE. Those substrates should

provide the clearest differentiation between the two samples.


Sample Collection and Handling

Soil samples were collected from an agricultural field on the Rutgers University

campus in New Brunswick, New Jersey, on July 21, 2004. The field was planted to corn

and the plants were approximately 2 m high at the time of sampling. Soil at this location

is a Nixon loam (fine-loamy, mixed, mesic Typic Hapludult) (USDA, SCS, 1987). Forty

samples, approximately 500 mL volume, were collected from the surface (0 – 6 cm) and

another 40 from the sub-surface (6 – 15 cm). Sample sites were approximately 1 m apart

58

in the inter-row space of the field. Surface samples were collected with a trowel and the

subsurface samples, from the same holes as the surface samples, using an auger. Upon

return to the laboratory, all samples were immediately sieved to 2 mm and assayed.

Potential Metabolic Diversity

Due to the possibility of elevated levels of metals in the soil, the procedure of

Kelly and Tate (1998) was used, with the modification that BiOLOG™ EcoPlates (31

substrates with built-in triplicates) were utilized instead of GN Plates. Briefly, 20.0 g of

soil was shaken for 10 minutes in 100 mL 0.1 M TRIS buffer adjusted to pH 6.5 and

centrifuged at 2600 g for 10 minutes. The resulting soil extract was inoculated directly

onto BiOLOG™ EcoPlates, which were then incubated at 30°C. All plates were read at

four hour intervals starting at 16 hours until they had reached an average well color

development (AWCD) of 0.30 absorbance units at a wavelength of 590 nm. It has been

found that the assay selects for various subpopulations of bacteria within the individual

wells, which resulted in a decrease in diversity during a 48-hour incubation (Smalla et al.,

1998). Thus, the composition of the microbial population in any given well is likely to

change from that of the original inoculum with extended incubation times (Garland,

1997, Preston-Mafham et al., 2002). Selection of a single AWCD as an end point to

compensate for variation in environmental samples has been assessed as the best method

for limiting the effects of differences in overall color development rate for multivariate

analysis (Garland, 1997) and the best approach when using potential metabolic diversity

as a rapid evaluation of temporal or spatial variation in bacterial communities (Garland et

al., 2001). Selection of a single incubation time was not feasible for this type of study.

59

Bossio and Scow (1995) found up to 75% variation in the substrates that contributed to

the separation of their soil samples from rice fields with incubation time. An AWCD of

0.30 absorbance units was chosen because it is just past the point at which all wells, on

average, have a positive response, as defined by Garland and Lehman (1999) while, at the

same time, limiting as much as possible the opportunity for selection of unique

communities within each well of the microtitre plates. The actual time needed to reach

this chosen AWCD did not exceed 28 hours except for one sample, which ran 32 hours.

Since each sample was run in triplicate, this procedure yielded 120 sets of optical density

(OD) data per soil depth.

The net optical density (OD) for each substrate was calculated by subtracting the

control well OD from the substrate well OD (Garland and Mills, 1991). If this subtraction

yielded a negative number, the net OD was considered to be zero. An AWCD was

calculated for each replicate substrate set at each reading time by taking the average of

the net OD values for all substrate wells (Garland, 1996). Once a plate had reached the

selected final AWCD of 0.30 absorbance units, the OD values of the triplicates of each

substrate were averaged to obtain a single set of 31 OD values for each soil sample. Due

to detection limits of the system, substrates with average OD ≤ 0.06 absorbance units for

both soil depths were considered to have OD = 0. Since an optical density of zero

indicates no utilization of the substrate, all substrates with OD = 0 for both soil depths

were excluded from all analyses. Using this criterion, eight substrates were eliminated;

thus, 23 substrates were included in our analyses.

Optical density data for the manure amended (MA) and control (MC) and the two

cranberry amended (CH and CL) soils were obtained as described in Chapter 1.

60

Statistical Analysis

Optical density data for the 23 substrates from the full dataset (n = 40/soil depth)

were entered into Principal Component Analysis (PCA) using a correlation matrix (SAS,

SAS Institute Inc., Cary, NC). Since a single average well color development (AWCD) of

0.3 absorbance units was selected for the comparison of data, no further normalization

was necessary. The PC scores were then input into Analysis of Variance (ANOVA,

Statistix 1.0, Analytical Software, Tallahassee, FL).

For the one-to-one comparison, the average OD for each substrate was calculated

for each soil depth and used as the graph coordinates, with data from the surface samples

on the x-axis and the sub-surface on the y-axis (SigmaPlot 9.0, Systat Software, Inc.,

Point Richmond, CA). Thus, a graph containing 23 points, each representing a different

substrate, was obtained. Standard error was also calculated and plotted as bi-directional

error bars. Standard error was not calculated from the individual replicates on each

EcoPlate, due to the high between-well variance caused by the small inoculum size (150

μL/well), but from the average of each plate. The data points, with their associated errors,

were then compared to the one-to-one (y = x) line. The averaged OD data were also run

in ANOVA to obtain F and p values.

In addition to the full dataset (n = 40/soil depth), smaller groupings of the data

were likewise analyzed using the one-to-one comparison. This was done to determine 1)

if the complete dataset was large enough to overcome the variability inherent in soils and

in the microtitre plate methodology, and 2) the size of the minimum dataset that could be

used to adequately characterize the soil microbial communities. Groupings of 3, 5, 10, 15,

61

20, 25, 30 and 35 samples per depth were either selected non-randomly, in order to

adequately represent the variation across the field with as little overlap of samples

between sets as possible, or were randomly selected. Non-random sets included the n =

10/depth, n = 20/depth and two of the n = 30/depth groupings. All other groupings were

randomly selected using a random number generator (http://www.randomizer.org).

Analysis of the smaller groupings was identical to that described above.

Optical density data obtained for MA and MC, and CH and CL were analyzed

using both ANOVA and one-to-one comparison, as described above. The 31 EcoPlate™

plate substrates can be categorized by organism type as well as by compound type. Of the

31 substrates, 25 are found on the BiOLOG GN plate, specific for Gram-negative

bacteria,19 are found on the GP (Gram positive) plate, 19 are found on the AN

(anaerobe) plate and 18 are found on the FF (filamentous fungi) plate. Only three

substrates are unique to the EcoPlate™: 2- and 4-hydroxy benzoic acid and L-arginine.

When grouped by compound type, there are 9 carboxylic acids, 7 carbohydrates, 6 amino

acids, 5 polymers and 2 each of phosphorylated compounds and amines. Results obtained

for MA/MC and CH/CL datasets were evaluated by organism type as well as substrate

type.


Raw optical density data were averaged across triplicates on each EcoPlate to

obtain a single OD dataset per soil sample; thus, the 120 individual OD sets per soil depth

were reduced to 40. This was necessary due to the high between-well variation inherent

in the BiOLOG method, in which each well receives only 150 μL inoculum. The large

62

number of samples was used to assure a statistically valid outcome as well as to insure

that the functioning of the rarer members of the two communities might be seen (Preston-

Mafham et al., 2002). In addition, eight of the 31 substrates were eliminated from all

statistical analyses based upon the fact that neither soil depth registered a response to

these substrates. Hence, for each soil sample, a set of 23 averaged OD values were used

in all analyses.

Development and Validation of the One-to-One Comparison Method

Optical density data from all samples, as described above, were analyzed using

Principal Component Analysis (PCA). The first two principal components (PC’s)

accounted for 47.1% of the total variance in the data, with PC1 accounting for 34.4% and

PC2 for 12.7%. There was no separation along PC1 (Fig. 1). However, ANOVA

identified significant separation along PC2 (p = 0.0001), which is not fully apparent in

the figure. Looking at the factor pattern, all substrates correlated well and positively to

PC1 (≥ 0.50) except E1, F1, H1, G2, H2 and E3, which had positive correlations less than

0.50. For PC2, only 5 substrates were correlated, either positively or negatively, at a

level of 0.50 or higher; these were C1, F1, G2, B3, and D4. Of these five, only F1, at

0.765, was correlated at a level of 0.65 or higher.

Using the same data as were input into PCA, plots of the optical density data for

the two soil depths were examined. When all the OD data ± one SE were plotted (n =

40/soil depth) with each axis representing a different soil depth, 12 substrates deviated

from the one-to-one line (Table 1 and Fig. 2). All of these 12 substrates, when analyzed

with ANOVA, were found to have F values greater than one, indicating that the sample

63

Fig. 1 - Principal component analysis of all data (n = 40 samples/soil depth).

□ = surface samples (0 – 6 cm), ■ = subsurface samples (6-15 cm)

(San Miguel et al., 2007).

64

7 –

30 c

m

Fig. 2 - One-to-one comparison of optical density data for complete dataset (n = 40

samples/soil depth). Each point represents a unique substrate. Open symbols

fall off and closed symbols fall on y = x line. ANOVA significance levels: α

= 0.05 (□), α = 0.1 (Δ), not significant (○ and ●) (San Miguel et al., 2007).

65

Table 1 - EcoPlate well number, name of substrate and substrate type of 12 substrates that deviated from the one-to-one line with n = 40 samples/soil depth. All had F values greater than 1 and those in bold had p values less than 0.05 (San Miguel et al., 2007).

EcoP

late

W

ell

Substrate Substrate Type Utilized to a

greater extent by

Separation Ranking (Hitzl et

al., 1997) B1 methyl pyruvate polymer surface 14

C1 Tween 40 polymer subsurface 22

D1 Tween 80 polymer surface 6

E1 α-cyclodextrin polymer surface 27

F1 glycogen carbohydrate subsurface 26

H1 α-D-lactose carbohydrate surface 13

G2 glucose-1-phosphate phosphorylated compound subsurface 5

H2 D,L-α-glycerol phosphate

phosphorylated compound surface 9

B3 D-galacturonic acid carboxylic acid surface 93

E3 γ-hydroxybutyric acid carboxylic acid subsurface 18

H3 D-malic acid carboxylic acid surface N/A

D4 L-serine amino acid subsurface 20

66

means were outside sampling variability of each other (significant). Furthermore, eight of

the 12 also had p values less than 0.05 (Table 2), indicating the differences were highly

significant. No substrates that fell on the one-to-one line were found to have an F value

greater than one or p value less than 0.05. Thus, the one-to-one comparison successfully

identified all substrates that differed by more than the sampling variability and the eight

substrates with differential utilization that was highly significant (α = 0.05), as

determined by ANOVA.

Of the five substrates found to be correlated with PC2, all deviated from the one-

to-one line (p ≤ 0.05). Seven additional substrates, three with p values less than 0.05,

were also identified using the one-to-one analysis. This suggests that the one-to-one

comparison is providing unique information on substrate utilization and indicates that this

analysis can resolve small differences between the metabolic capabilities of two closely

related microbial communities.

To verify the relationship between the one-to-one analysis procedure and

ANOVA F values, datasets of 3, 5, 10, 15, 20, 25, 30 and 35 samples/soil depth were also

plotted as described above. This also allowed verification that the full dataset (n = 40/soil

depth) was large enough to overcome inherent variability in environmental samples and

this CLPP method. Regardless of grouping size, plotting the OD data versus the one-to-

one line consistently identified all substrates with F values greater than one. Comparisons

with the eight highly significant (p ≤ 0.05) substrates identified with the full dataset were

made using F values obtained for the smaller groupings. Hence, if any of the eight

substrates deviated from the one-to-one line with a smaller grouping, it was considered to

be identified by that grouping (Table 2).

67

Tab

le 2

- C

ompa

rison

of s

mal

ler g

roup

ings

, whe

n pl

otte

d ag

ains

t one

-to-o

ne li

ne, v

ersu

s n =

40

data

set

resu

lts.

Bol

d Ec

oPla

te w

ells

= s

ubst

rate

s with

p ≤

0.0

5 in

AN

OV

A. *

= su

bstra

tes t

hat w

ere

corr

elat

ed,

posi

tivel

y or

neg

ativ

ely,

to P

C2

at th

e 0.

50 le

vel o

r hig

her.

Gre

y bo

xes i

ndic

ate

that

the

grou

ping

mis

sed

a su

bstra

te w

ith p

≤ 0

.05

for t

he n

= 4

0 da

tase

t (Sa

n M

igue

l et a

l., 2

007)

.

68

7 –

30 c

m

Fig. 3 - One-to-one comparison of optical density data for grouping of 3 samples/soil

depth. Symbols reflect results from n = 40 dataset. Open symbols fell off and

closed symbols fell on y = x line when n = 40. ANOVA significance levels: α

= 0.05 (□), α = 0.1 (Δ), not significant (○ and ●), when x = 40 (San Miguel et

al., 2007).

69

7 –

30 c

m

Fig. 4 - One-to-one comparison of optical density data for grouping of 10

samples/soil depth. Symbols reflect results from n = 40 dataset. Open

symbols fell off and closed symbols fell on y = x line when n = 40. ANOVA

significance levels: α = 0.05 (□), α = 0.1 (Δ), not significant (○ and ●), when

x = 40 (San Miguel et al., 2007).

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70

The results from the smallest grouping examined (n = 3 samples/soil depth) were

found to differ substantially from the results of the full dataset. Of the four datasets

constructed, one missed only three of the eight substrates; however, this size grouping

also missed half or more of the significant substrates (Fig. 3 and Table 2). For the two

smallest grouping sizes, n = 3 and 5 samples/soil depth, three of the eight significant

substrates were consistently missed (Table 2). These 3 were methyl pyruvate (EcoPlate

well B1), glycogen (F1) and D-malic acid (H3). Groupings of 10 to 25 provided

improved identification of the eight substrates, generally only missing one or two (Fig. 4

and Table 2). Groupings of n = 30 and n = 35 samples/soil depth consistently identified

all 8 significant substrates found with the full dataset. Thus, it is clear that 40 samples per

soil depth, assayed in triplicate, was a sufficiently large dataset to be a valid standard.

The full dataset was able to overcome the inherent variability in environmental samples

and of this assay due to inoculum variability between microtitre wells.

Assessment of Minimum Number of Samples Needed to Overcome Assay Variability

With the BiOLOG method, each microtitre well receives only 150 μL of

inoculum, allowing for between-well variation in inoculum composition. To best describe

the microbial community of any given soil sample using this CLPP assay, Lowit et al.

(2000) and Balser et al. (2002) both asserted that assaying a greater number of replicate

subsamples was more useful than was running replicate plates on the same sample.

Further, Insam and Hitzl (1999) provided an equation for calculating the minimum

sample size required for ANOVA to be appropriately applied and informed the reader

that for 31 substrates, such as found on a BiOLOG™ EcoPlate, more than 17 replicates

71

are needed. The authors detailed the breakdown of these 17 replicates into 6 soil samples

each run in triplicate. (The authors, in this case, did not average the OD values of each

triplicate to obtain one OD value per substrate for each soil sample.)

For each grouping, an estimated reliability was calculated as the average accuracy

for groupings of a particular size at identifying the significant substrates (all 8 = 100%).

Consistent identification of these eight substrates would indicate that the between-well

variation, due to the small inoculum amount per well on the microtitre plates, had been

overcome. Estimated reliability was poor with groupings of n = 3 and n = 5 samples/soil

depth (50.0% and 55.0%, respectively). There was a considerable increase in reliability at

n = 10 samples/soil depth (87.5%). This estimated reliability was the same for n = 15

samples/depth. The reliability increased for n = 20 samples/depth (93.75%) but decreased

when n = 25 samples/depth (81.25%), possibly due to the fact that only two groupings of

n = 25 were used, versus three or four for the others. Groupings of n = 30 and n = 35

samples/soil depth consistently identified all eight substrates.

Studies of environmental microbial communities often utilize triplicate samples,

with the assumption that this number is sufficient to overcome variation in the

population. However, the effects of rare members of the community may not be detected

with a low number of replicates (Preston-Mafham et al., 2002). Our study found that

when 3 samples per soil depth were run in triplicate, results varied greatly and could miss

up to 62.5% of the significantly different substrates, as identified by the full dataset of 40

samples per soil depth. Overall, the estimated reliability obtained using 3 samples/depth

was calculated to be 50.0%. Thus, this sample set was too small to reliably describe the

microbial communities in this study. Similarly, groupings of 5 samples/ depth also

72

proved too small to accurately describe the microbial communities, with an estimated

reliability of 55.0%.

From the results of this study, it can be said that a minimum of 30 samples per

depth, assayed in triplicate, would be needed to characterize these soil microbial

communities with statistical accuracy. This is a five-fold increase from the recommended

6 samples per treatment suggested by Insam and Hitzl (1999) for metabolic diversity

assays employing 31 substrates. However, at 10 samples per soil depth, run in triplicate,

the estimated reliability was 87.5%. Although this reliability continued to rise from 10 to

20 samples/depth, the difference was not great (only up to 93.75%) and may not warrant

the additional expense. Thus, n = 10 samples/soil depth could be considered to be a

minimum dataset needed to estimate similarity in functional capabilities of these soil

microbial communities. This number of samples is on par with that suggested by Insam

and Hitzl (1999) of six samples per treatment.

While this study was conducted using the BiOLOG method, our results have

implications for other CLPP assays that also utilize microtitre plates. For example,

Campbell et al. (2003) assayed CO2 evolution using 300 μL of soil deposited directly into

microtitre wells. Similarly, Garland et al. (2003) assayed O2 loss using not more than 300

μL of either diluted or undiluted extract using 1 cm X 1 cm of root mat. Like BiOLOG,

these assays may experience high between-well variability due to the small amount of

inoculum used. Thus, our finding that a minimum of 10 samples was necessary to

successfully estimate these communities’ functional capabilities should be taken into

consideration with any CLPP methodology.

73

Differential Utilization of Specific Substrates

Of the 12 substrates identified with the one-to-one comparison of the full dataset,

four were polymers, three were carboxylic acids, two were phosphorylated compounds,

two were carbohydrates and one was an amino acid (Table 1). Of these, three of the

polymers (methyl pyruvate, Tween 80, and α-cyclodextrin) were utilized to a greater

extent by the surface community as were two of the three carboxylic acids (D-

galacturonic acid and D-malic acid), one of the phosphorylated compounds (D,L-α-

glycerol phosphate) and one carbohydrate (α-D-Lactose). Conversely, one of the

polymers (Tween 40), one of the phosphorylated compounds (glucose-1-phosphate), the

third carboxylic acid (γ-hydroxybutyric acid), one of the carbohydrates (glycogen) and

the amino acid (L-serine) were utilized to a greater extent by the subsurface community

(Table 1 and Fig. 2). These findings do not agree with those of Lehman et al. (1995)

showing that the subsurface communities utilized both Tween 40 and Tween 80 to a

greater extent than did the surface communities while surface communities better utilized

carbohydrates. The difference in findings could be due to the orders of magnitude

difference in depth of the samples dubbed “subsurface” between the two studies.

Hitzl et al. (1997) calculated the separation measure for each of the 95 substrates

found on the BiOLOG™ GN plates when incubated at 30 ºC and, thus, were able to rank

the substrates according to their ability to separate dissimilar samples. Only 14 of the top

30 substrates providing the best separation measures are also found on the BiOLOG™

EcoPlate. Of these, 10 were identified by deviation from the one-to-one line using the full

dataset of 40 samples per soil depth (Table 1), six of which also had p values less than

0.05. The remaining two substrates that deviated from the one-to-one line with p ≤ 0.05

74

were D-malic acid (EcoPlate well H3), which is not found on the GN plate, and D-

galacturonic acid (EcoPlate well B3), which was ranked 93 out of 95 by Hitzl et al.

(1997). These results indicate that the resolving power of the one-to-one comparison is

far greater than its simplicity might imply and that the one-to-one graphing technique

provides a simple and statistically meaningful evaluation for CLPP data.

The information obtained in this study on specific substrate utilization, while

interesting, does not allow elucidation of microbial community structure. This is because

the carbon sources on a BiOLOG™ plate are ecologically common and may be utilized

by a few generalists rather than each substrate being utilized by a different specialist

population. As noted by Konopka et al., (1998), there is little resolving power in using

such substrates. Garland (1997) suggested the use of more diverse or unusual substrates

to allow for better characterization of the microbial community in question. The use of

the one-to-one comparison method, presented here, with specific groups of carbon

sources, such as hydrocarbons, may be more useful and could, potentially, provide

community structure and metabolic pathway information as well as metabolic diversity

data. Thus, the one-to-one comparison method would be a simple, reliable and

statistically valid way to assess custom substrate arrays.

Application of One-to-One Comparison Method

Manure Amended and Control Soils

Although only eight samples were obtained in the field study of the impact of

manure amendment, each was a composite of three samples. Thus, application of the one-

to-one comparison method presented here should be statistically valid. One EcoPlate

75

substrate was not utilized it to any extent by either community and was excluded from

the analysis.

Unexpectedly, one-to-one comparison of the manure amended (MA) and control

(MC) soils shows differential utilization of 11 substrates found on the EcoPlate (Fig.

5a,b,c), despite the fact that linear regression of the data revealed a slope of 1.122, only

slightly above 1, indicating that the two communities had nearly identical metabolic

capabilities. Of the 11, all had F values above 1, although none was significant at either

the α = 0.05 or α = 0.10 level. Those substrates that fell off the y = x line included 5

carbohydrates, 2 carboxylic acids, 2 polymers, 1 phosphorylated compound and 1 amino

acid. Five of the 11 substrates are present on the Gram negative (GN), Gram positive

(GP), anaerobic (AN) and filamentous fungi (FF) BiOLOG plates. An additional three are

present on GN, GP and FF plates, one is present on GP, AN and FF plates, one is on GN

and AN plates and the last is on GP and FF plates.

Although the majority of the 11 substrates were utilized to a greater extent by the

microbial community associated with manure amended (MA) soil, three were utilized

more by the MC community. These included D-xylose (carbohydrate, GP and FF plates),

Tween 80 (polymer, GN, GP and FF plates) and γ-hydroxybutyric acid (carboxylic acid,

GN, GP, FF). Thus, three of the four substrates not found on the AN plate were utilized

to a greater extent by the control soil community, indicating land management of no-till

plus manure may favor anaerobes more so than does no-till alone (van Eekeren et al.,

2009, Zhong et al., 2010). Higher soil respiration in the manured soil may account for the

greater anoxia.

76

A

B

C

Fig. 5 – One-to-one comparison optical density data from MA and MC. A) OD ≥ 0.25 AU, B) OD between 0.25 and 0.14 AU, C) OD ≤ 0.14 AU. Open symbols fall off and closed symbols fall on y = x line. No substrate was found to be significant with ANOVA.

77

Cranberry Amended Soils

Direct comparison of individual substrate utilization by the bacterial communities

associated with high (CH) and low (CL) cranberry amendment showed that the majority

of substrates were utilized to different extents by the two communities, with only six of

the 31 substrates falling on the y = x line (Fig. 6a,b,c), corresponding to those substrates

with F values below 1. As is clear from Fig. 6b, only those substrates with the lowest

optical densities (≤ 0.02 AU for CL and ≤ 0.04 AU for CH) were utilized to a greater

extent by the CH community, indicating that high rate cranberry application selected for

rare members of the microbial community that were either very slow growing or present

in low numbers. The eight substrates utilized by CH over CL included itaconic acid, i-

erythritol, 2-hydroxybenzoic acid (salicylic acid), L-threonine, glycyl-L-glutamic acid,

glucose-1-phosphate, α-ketobutyric acid, and α-D-lactose. These substrates represent four

of the six compound types found on the plate. All but one are also found on either the

AN or the FF plate; that one (salicylic acid) is unique to the EcoPlate™. Three of the

eight substrates were not utilized at all by the CL community including: i-erythritol

(found on GN and AN plates), salicylic acid (unique to EcoPlates), and α-ketobutyric

acid (GN and AN plates). There were no substrates that were utilized to any extent by

the CL community that were unused by the CH community. These results suggest that the

chemical and/or physical properties of the CH soil have selected for some rare members

of the community that cannot be discerned in the CL community, possibly because they

are slower growers that get outcompeted in the CL soil where water and nutrients are

more readily available. While the identities of these members of the CH community

remain unknown, it is quite possible they are Gram negative facultative anaerobes and,

78

Fig. 6 – One-to-one comparison of optical density data from CH and CL. A) OD ≥ 0.18 AU, B) OD < 0.18 AU. Open symbols fall off and closed symbols fall on y = x line. ANOVA significance levels: α = 0.05 (□), α = 0.1 (Δ), not significant (○ and ●).

79

given the pH, fungi. In addition to the low pH associated with the CH treatment, the high

free phenol content of the cranberry waste (Vinson et al., 2001) may help explain the

metabolic diversity pattern seen with in this treatment. Additionally, cranberries are also

known for their anti-adhesion properties (Zhang et al., 2005). This, together with low pH

and high phenol content, likely impacted the microbial community in the CH soil (Welp

and Brümmer, 1999).

CONCLUSIONS

In conclusion, one-to-one comparison provides information on the usage of the

individual substrates that is unique from those identified by the correlations obtained with

PCA. This technique has been shown to work consistently with two soil depths to

identify eight significant substrates (α = 0.05) using 30, 35 or 40 samples per depth.

While this may be excessively expensive and time consuming for some, the accuracy of

this method in identifying significant substrates may be acceptable when ten samples, run

in triplicate, are assayed. Additionally, this technique has been shown to provide a

simple, user-friendly statistical analysis of metabolic diversity data. Although this study

utilized the BiOLOG method to evaluate metabolic diversity, the one-to-one comparison

analysis should be applicable to other sole carbon source assays as well, such as those

measuring CO2 evolution (Degens and Harris, 1997, Campbell et al., 2003) and those

measuring O2 loss (Garland et al., 2003). Moreover, one-to-one comparison has the

potential to provide not only information on community functional capabilities but also

community structure and metabolic pathway information, if used with a custom substrate

array.

80

One-to-one comparison clearly provides additional information about the soil

ecosystems associated with the manure amended and control soils beyond that obtained

with PCA (Fig. 3, Chapter 1). This analysis may also explain why H’ was highly

correlated to PC1 in the combined PCA of MA and MC (Fig. 4 and Table 2, Chapter 1).

We can, thus, conclude that, in a no-till system, manure amendment for four years

followed by two years of no manure amendment did have a residual impact the functional

structure of the bacterial community when compared to a control soil that was planted to

the same crop but received no manure. This result would be anticipated given the

significantly difference seen in organic matter content and dehydrogenase activity

between the two treatments (Table 1, Chapter 1).

Similarly, one-to-one comparison of the cranberry amended soils, which showed

that only six of 31 substrates were utilized to the same extent by the two communities,

expanded on the information provided by PCA of the same data (Fig. 7, Chapter 1).

Although separation was seen along both PC1 and PC2, the extent of differential

utilization was not apparent using PCA alone. Thus, one-to-one comparison provided

unique information about the functional structure of these communities.

81

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Bossio, D.A., Scow, K.M. 1995. Impact of carbon and flooding on the metabolic diversity of microbial communities in soils. Appl. Environ. Microb. 61:4043-4050.

Campbell, C.D., Chapman, S.J., Cameron, C.M., Davidson, M.S., Potts, J.M. 2003. A rapid microtitre plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microb. 69:3593-3599.

Degens, B.P., Harris, J.A. 1997. Development of a physiological approach to measuring the catabolic diversity of soil microbial communities. Soil Biol. Biochem. 29:1309-1320.

Garland, J.L. 1996. Analytical approaches to the characterization of samples of microbial communities using patterns of potential C source utilization. Soil Biol. Biochem. 28:213-221.

Garland, J.L. 1997. Analysis and interpretation of community-level physiological profiles in microbial ecology. FEMS Microbiol. Ecol. 24:289-300.

Garland, J.L., Lehman, R.M., 1999. Dilution/extinction of community phenotypic characters to estimate relative structural diversity in mixed communities. FEMS Microbiol. Ecol. 30:333-343.

Garland, J.L., Mills, A.L. 1991. Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Appl. Environ. Microb. 57:2351-2359.

Garland, J.L., Mills, A.L., Young, J.S. 2001. Relative effectiveness of kinetic analysis vs single point readings for classifying environmental samples based on community-level physiological profiles (CLPP). Soil Biol. Biochem. 33:1059-1066.

Garland, J.L., Roberts, M.S., Levine, L.H., Mills, A.L. 2003. Community-level physiological profiling performed with an oxygen-sensitive fluorophore in a microtitre plate. Appl. Environ. Microb. 69:2994-2998.

Hitzl, W., Rangger, A., Sharma, S., Insam, H. 1997. Separation power of the 95 substrates of the BIOLOG system determined in various soils. FEMS Microbiol. Ecol. 22:167-174.

Insam, H, Hitzl, W. 1999. Data evaluation of community-level physiological profiles: a reply to the letter of P.J.A. Howard. Soil Biol. Biochem. 31:1198-1200.

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Kelly, J.J., Tate, R.L. 1998. Use of BIOLOG for the analysis of microbial communities from zinc-contaminated soils. J. Environ. Qual. 27:600-608.

Konopka, A., Oliver, L., Turco, Jr., R.F. 1998. The use of carbon substrate utilization patterns in environmental and ecological microbiology. Microb. Ecol. 35:103-115.

Lehman, R.M., Colwell, F.S., Ringelberg, D.B., White, D.C. 1995. Combined microbial community-level analyses for quality assurance of terrestrial subsurface cores. J. Microbiol. Meth. 22:263-281.

Lowit, M.B., Blum, L.K., Mills, A.L. 2000. Determining replication for discrimination among microbial communities in environmental samples using community-level physiological profiles. FEMS Microbiol. Ecol. 32:97-102.

Preston-Mafham, J., Boddy, L., Randerson, P.F. 2002. Analysis of microbial community functional diversity using sole-carbon-source utilization profiles – a critique. FEMS Microbiol. Ecol. 42:1-14.

San Miguel, C., Dulinski, M., Tate, R.L. 2007. Direct comparison of individual substrate utilization from a CLPP study: a new analysis for metabolic diversity data. Soil Biol. Biochem. 39:1870-1877.

Smalla, K., Wachtendorf, U., Heuer, H., Liu, W.-T., Forney, L. 1998. Analysis of BIOLOG GN substrate utilization patterns by microbial communities. Appl. Environ. Microb. 64:1220-1225.

United States Department of Agriculture. Soil Conservation Service. 1987. Soil Survey of Middlesex County, New Jersey.

van Eekeren, N., de Boer, H., Bloem, J., Schouten, T., Rutgers, M., de Goede, R., Brussaard, L. 2009. Soil biological quality of grassland fertilized with adjusted cattle manure slurries in comparison with organic and inorganic fertilizers. Biol. Fertil. Soils 45:595-608.

Vinson, J.A., Su, X., Zubik, L., Bose, P. 2001. Phenol antioxidant quantity and quality in foods: fruits. J. Agric. Food Chem. 49:5315-5321.

Welp, G., Brümmer, G.W. 1999. Effects of organic pollutants on soil microbial activity: The influence of sorption, solubility, and speciation. Ecotox.Environ. Safe. 43:83-90.

Zhang, L., Ma, J., Pan, K., Go, V.L.W., Chen, J., You, W.-C. 2005. Efficacy of cranberry juice on Helicobacter pylori infection: a double-blind, randomized placebo-controlled trial. Helicobacter 10:139-145.

Zhong, W., Gu, T., Wang, W., Zhang, B., Lin, X., Huang, Q., Shen, W. 2010. The effects of mineral fertilizer and organic manure on soil microbial community and diversity. Plant Soil 326:511-522.

83

Chapter 3

Multidrug Resistance in an Archived Soil Bacterium

ABSTRACT

Antibiotic resistance (AR) in clinically relevant bacteria is a growing problem.

However, the question of where resistance determinants originate remains open. Analysis

of pre-antibiotic era pathogens has indicated that they did not carry resistance

determinants. At the same time, soils have been found to harbor abundant and diverse AR

gene pools. Hence, the possibility exists that pathogens have acquired resistance from

environmental strains. In this study, an archived soil bacterium, isolated in 1963 and

identified by 16S rRNA sequencing as an emerging pathogen, Achromobacter

xylosoxidans, was examined for innate resistance to six different antibiotics from five

different classes, including ciprofloxacin (CIP), a fully synthetic compound introduced 25

years after isolation. The goal was to determine if clinically important antibiotic

resistance mechanisms result from selective pressure or may be acquired from

environmental organisms. High-level resistance was seen to all drugs tested. Given this

level of resistance, especially to CIP, it appears that inherent, high-level multidrug

resistance may be common in environmental strains of this pathogen. Due to the range of

drugs affected and the levels of resistance seen, it was hypothesized that the resistance

mechanism was likely to be efflux; however, MIC testing in the presence of an efflux

pump inhibitor showed no increase in susceptibility. Although several other resistance

mechanisms were tested for, the mechanism(s) utilized by this bacterium remains

undetermined.

84

INTRODUCTION

Bacterial resistance to antibiotics has been problematic almost since the discovery

of these compounds more than 70 years ago. To some extent, the development of resistance

is inevitable, given that many antibiotics are natural products of microorganisms, or semi-

synthetic derivatives of these products. Tomasz (2006) reported that the majority of

effective mechanisms of antibiotic resistant (AR) in human pathogens are acquired by

genetic exchange, not spontaneous mutation. It has been noted that many AR determinants

have “non-clinical” origins (Martinez, 2008). Since these genes were not present in human

or animal flora in the pre-antibiotic era (Mazel and Davies, 1999), many believe antibiotic

producer bacteria, which are ubiquitous in the environment, especially soil, are the original

source of resistance (Alonso et al., 2001). However, a recent phylogenic analysis showed

that non-antibiotic producing bacteria harbored more readily-available and diverse pools of

AR genes than did producer strains (Aminov and Mackie, 2007). Regardless, soils have

been found to be an abundant reservoir of a highly diverse pool of AR genes (see, for

example, Mazel and Davies, 1999, Burgos et al., 2005, D’Costa et al., 2006, Ghosh and

LaPara, 2007).

Bacteria have adapted a variety of mechanisms to subsist and even thrive in the

presence of most antibiotics currently on the market. The most notorious example is

methicillin resistant Staphylococcus aureus, commonly known as MRSA. However, many

other multiple drug resistant (MDR) bacteria exist in both clinical and natural settings.

Resistance mechanisms can take one of three forms: 1) alteration of the drug target ( e.g.,

qnr genes and mutations of gyrA and parC for quinolone resistance, tet(M, O, Q, S, T, W)

for tetracycline resistance); 2) enzymatic inactivation of the drug (e.g., β-lactamases for

85

lactams, tet(X) for tetracyclines); 3) efflux of the drug (e.g., AcrAB-TolC system of the

resistance nodulation (RND) family of efflux pumps). Often, MDR organisms employ

efflux systems capable of handling multiple classes of antibiotics, such as the AcrAB

system, rather than having separate resistance mechanisms for each drug. Although

environmental strains of human pathogens are common, the majority of studies into

antibiotic resistance mechanisms have been undertaken on clinical strains only. This fact,

together with the abundance of AR genes in the environment, indicates there may be

resistance mechanisms existent in environmental strains that might easily be acquired or

evolved in the clinical ones.

In the current study, an archived environmental bacterium was assayed for

antibiotic resistance. This organism, isolated from soil in the early 1960’s, was originally

identified as Alcaligenes spp.; however, 16S rRNA sequencing of a 1490 bp fragment

showed 100% identity to Achromobacter xylosoxidans, an emerging pathogen. The abilities

of this bacterium to resist six different antibiotics from five different classes were

investigated, including natural, semi-synthetic and synthetic compounds. Only four of the

six antibiotics were discovered and in use prior to isolation of this Achromobacter,

including streptomycin (introduced in 1943), chlortetracyline (1948), kanamycin (1957)

and ampicillin (1961), while both rifampicin (1967) and ciprofloxacin (1988) were

introduced after isolation.

These drugs represent compounds commonly utilized in both veterinary (STR, cTc,

KAN, AMP) and human (STR, cTc, AMP, RIF, CIP) treatment, as well as in agriculture

(STR), a situation known to increase the risk of the development of resistance. According

to the World Health Organization (WHO), STR is a critically important drug in the

86

treatment of human disease (National Antimicrobial Resistance Monitoring System

(NARMS) , 2008). However, it is also used in the treatment of gram-negative infections in

animals, as a feed additive in animal husbandry, and as a pesticide. Chlortetracycline (cTc)

is used in humans for certain dental and ocular infections, for a wide variety of veterinary

infections, and as a feed additive in cattle, swine, sheep and poultry. Tetracyclines are

listed as highly important in human medicine by WHO (NARMS, 2008). Kanamycin

(KAN) is likewise considered highly important and is further utilized extensively in

veterinary treatment of disease. Ampicillin (AMP), one of the most widely prescribed

drugs worldwide, is listed as critically important to human medicine by WHO (NARMS,

2008), and is utilized for urinary tract infections and pneumonia, among other diseases. It is

also used in injectable form for the treatment of disease in large animals (i.e, horses and

elephants). Rifampicin (RIF), also known as rifampin, is a semi-synthetic compound used

extensively to treat tuberculosis. Although it is highly effective, resistance develops

quickly; hence, it is used in combination therapy only. Enteric gram-negative bacilli as well

as Pseudomonas spp. are intrinsically resistant to RIF, possibly due to membrane

permeability. Unlike the prior four drugs discussed, RIF is not approved by the U.S. Food

and Drug Administration (FDA) for veterinary use. Ciprofloxacin (CIP) is similarly not

approved by the FDA for use in animals. However, in 1995, fluoroquinolones were

approved for use in livestock (Goforth and Goforth, 2000) and are used internationally in

veterinary medicine and as feed additives, with CIP found in 25% to 41% of the manure

samples tested in a Chinese study (Zhao et al, 2010). Once referred to as “the drug of last

resort”, CIP is listed by WHO as critically important (NARMS, 2008), most widely known

for its application in the treatment of Anthrax (Bacillus anthracis).

87

For the current study, it was hypothesized that, if selective pressure were the driving

force in antibiotic resistance in the soil environment, the soil isolate, Achromobacter

xylosoxidans, would exhibit resistance only to natural antibiotics and possibly to those in

use prior to isolation while being susceptible to both RIF and CIP, drugs introduced after

isolation. Minimum inhibitory concentrations (MIC), determined according to Clinical

Laboratory Standards Institute (CLSI, 2008) methodology, were used to establish

resistance levels, and molecular methods were used to further investigate resistance

identified by MIC testing.


An Achromobacter xylosoxidans was obtained from American Type Culture

Collection as an Alcaligenes sp. (ATCC 15446), isolated from soil at an unspecified

location in 1963, and later identified using 16S rRNA sequencing (27F;1525R, 100%

identity of 100% coverage). This organism was revived in nutrient broth according to

ATCC instructions. After 24 h growth at 30°C, fresh medium was added to a final volume

of 100 mL. After another 24 h growth, the culture was frozen at -80°C in 20% glycerol.

This frozen stock was used to grow up all subsequent cultures.

MIC Determination

MIC testing was conducted on the bacterium using six antibiotics from five classes

including chlortetracycline (cTc), kanamycin (KAN), streptomycin (STR), ampicillin

(AMP), rifampicin (RIF) and ciprofloxacin (CIP). Although all the antibiotics used are

broad spectrum, effective against both gram-positive and gram-negative bacteria, intrinsic

88

resistance to RIF is known to exist in Enterobacteriaceae, Acinetobacter and Pseudomonas

species. With the exception of cTc, all are bacteriocidal. Table 1 lists modes of action and

related properties of each drug. Stock solutions of KAN, STR and AMP were made at

10,000 µg/mL in filtered DI water; those of cTc and RIF were made at 1,000 µg/mL in

50% glycerol. CIP stock was made at 500 µg/mL, also in 50% glycerol. All stock solutions

were made not more than one month prior to use, filter sterilized to 0.2 µm and stored

frozen at -20°C.

The frozen isolate was streaked onto Luria-Bertani (LB) agar and grown for 24 to

48 h at 30 °C. A single colony was picked, restreaked onto fresh agar and grown another 24

to 48 h. From this plate, a single colony was picked and used to inoculate 100 mL cation-

adjusted Mueller-Hinton broth (CAMHB) (BBL™ Mueller Hinton II Broth, BD, Sparks,

MD) . This liquid culture was grown at 30°C on a water bath shaker (Innova 3100, New

Brunswick Scientific, New Brunswick, NJ) to a density of approximately 109 CFU/mL.

This stock culture was then diluted, as described below, for use as the inoculum for MIC

testing.

The procedure used for MIC determination was that of CLSI (2006) with the

modification that 125 mL flasks were used and the final cell density for each culture was

determined by spectrophotometry (Lambda 25, Perkin Elmer, Shelton, CT) at 625 nm. For

each antibiotic, liquid cultures were set up in flasks using CAMHB, stock culture and

antibiotic to a final volume of 10 mL. For the inoculum, fresh stock culture was adjusted to

a density equal to 0.5 McFarland standard using fresh medium. This adjusted culture was

then further diluted to a final dilution of 1:300. The concentrations tested for KAN, STR,

AMP, cTc and RIF were 8, 16, 32, 64 and 128 µg/mL. For CIP, the concentrations tested

89

Table 1 – Properties of Antibiotic Utilized

Antibiotic/ Year introduced Chemical Class Structure Mode of Action

Streptomycin (STR) 1943

Aminoglycoside

Natural

Disrupts cell wall and inhibits initiation and elongation processes

during protein synthesis

Chlortetracycline (cTc) 1948

Tetracycline

Natural

Inhibits translation (protein synthesis)

Kanamycin (KAN) 1957

Aminoglycoside

Natural

Disrupts cell wall and inhibits translation (protein synthesis)

Ampicillin (AMP) 1961

Β-lactam

Semi-synthetic

Inhibits cell wall synthesis

Rifampicin (RIF) 1967

Ansamycin

Semi-synthetic

Inhibits DNA-dependent RNA polymerase activity (suppresses the initiation

of RNA synthesis)

Ciprofloxacin (CIP) 1988

Fluoroquinolone

Synthetic

Inhibits cell division via DNA gyrase and topoisomerase IV

90

were 0.5, 1, 2, 4 and 8 µg/mL. A live control (0 µg/mL) was set up for each antibiotic

tested. Sterile controls were set up along with the test cultures for each antibiotic at each

concentration used. Both test and control flasks were placed on a water bath shaker at 30°C

at 150 rpm for 20 h. After incubation, the optical density (OD) of each test culture was

determined. Samples were diluted as needed to achieve OD readings not more than 0.5

absorbance units. These OD readings were then multiplied by the dilution factor and used

to calculate 50% and 90% loss of density values (MIC50 and MIC90, respectively)

compared to the live, no antibiotic control (maximal growth).

To test for efflux activity, MIC testing was repeated using CIP both with and

without an efflux pump inhibitor (EPI), phenylalanine arginine β-naphthylamide (PAβN) at

a dose of 100 μg/mL broth.

Polymerase Chain Reaction and Sequencing

The presence of specific antibiotic resistance genes was evaluated by polymerase

chain reaction (PCR). Genes tested for included clinically relevant quinolone resistance

genes (qnrABS) as well as extended spectrum β-lactamase (ESBL) genes (blaOXA and

blaVIM), and genes providing tetracycline resistance via ribosomal protection proteins

(tetRIBO). Positive controls were obtained where possible. Several genes, mutations in which

are known to cause quinolone resistance via alteration of the drug target, were also isolated

with PCR for sequencing (GENEWIZ®, South Plainfield, NJ). These included DNA gyrase

genes (gyrA,B), type IV topoisomerase gene (parC), and efflux pump genes (acrA,R).

Table 2 lists primer sequences and PCR conditions used. For the purpose of amplification,

genomic DNA was extracted using UltraClean™ Microbial DNA Extraction Kit (MoBio

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Table 2 – Primer Sequences and PCR Conditions

Gene target Primer Oligonucleotide sequence (5’-3’) PCR conditions Source

blaOXA

OXA-F ACACAATACATATCAACTTCGC 95°C for 5:00; 30 cycles of 95°C for

0:30, 60°C for 0:30, 77°C for 1:30; 77°C

for 10:00

Lim et al., 2009 OXA-R AGTGTGTTTAGAATGGTGATC

OXA114-F ACGCCTGAACCCTTTTATCC Same as OXA Doi et al.,

2008 OXA114-R ATCGACAGGCCGCGCAGT

blaVIM VIM-F AGTGGTGAGTATCCGACAG

95°C for 5:00; 30 cycles of 95°C for

1:00, 55°C for 0:45, 72°C for 1:30; 72°C

for 8:00

Lim et al., 2009 VIM-R ATGAAGTGCGTGGAGAC

tetRIBO Ribo2-FW GGMCAYRTGGATTTYWTIGC


0:30, touchdown 72-50°C for 0:30, 72°C for 0:30; 20 cycles of 94°C for 0:30, 50°C for 0:30, 72°C for

0:30, 72°C for 7:00

Aminov et al., 2001 Ribo2-RV TCIGMIGGIGTRCTIRCIGGRC

gyrA gyrAF ACGTATTGGGCAATGACTGG


0:30, 55°C for 0:45, 72°C for 0:45; 72°C

for 7:00

Chen et al., 2007 gyrAR GGAGTCGCCGTCAATAGAAC

gyrB gyrBF CAAACTGGCGGACTGTCAGG

Same as gyrA Chen et al., 2007 gyrBR AGCCCAGCGCGGTGATCAGC

parC parCF CGTCTATGCGATGTCAGAGC

Same as gyrA Chen et al., 2007 parCR TAACAGCAGCTCGGCGTATT

acrA EcacrA1F ATGAACAAAAACAGAGGG


0:30, 50°C for 0:45, 68°C for 1:30; 68°C

for 10:00

This study EcacrA1194R TTAAGACTTGACTGTTC

acrR EcacrR1F ATGGCACGAAAAACCAAAC

Same as EcacrA This study EcacrR648R TTATTCGTTAGTGGCAGG

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Table 2 – Primer Sequences and PCR Conditions (continued)

Gene target Primer Oligonucleotide sequence (5’-3’) PCR conditions Source

qnrA

qnrAm-F AGAGGATTTCTCACGCCAGG 95°C for 10:00; 35 cycles of 95°C for

1:00, 54°C for 1:00, 72°C for 1:00; 72°C

for 10:00

Cattoir et al., 2007 qnrAm-R TGCCAGGCACAGATCTTGAC

qnrAFam-F ACGCCAGGATTTGAGTGAC Same as qnrAm Lavilla et

al., 2008 qnrAFam-R CCAGGCACAGATCTTGAC

qnrB

qnrBm-F GGMATHGAAATTCGCCACTG Same as qnrAm Cattoir et

al., 2007 qnrBm-R TTTGCYGYYCGCCAGTCGAA

qnrB-F GGCACTGAATTTATCGGC Same as qnrAm Lavilla et

al., 2008 qnrB-R TCCCGAATTGGTCAGATCG

qnrB-283F GGMATYGAATTCGCCACTG Same as qnrAm This

study qnrB-526R TTRGCTGCTCGCCAGTCGAA

qnrB4 qnrB4-F AGTTGTGATCTCTCCATGGC

Same as qnrAm Lavilla et al., 2008 qnrB4-R CGGATATCTAAATCGCCCAG

qnrS

qnrSm-F GCAAGTTCATTGAACAGGGT Same as qnrAm Cattoir et

al., 2007 qnrSm-R TCTAAACCGTCGAGTTCGGCG

qnrS-F CCTACAATCATACATATCGGC Same as qnrAm Lavilla et

al., 2008 qnrS-R GCTTCGAGAATCAGTTCTTGC

93

Laboratories, Inc., Carlsbad, CA) and plasmid DNA was extracted with StrataPrep®

Plasmid Miniprep Kit (Stratagene, Agilent Technologies, Inc., Santa Clara,CA). For those

genes known to be plasmid-borne in clinical isolates, both plasmid and genomic DNA was

used for PCR.

To verify the EPI results, expression of acrA was quantified by reverse transcriptase

polymerase chain reaction (Q-RT-PCR). Cultures were set up with CIP as described above.

RNA was extracted using Ribo-Pure™-Bacteria and reverse transcription was performed

using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster

City, CA). Custom 16S rRNA (endogenous control) and acrA primers were constructed

using the TaqMan system with SYBR®Green I dye (Applied Biosystems). Q-RT-PCR was

run on the StepOnePlus™ real-time PCR system and StepOne Software v2.1 (Applied

Biosystems) was used to calculate threshold cycle (CT), ∆CT (CTacrA-CT16S for each CIP

dose), ∆∆CT (∆CT of each CIP dose - ∆CT of 0 CIP control) and RQ (fold difference in

starting RNA).


Minimum Inhibitory Concentrations

The present study was designed to look at the abilities of Achromobacter

xylosoxidans (ATCC 15446), isolated from soil in 1963, to resist a variety of antibiotics,

including natural, semi-synthetic and synthetic compounds that were introduced both

before and after isolation, in an attempt to determine the molecular ecology of clinically

relevant resistance mechanisms. Significant resistance was seen to all antibiotics tested,

well above the CLSI (2008) resistance standards for Enterobacteriaceae spp. (Table 3),

94

Table 3 –Antibiotic tolerance of 15446 compared to minimum inhibitory concentration standards for resistance (MIC90 ) for Enterobacteriaceae spp.

Antibiotic

Strain

MIC resistance standards (CLSI, 2008) 15446

Tc ≥ 16 64‡

KAN ≥ 64 > 128

STR ≥ 64* >> 128

AMP ≥ 32 >> 128

RIF ≥ 4 + > 128

CIP ≥ 4 8

‡ 15446 tested in chlortetracycline (cTc) * STR resistance breakpoint from NARMS (2004); CLSI does not establish any

breakpoint for this drug. + RIF resistance standard reported for Haemophilus spp., not Enterobacteriaceae spp.

95

regardless of their date of introduction or if they were natural, semi-synthetic or fully

synthetic compounds.

Resistance to Antibiotics in Use Prior to Isolation

No MIC was determined for three of the four antibiotics introduced prior to the

isolation of this organism (AMP, KAN, and STR) while the MIC of the fourth (cTc) was

64 µg/mL, four times above CLSI breakpoint. Resistance to AMP is not unexpected given

the prevalence of naturally occurring, β-lactamases in gram-negative bacteria. Examples

include the oxacillinase (OXA) family of β-lactamases, common in Acinetobacter, and the

Veronaintegron-encoded metallo- β-lactamase (VIM) family produced mainly by

Pseudomonas aeruginosa but infrequently occurring in Enterobacteriacea. Amplification

by polymerase chain reaction (PCR) yielded no results for either of these families of β-

lactamases.

The use of chlortetracycline (cTc) in agriculture began in the mid-1940’s and

continues today. Due to its subtherapeutic use in animal husbandry, cTc contamination of

agricultural soils is common (Hamscher et al., 2002, Martínez-Carballo et al., 2007,

Andreu et al., 2009) where it has a half-life of around 20 days (Carlson and Mabury, 2006).

Additionally, studies have shown high proportions of resistant fecal isolates from livestock

given cTc-supplemented feed (Langlois et al., 1986, Lee et al., 1993, Alexander et al.,

2009). One study found tetracycline resistance in 100% of fecal isolates from pigs

continuously exposed to subtherapeutic doses of cTc (Langlois et al., 1986). From this

perspective, the finding that A. xylosoxidans ATCC15446 has high level resistance to cTc

is not surprising; however, many tet resistance determinants are plasmid-borne. Attempts to

96

extract plasmid DNA (pDNA) from this isolate were unsuccessful. Polymerase chain

reaction (PCR) using both pDNA and genomic DNA (gDNA) and a primer set designed to

amplify ribosomal protection proteins: Tet B P, M, O, Q, S, T, and W (Aminov et al.,

2001), failed to amplify the 1,200 bp target although a 10,000 bp amplicon was obtained

from gDNA.

Resistance to Antibiotics Introduced After Isolation

Although both RIF, a semi-synthetic drug, and CIP, fully synthetic, were developed

and introduced years after isolation, A. xylosoxidans ATCC 15446 was found to be highly

resistant to both drugs. Testing up to 128 µg/mL did not yield an MIC for RIF. While

intrinsic RIF resistance due to membrane permeability is known in several γ-

Proteobacteria, including those in the family Enterobacteriaceae, as well as the genera

Pseudomonas and Acintobacter, A. xylosoxidans is a β-Proteobacteria. As such, intrinsic

RIF resistance has not be described for this or any related organism.

The most surprising result obtained was the MIC for CIP, determined to be 8

µg/mL, twice the resistance breakpoint dose of 4 µg/mL, eight times the susceptibility

breakpoint of ≤ 1 µg/mL (CLSI, 2008) and well above the human fatal dose of 6 µg/mL.

This result is highly significant since A. xylosoxidans is an emerging pathogen causing

bacteremia, neonatal meningitis, pneumonia, urinary tract infections and, most commonly,

pulmonary infection in cystic fibrosis patients. Given that CIP was introduced 25 years

after isolation, is completely synthetic and is used extensively in human chemotherapy, the

major clinical fluoroquinolone resistance mechanisms were investigated in an attempt to

identify the mechanism used by A. xylosoxidans ATCC 15446.

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Fluoroquinolone Resistance

In addition to being powerful antibiotics, the original purpose of quinolones in the

environment is believed to be as signaling molecules, controlling bacterial behavior such as

quorum sensing and biofilm formation. Although fluoroquinolone drugs are a synthetic

form of quinolones, obtained by a fluorine substitution at position 6, it is not surprising that

environmental strains of some species may be more resistant than clinical ones to these

compounds (Alonso et al., 1999). A recent study of 480 soil-isolated Streptomyces strains

found that 11% had high-level (6-128 μg/mL) intrinsic resistance to CIP (D’Costa et al.,

2006). Additionally, it has been noted that the multidrug resistant (MDR) efflux pumps

found in Pseudomonas aeruginosa play a key role in the export of quinolones used as cell

signaling compounds (Séveno et al., 2002).

The targets of CIP are two tetrameric enzymes involved with DNA replication,

DNA gyrase and topoisomerase IV. In clinical gram-negative strains, mechanisms for

alteration of these targets include specific mutations in the quinolone resistance

determining regions (QRDR) of gyrA, gyrB and parC. A double mutation in the DNA

gyrase subunit gene gyrA, including substitution at both amino acid 83 from serine to

leucine (S83L) and at AA87 from aspartic acid to asparagine (D87N), is required for

fluoroquinolone resistance. In Escherichia coli isolates, these mutations, in combination

with mutation in the topoisomerase IV subunit gene parC (S80I), resulted in increased MIC

values (Moon et al. 2010). Similarly, mutation at AA464 in gyrB also confers resistance.

Polymerase chain reaction (PCR) and sequencing of the QRDR of the genes gyrA, gyrB

and parC showed that the specific mutations that confer quinolone resistance were absent

98

in A. xylosoxidans ATCC 15446. Although nucleotide substitutions were found, these were

all silent, i.e., did not alter the amino acid sequences.

Another mechanism of target alteration is as a family of plasmid-borne quinolone

resistance (qnr) genes. The first qnr gene, qnrA, was reported in literature in 1998

(Martínez-Martínez et al., 1998) followed by qnrS (Hata et al, 2005) and qnrB (Jacoby et

al., 2006). Most recently, in 2009 qnrC and qnrD were identified, both in γ-proteobacteria

(Wang et al., 2009, Cavaco et al., 2009). Although the exact mode of action is unclear, it

has been suggested that the pentapeptide repeat proteins produced from these genes bind to

both subunits of DNA gyrase thereby lowering gyrase binding to DNA and reducing gyrase

participation in the quinolone-gyrase-cleaved DNA complex that is fatal to the cell

(Strahilevitz et al., 2009). These genes alone provide low-level resistance to quinolones;

however, they often occur in tandem with other resistance mechanisms. Although these

plasmid-mediated genes were once thought to be restricted to clinical Enterobacteriaceae

strains, recently qnrS2 was found in environmental Aeromonas spp. (Cattoir et al., 2008)

and qnrS1 was discovered in E. coli from poultry (Cerquetti et al., 2009). Repeated

attempts to extract plasmid DNA from A. xylosoxidans ATCC 15446 were unsuccessful;

thus, if this strain does carry plasmids, they are larger than 45 kb, the size limit of the

extraction kit used. Genes encoding pentapeptide repeat proteins similar to the Qnr

proteins have also been identified on the chromosomes of both gram-negative and gram-

positive bacteria (Strahilevitz et al., 2009). Thus, PCR was performed with both pDNA and

gDNA; however, amplification of qnrA,B,S was unsuccessful. No attempt was made to

amplify qnrC,D.

99

In gram-negative bacteria, efflux systems associated with clinically significant

resistance belong to the resistance nodulation division (RND) family (Piddock, 2006). One

such system is the AcrAB-TolC system, known substrates of which include

fluoroquinolones, β-lactams, rifampicin and tetracyclines. Although aminoglycosides are

not substrates, a transporter protein, AcrD, similar to AcrB, is known to efflux these

compounds. Overexpression of the AcrAB efflux system, often a result of mutation to the

regulator gene, acrR, has also been shown to enhance multidrug resistance (Wang et al.,

2001, Pradel and Pagès, 2002); however, amplification and sequencing of acrR did not

reveal any mutation.

To clarify the role of efflux in the resistance seen in this study, MIC testing was

performed both with and without an efflux pump inhibitor (EPI), phenylalanine arginine β-

naphthylamide (PAβN), present in the growth medium. Results indicate no increase in the

susceptibility of this bacterium to CIP in the presence of the EPI, indicating that efflux is

not the resistance mechanism employed by the bacterium (Fig. 1).

Given the number of antibiotics to which A. xylosoxidans ATCC 15446 showed

resistance, confirmation was needed that efflux via AcrAB was not the mechanism utilized.

To fully rule out the AcrAB system, quantitative, reverse transcriptase polymerase chain

reaction (Q-RT-PCR) was performed. Results indicate that not only was acrA not over

expressed in the presence of CIP, but it was, in fact, down regulated (Fig. 2). Given the

recent report that cTc inhibits the efflux via AcrAB-TolC but CIP does not (Bohnert et al.,

2010), our findings could suggest CIP stimulates the up regulation of some other system

that confers high level fluoroquinolone resistance.

100

Dose (μg/mL)

0 2 4 6 8 10

perc

ent m

axim

al g

row

th

-20

0

20

40

60

80

100

120

Cip Cip + PAβN

Fig. 1 – MIC for Ciprofloxacin (CIP) With and Without the Efflux

Inhibitor Phenylalanine Arginine Β-Naphthylamide (Paβn)

101

CIP dose (μg/mL)

Fig. 2 – Differences in Sample acrA RNA as Measured by Q-RT-PCR Using 0 μg

CIP/mL Sample as Calibrator

102

CONCLUSIONS

Intrinsic, low level antibiotic resistance in environmental bacteria has been known

for some time. Given that the majority of antibiotics are natural products, mainly of

Actinomycetes, such resistance is an evolutionary probability. However, finding high level

resistance to such a wide range of antibiotics in an archived environmental bacterium was

surprising. The most startling result of this investigation was this organism’s ability to

resist ciprofloxacin, a fully synthetic, bacteriocidal antibiotic introduced nearly a quarter of

a century after its isolation.

It was known as early as 1978 that within a given ecosystem bacteria could and

would share antibiotic resistance (AR) genes (Kelch and Lee, 1978). In soil microcosms,

A. xylosoxidans (then known as Alcaligenes xylosoxidans (Coenye et al., 2003)) has been

shown to horizontally transfer genes to the unrelated species, Pseudomonas fluorescens as

well as to indigenous soil bacteria (Brokamp and Schmidt, 1991). Horizontal gene transfer

(HGT) is one mechanism for acquisition of AR genes in the soil environment. However,

naked DNA can persist in soils, especially those high in organic matter (Levy-Booth et al.,

2007); thus, transformation can also occur. Moreover, competence can be induced in

ecosystems with high nutrient availability, as manured soil would be (Levy-Booth et al.,

2007), making acquisition of genetic material more likely.

The findings of this study reinforce the notion that the soil AR gene pool has the

potential to emerge in clinically important bacteria ( D’Costa et al., 2006). In fact,

multidrug resistance in clinical strains of A. xylosoxidans has been known for many years,

including almost complete resistance to β-lactams, aminoglycosides and quinolones

(Glupczynski et al., 1988). However, no investigation into the ecology of this resistance

103

was found in the literature and the findings reported here suggest that pre-antibiotic era

organisms may carry intrinsic resistance to even synthetic antibiotics, although the reason

for this is unclear.

There are possible mechanisms that were not investigated in the current study

which could account for one or two of the resistances seen in this bacterium, although none

would explain all. For example, one common mechanism of aminoglycoside resistance is

N-acetylation catalyzed by aminoglycoside acetyltransferases (AACs), of which AAC(6’)

is the most common class in nature (Vetting et al., 2008). Although no acetyltransferase is

known to provide protection against both streptomycin (STR) and kanamycin (KAN), a

plasmid-borne variant, AAC(6’)-Ib-cr, has been reported to confer resistance to both KAN

and ciprofloxacin (CIP) (Robicsek et al., 2006). Additionally, clinical resistance to RIF is

almost always a result target alteration due to mutation of the gene encoding the β subunit

of RNA polymerase, rpoB (Cohen et al., 2003), which may have no fitness cost over

generations due to compensatory mutation (Reynolds, 2000, Enne et al., 2004). However, a

study of the soil Streptomyces found that 40% of the resistant isolates utilized drug

inactivation rather than target alteration (D’Costa et al., 2006) although no specific

mechanisms were elucidated. Further investigation into the resistance mechanism(s)

utilized by A. xylosoxidans ATCC 15446 is warranted.

Given that soil organisms have been found to carry not only resistant determinants

but also virulence characteristics (Alonso et al., 1999) and that soil-borne transmission of

virulence factors as well as pathogenicity islands has been documented (Burgos et al, 2005,

Qui et al., 2006), our finding of a multidrug resistant, pathogenic, archived soil bacterium,

known to be capable to gene transfer to unrelated species, gains even greater significance

104

and justifies enhanced concern. The fact that known fluoroquinolone resistance

mechanisms were found to account for only 50-70% of the resistance seen in E. coli

(Morgan-Linnell et al., 2009) indicates that further work to elucidate the resistance

mechanism(s) utilized by A. xylosoxidans ATCC 15446 is crucial, particularly in light of

the fact that efflux may not be involved.

105

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Chapter 4

Manure as a Selective Pressure for the Proliferation of Antibiotic Resistance in the Soil Environment

ABSTRACT

With the growth of organic farming, an increasing amount of manure is being

land applied. However, intensive animal husbandry relies on the extensive use of

prophylactic antibiotics (AB). These compounds have been found to be present in

manures, along with antibiotic resistant (AR) enteric bacteria and AR determinants.

Additionally, manure has been found to increase residence time in soil of both AB and

naked DNA. Thus, the possibility exists that land application of manure would result in

an increased incidence of AR in the soil bacterial community. As environmental strains

of several pathogenic species are known, this situation could result in the spread of AR to

clinically relevant bacteria.

A microcosm study was designed to assess whether the addition of untainted

manure would result in an increase in the incidence of AR in the soil bacterial community

or if the increased resistance associated with manure application results from the

antibiotics, resistant enteric bacteria and resistance determinants often present in manure.

Treatments included soil only and soil + manure receiving 0, 1, 10 or 25 µg cTc/g soil

(dw). Plate counts revealed that although the manured microcosms had higher resistant

counts, these bacteria accounted for a smaller percentage of the total community while

the greatest percent AR was seen in the soil only control, likely resulting from stress

response. Molecular analysis revealed little change in bacterial community structure with

manure addition or with time. However, the addition of 25 µg cTc/g soil favored

111

Pseudomonas species. Thus, the land application of untainted manure may actually

improve soil resilience and, thereby limit the proliferation of AR in the soil environment.

INTRODUCTION

Since the implementation of the National Organic Program (NOP) in 2002, U.S.

cropland under organic management has increased from approximately 1.3 million acres

to over 2.6 million acres in 2008, with an annual increase in organic cropland use of 15%

(USDA, 2010). Organic land management includes land application of animal wastes,

which are rich in carbon as well as plant nutrients such as nitrogen and phosphorous.

According to the NOP, raw manure may be used as long as incorporation into soil occurs

90-120 days prior to harvest, depending on the crop, while composted manure may be

applied at any time (USDA, 2000). There are no restrictions in place that limit the source

of manure.

Millions of pounds of antibiotics (AB) are used annually in animal husbandry at

subtherapeutic levels (Chee-Sanford et al., 2009). This practice is especially common at

high intensity operations, such as feedlots. As much as 100% of these AB are excreted

(see Appendix G in Boxall et al., 2002). In addition to its nutritive value, manures from

around the world have been found to contain a variety of antibiotics at mg/kg levels

(Christian et al., 2003, Martínez-Carballo et al., 2007, Zhao et al., 2010). Moreover,

prophylactic use of AB is known to increase the incidence of antibiotic resistant (AR)

enteric bacteria (Huber, 1971, Langlois et al., 1986), which are also present in manure

(Alexander et al., 2009). These bacteria may secret DNA into the soil environment as a

112

result of low nutrient availability (Chee-Sanford et al., 2009), as has been shown to occur

with cultured soil bacteria at all growth phases (Lorenz et al., 1991).

Various soil components can bind both AB and naked DNA including, in order of

increasing binding ability, sand, 1:1 clay, 2:1 clay and humic substances (Sithole and

Guy, 1987, Levy-Booth et al., 2007, Martíntez-Carballo et al., 2007, Heuer et al., 2008).

Bound AB and DNA do not lose their bioactivity while, at the same time, are protected

against degradation so can persist in soils (Aardema et al, 1983, Crecchio and Stotzky,

1998, Hamscher et al., 2002, Chander et al., 2005). Additionally, exchange of genetic

material is known to take place in soils, with rates of conjugation and transformation

dependent on soil conditions (Lorenz and Wackernagel, 1991, Khanna and Stotzky, 1992,

Götz and Smalla, 1997, Crecchio and Stotzky, 1998). One study found that attachment to

sand grains increased transformation in Bacillus subtilis 25-50 fold over the standard

liquid procedure and required almost 2 orders of magnitude more DNase for inhibition

(Lorenz et al., 1988). Other studies have indicated that soils receiving manure have

increased occurrence of antibiotic resistance (AR) genes and AR bacteria (Götz and

Smalla, 1997, Onan and LaPara, 2003, Sengeløv et al., 2003, Storteboom et al., 2007,

Alexander et al., 2009).

Carbon, nitrogen and phosphorous, nutrients readily available in manure, have

been shown to dramatically increase the rate of transformation in Pseudomonas stutzeri

in minimal medium, likely due to the development of competence (Lorenz and

Wackernagel, 1991). Thus, land application of manure could increase competence and,

consequently, transformation. Moreover, farmyard manure has been found to increase the

labile fraction of two both zinc and lead in soil (Santos et al., 2010). Heavy metal and

113

antibiotic resistance determinants are often located on the same genetic elements. Thus,

both the resulting increase in available metals as well as antibiotics present in manure

could exert selective pressure on soil bacteria while enteric bacteria in manure could

introduce resistance determinates. These enteric bacteria can transfer not only AR

determinants but also large pathogenicity islands, as has been found to occur with the

pathogenic soil organism, Pseudomonas aeruginosa (Qui et al., 2006) and virulence

factors (Burgos et al., 2005).

Soil bacteria can also carry intrinsic antibiotic resistance, as was shown in

Chapter 3, with low-level resistance fairly common compared to high-level (Esiobu et al.,

2002). In fact, many clinically relevant AR determinants have been shown to originate in

environmental bacteria (Cantón, 2009). Thus, both animal and human pathogens can

acquire genes from soil organisms, such as actinobacteria and proteobacteria, the source

of many of the genes studied in Mycobacterium avium subsp. paratuberculosis (Marri, et

al., 2006). Other examples of AR mechanisms found to originate in environmental

organisms include qnr-like genes, β-lactamases, tetracycline resistance determinants,

acetyltransferases and genes encoding efflux pump proteins (Nwosu, 2001, Alonso et al.,

2001, Séveno et al., 2002, Riesenfeld et al., 2004).

A microcosm study was designed to assess the role manure plays in the

proliferation of AR in the soil environment , both in the presence and absence of an

antibiotic at subtherapeutic levels. It was hypothesized that manure alone will increase

the incidence of AR in the bacterial community due to mobilization of metals, increased

residence time of bound AB and DNA, and increased competence. Furthermore, the

combination of manure plus selective pressure, as would occur with land application of

114

AB tainted manure, would result in even greater incidence AR. Since the compound most

commonly used as a feed additive is chlortetracycline (cTc) (Storteboom et al., 2007),

this drug was selected for use in this study.


Soil Collection and Microcosm Set Up

Soil was collected on March 4, 2010, from a plot located on Rutgers University’s

New Brunswick campus that was organically managed since 2005, prior to which it was

planted to grass for many years. Soil at this location is classified as a Nixon variant loam

of 44% sand, 41% silt and 15% clay (NRCS, 2008). Soil at the sampling location had

been fallow for a minimum of 1 year prior to sampling and was covered with grass and

perennial weeds at the time of sampling. The surface soil (0-10 cm) was collected from a

650 cm2 area and composited. Soil was sieved to 2 mm, air dried overnight and stored at

4°C. Final moisture content of the stored soil was approximately 13% (w/w).

Two soil conditions were tested: soil only and soil + manure. Microcosms were

set up in glass jars measuring 5.5 cm I.D. and 6.5 cm high. Ten grams (dry weight) of soil

was placed in each jar, to a height of approximately 0.4 cm. Partially composted cow

manure was obtained from a pile near the soil sampling site. The manure was then air

dried and sieved to 2 mm prior to storage. Before use, the manure was rewetted to 60%

moisture, a reasonable moisture content of fresh manure. Half the microcosms received

one gram of this moist manure, representing an application rate of 61.2 Mg/ha, a realistic

field application rate, assuming a 12 inch plow depth and a bulk density of 1 g/cm3(see

Fig. 1 for set up and notation).

115

0(-) 0(+)

1(-) 1(+)

10(-) 10(+)

25(-) 25(+)

Fig. 1 – Schematic of Microcosm Setup for One Replicate of One Sampling Time

and Notation of Treatments.

116

Control microcosms consisted of soil only and soil + manure, with no antibiotic

added. The remaining microcosms each received a single dose of chlortetracycline (cTc),

diluted in 10% ethanol, at a rate of 1, 10 or 25 μg cTc/g dry soil. Moisture content was

adjusted to 20%, approximately half the saturated water content. The weight of each

microcosm was recorded for moisture adjustment during the incubation. Samples were

taken on days 1, 4, 7, 14 and 31. Samples of soil only, manure only and soil + manure, as

described above, were taken at day 0 for a baseline. Microcosms were set up in triplicate

for each treatment/time point and were sacrificial.

Microcosms were randomly arranged and incubated in the dark at room

temperature (approximately 22°C). Remaining microcosms were returned to their original

weight with tap water on days 8, 16 and 27 of the incubation. The soil was not stirred

after water addition to limit clumping of the soil.

Viable Plate Counts

A 10 g sample of soil, composited from triplicate microcosms, was added to 50 mL of

0.01M Phosphate buffer, shaken for 10 minutes and centrifuged at 2,600 G for 10

minutes. Supernatant was serially diluted using phosphate buffer then spread, in

triplicate, on soil extract (SE) agar plates with either no cTc or with 10 μg cTc/mL agar to

determine low-level resistance (cTc10R), 25 μg cTc/mL agar to determine mid-level

resistance (cTc25R) or 50 μg cTc/mL agar to determine high-level resistance (cTc50R).

The accepted resistance breakpoint for tetracycline is 16 μg/mL (CLSI, 2008). Plates

were incubated in the dark at 30°C for four days, at which time colony forming units

were counted. Plates were refrigerated and reread after approximately 1 month. This was

117

done to allow pinpoint colonies to grow and for better discrimination between bacterial

and fungal growth. Except as noted below, recount data were used in all statistic analysis.

Statistical Analysis

Significance of the plate count data was tested using one-way analysis of variance

(ANOVA) and multivariate analysis of variance (MANOVA) at the 95% probability

level (SPSS Statistics 17.0, IBM Corp., Somers, NY). All statistics were performed on

triplicate plate counts except the soil only control (0(-)) and the soil only with 1 μg cTc/g

soil treatment (1(-)) from day 7, for which only duplicates were available. Due to all

counts being above the 95% confidence interval, counts from the four day reading were

used for analysis of 0(-) on day 7. For all other treatments, recount data (from the one

month reading) were used.

DNA extraction, Polymerase Chain Reaction and Sequencing

Mo Bio’s UltraClean Soil DNA extraction kit was used to obtain DNA from soil

composited from triplicate microcosms (MO BIO Laboratories, Inc., Carlsbad, CA),

which was then frozen at -20°C until needed. Polymerase chain reaction (PCR) was

performed using 16S rRNA universal bacterial primers, 27F;519R. To bind potential

inhibitors, bovine albumen serum (BSA) was added to the PCR reaction at a rate of 1μL

BSA/50μL reaction. Denaturing gradient gel electrophoresis (DGGE) was run on the

PCR products to separate amplicons with varying nucleic acid sequence. DGGE was run

for 17 h at 55 v, fixed in an acetic acid bath and stained with SYBRGreen I. Bands of

interest were cut from the gel and eluted at 4°C overnight in either 50 or 15 mL sterile

118

filtered DI water (Q-H2O). Eluted DNA was then reamplified as described above and sent

for sequencing (GENEWIZ, South Plainfield, NJ). Sequences were assembled and

analyzed using SeqMan (DNASTAR, Inc., Madison, WI). Phylogenetic analysis was

performed using MEGA 4.1 (Tamura et al., 2007). A phylogenetic tree was constructed

using the maximum composite likelihood model with neighbor-joining (1,000 reps and

pairwise deletion). Type species were included whenever possible.


Total Cultivable Plate Counts

The addition of cTc did impact total cultivable plate counts (total counts),

although not in the way expected. In the soil only treatments, the counts on day 4 from

the 25 μg cTc/g soil treatment, 25(-), were significantly higher than all other non-

manured soil counts throughout the incubation (Fig. 1A and Appendix 1), while the 1(-)

and 10(-) treatments showed no significant change over time. In the soil + manure

treatments, 10(+) was significantly higher than 1(+) and 0(+) on day 4, while 25(+) was

significantly higher on sampling days 1 through 14 (Fig. 1B). Like 25(-), the day 4 count

as well as the days 7 and 14 counts from 25(+) were higher than all other soil + manure

counts (see Appendix 1). As with the soil only treatments, 1(+) and 10(+) did not show

significant change over time. Analysis of the data by MANOVA confirmed the impact of

cTc concentration on total counts in each treatment and across treatments (p = 0.000 for

all). When all data were analyzed, MANOVA also showed a significant impact of

sampling day (p = 0.011). However, when the two treatments, (-) and (+), were analyzed

119

Fig. 2 - Total biomass counts from SE plates in log scale. Straight lines indicate

linear regression of no-cTc control counts. A) soil only; B) soil + manure.

120

separately, only the non-manured data showed sampling day to be significant (p = 0.001).

This is likely due to the fact that on day 4, the 25(-) cultivable count was significantly

higher than all other time points, whereas the 25(+) counts had similarity to at least one

other time point.

When assessing the impact of manure on the total counts, no significant

differences were seen between 1(-) and 1(+) at any time point (Fig. 2A). Total counts

from 10(+) were significantly higher than 10(-) on day 7 (p = 0.035) and, to a lesser

extent, on days 1 and 14 as well (p = 0.051 and 0.085, respectively) (Fig. 2B). The counts

from 25(+) were highly significantly greater than those from 25(-) until day 31 (p ≤

0.007), at which time there was no significant different between the treatments (Fig. 2C

and Appendix 1).

Low-level cTc Resistant Plate Counts

Cultivable low-level cTc resistant (cTc10R) bacteria from 0, 1 or 10 μg cTc/g soil-

only microcosms did not significantly change over time (see Appendix 2). However, by

day 7, 25(-) had significantly higher cTc10R than at any other time point except day 14 (p

≤ 0.005) (Fig. 3A). Likewise, the counts from day 14 were significantly higher than from

day 31 (p = 0.032), although not significantly so from days 1, 4 and 7 (see Appendix 2).

Looking across cTc dose at each time point, only on day 7 was a significant difference

seen, with 25(-) higher than all other treatments (p ≤ 0.032) (Fig. 3A).

Significant differences in cTc10R counts were seen on at least one sampling day

in all manure-amended microcosms, except 10(+). In the control microcosms, 0(+),

121

Fig 3 - Comparison of total biomass counts within a cTc concentration in log scale.

A) 1 μg/g soil; B) 10 μg/g soil; C) 25 μg/g soil.

122

Fig. 4 - cTc10R counts in log scale. Straight lines indicate linear regression of

no-cTc control counts. A) soil only; B) soil + manure.

123

cTc10R counts were significantly higher on day 14 than on days 1 and 4 (p = 0.016 and

0.045, respectively) and those from the 1(+) microcosms were significantly higher on day

7 than on day 1 (p = 0.032) (Fig. 3B). Although the actual counts from 1(+) were highest

on day 14, this count was not significantly different from any other time point (see

Appendix 2) due to the high variability inherent in plate counting. Significant differences

in the 25(+) microcosms were seen, with cTc10R counts from days 4 and 7 significantly

higher than day all other days (Fig. 3B and Appendix 3). Additionally, on both day 4 and

7, cTc10R counts from 25(+) were significantly higher than 1(+) and 10(+) (p = 0.000 on

day 4 and p ≤ 0.003 on day 7).

Manure did not impact cTc10R in the 0, 1 or 10 μg cTc/g soil microcosms (Fig.

4A and 4B); however, on day 4, 25(+) showed significantly more cultivable cTc10R

bacteria than did 25(-) (p = 0.000) (Fig. 4C). This difference may explain why

MANOVA indentified manure addition as significantly impacting cTc10R counts

Moderately cTc Resistant Plate Counts

Counts of moderately cTc resistant (cTc25R) bacteria differed significantly with

time except in the soil-only control (Fig. 5 and Appendix 3). Counts from day 14 were

significantly higher than those from day 1 for 1(-) (p = 0.001) (Fig. 5A). Counts from this

day were also significantly higher than those from all other days in the 10(-) microcosms

(p ≤ 0.008). Similarly, cTc25R on days 7 and 14 was higher in 25(-) than on days 1, 4 and

31 (p = 0.000 and p ≤ 0.006, respectively). Greater cTc25R was seen in 25(-) on day 7

124

Fig. 5 - Comparison of cTc10R counts within a cTc concentration in log scale.


125

Fig. 6 – cTc25R counts in log scale. Straight lines indicate linear regression of no-cTc

control counts. A) soil only; B) soil + manure.

126

compared to all other treatments (p = 0.000 for all) and all cTc treatments showed greater

cTc25R than 0(-) on day 14 (p ≤ 0.048) (Fig. 5A). Thus, cTc25R increased in all soil-only

treatments, peaking on day 14, although this level was reached sooner (day 7) in 25(-)

and was maintained through two sampling times.

In the manure amended control, cTc25R was significantly lower at the end of the

incubation than on days 7 and 14 (p = 0.043 and 0.022, respectively) (Appendix 3). For

1(+) and 10(+) treatments, counts from day 14 showed significantly greater cTc25R than

all other time points (p = 0.000 and ≤ 0.002, respectively) (Fig. 5B). Differences in

moderate resistance were insignificant between days 7 and 14 in 25(+) and both days

showed significantly greater cTc25R than day 31 (p = 0.002 and 0.019, respectively).

Counts from day 7 were also higher than those on day 1 (p = 0.010). As with the soil-

only treatments, all treatments showed the highest cTc25R on day 14 with 25(+) reaching

the same degree of resistance by day 7.

Although the difference between 25(-) and 25(+) on day 7 was determined to be

significant (p = 0.000), this result is believed to be an artifact (Fig. 6C). No significant

differences were seen with manure addition at cTc doses of 1 and 10 μg/g soil (Fig. 6A

and 6B), MANOVA indicates manure treatment was not a significant factor in moderate

resistance (p =0.222). Thus, manure addition did not impact cTc25R (Fig. 6).

High-level cTc Resistant Plate Counts

While the high-level cTc resistant (cTc50R) bacteria increased in the soil-only

control from day 1 to day 31 (p = 0.048), only counts from days 4 and 14 were

significantly different in the soil + manure control (p = 0.016) (Appendix 4). As with

127

Fig 7 - Comparison of cTc25R counts within a cTc concentration in log scale.


128

Fig. 8 – cTc50R counts in log scale. Straight lines indicate linear regression of no-

cTc control counts. A) soil only; B) soil + manure.

129

cTc25R, cTc50R appears to have peaked on day 14 (Fig. 7); however, cTc50R did not

significantly decline from day 14 to day 31 in the soil-only microcosms (Fig. 7A). Over

the course of the incubation, cTc50R in 1(-) was significantly higher on day 14 than days

1, 4 and 7 (p ≤ 0.011) and was also higher on day 31 than on days 1 and 4 (p = 0.036 and

0.008, respectively). In 10(-), resistance was significantly higher on days 14 and 31

compared to days 1, 4 and 7(p ≤ 0.007) (see Appendix 4). Only resistance in 25(-) decline

enough on day 31 for this count to be significantly lower than that from day 14 (p =

0.000). While cTc25R peaked in 25(-) on day 7, this was not the case with cTc50R, which

only approached significantly higher levels on day 7 (p = 0.065). However, the pattern of

resistance in all treatments being significantly higher than the control on day 14 held true

(p ≤ 0.013). The same was seen in the soil + manure microcosms, with significantly

higher counts attained on day 14 in all treatments (Fig. 7B). However, with 10(+), the

count from day 1 was surprisingly high, equal to that of day 14. This is likely an artifact

of the plate count technique.

The impact of manure was seen at one sampling point in all cTc treatments. It is

interesting to note that, in 1(-) and 1(+), the peak cTc50R achieved on day 14 was

impacted by the presence of manure (Fig. 8A). Only counts from day 1 differed

significantly between 10(-) and 10(+) (Fig. 8B), although the count from 10(+) is likely

in error. Although both 25(-) and 25(+) reached the same cTc50R peak on day 14, the

presence of manure allowed 25(+) to reach this level on day 7 (Fig. 8C).

130

Fig 9 - Comparison of cTc50R counts within a cTc concentration in log scale.


131

Percent Resistant

While the soil + manure microcosms generally exhibited higher resistant counts

compared to the soil-only, these also had higher total counts. To better understand the

overall trend, resistance was normalized by the total count and looked at as a percentage

(Fig. 9). No increase in low (cTc10R) and moderate (cTc25R) resistance was seen over

time in the soil + manure microcosms but, rather, resistance appears to be at a steady

state. Although bound antibiotics retain their bioactivity (Chander et al., 2005), diffusion

of antibiotics into the organic matter matrix (Sithole and Guy, 1987) may physically

remove them from bacteria, thereby reducing or eliminating selective pressure. At the

same time, bacterial communities associated with manured soils are known to be more

resilient and less susceptible to stress when compared to communities from non-manured

soils (Wada and Toyota, 2007). Thus, the ability of manure to limit stress also appears to

limit the incidence of AR bacteria in the soil environment in the absence of introduced

resistant enteric organisms or AR determinants.

Conversely, the lack of resilience can be seen in the incidence of AR in the soil

only control, which attained the highest percent resistant bacteria for all cTc resistance

levels, followed closely by 1(-) (Fig. 9). This increase may be attributable to stress

response, which, along with various housekeeping genes, has been implicated in

environmental AR (Aminov and Mackie, 2007). Although little change in percent AR

was seen in the 10(-) and 25(-) microcosms, this may be due to the bacteriostatic effect of

cTc, which would be less bound in the soil-only microcosms.

At the same time, high level resistance (cTc50R) increased substantially in 0(+) as

well as in 25(-) (Fig. 9c). Similar to the non-manured control, the increase in 0(+) could

132

Fig. 10 – Percentage of Bacterial Community Resistant to cTc by cTc and Manure

Treatment. 0(-) = ♦, 1(-) = ●, 10(-) = ▲, 25(-) = ■, 0(+) = ◊, 1(+) = ○, 10(+) =

∆, 25(+) = □. A) cTc10R; B) cTc25R; C) cTc50R.

133

be due to stress, although likely not from carbon limitation. Conversely, stress is not a

reasonable explanation for the AR increase seen in 25(-), which may, instead, result from

a priming effect of the added cTc on some unknown, intrinsic resistance mechanism.

Multivariate Analysis of Variance (MANOVA)

When the plate count data were analyzed with MANOVA, it was seen that

moderate (cTc25R) and high level (cTc50R) resistance was not impacted by the presence

of manure, although total counts and low-level resistance (cTc10R) counts were (Table 1).

At the same time, when data from all treatments were analyzed, cTc concentration in the

microcosm did effect counts at all resistance levels as did the incubation time (sampling

day) . There was a significant interaction between these two variables in all treatments

although significant interaction between all three variables (manure, cTc dose and time)

was only seen with the cTc50R counts.

Looking at resistance in the soil only and the soil + manure microcosms

separately, the soil only microcosms followed the same pattern as the complete dataset in

that dose and time were both significant, with significant interaction between these two

(Table 1). In the soil + manure microcosms, cTc dose only impacted total and cTc10R

counts but not cTc25R or cTc50R counts, suggesting that the bacteria may not have been

seeing the full dose applied to the microcosms. Soil particles, particularly clay, are known

to bind antibiotics (Demanèche et al., 2001). However, bound antibiotics have been found

to retain their bioactivity (Chander et al., 2005). If binding were occurring in the present

study, it is likely not to soil mineral particles since these are the same in all treatments. In

134

Table 1 – Multivariate Analysis of Variance (MANOVA) of Plate Count Data

Manure cTc Dose

Sampling Day

Manure X cTc

Manure X Day

cTc X Day

Manure X cTc X Day

Total

All 0.000 0.000 0.011 0.000 0.131 0.013 0.691

Soil-Only 0.000 0.001 0.001

Soil + Manure 0.000 0.070 0.244

cTc10R

All 0.000 0.000 0.000 0.469 0.177 0.004 0.203

Soil-Only 0.000 0.000 0.001

Soil + Manure 0.005 0.001 0.105

cTc25R

All 0.222 0.011 0.000 0.200 0.760 0.000 0.360

Soil-Only 0.000 0.000 0.002

Soil + Manure 0.881 0.000 0.115

cTc50R

All 0.151 0.000 0.000 0.720 0.001 0.000 0.000

Soil-Only 0.001 0.000 0.005

Soil + Manure 0.120 0.000 0.000

135

contrast, organic matter was added only to those microcosms that received manure.

Organic matter also binds antibiotics and, while some binding may occur at the surface of

humic materials, some results from diffusion of the antibiotic into the humic matrix

(Sithole and Guy, 1987). This sequestration of the AB could physically separate the

compound from the target organisms, rendering it ineffective as a selective agent despite

remaining bioactive.

Molecular Analysis

Denaturing gradient gel electrophoresis (DGGE) allows “fingerprinting” of

bacterial communities from environmental sources. Given the bias of PCR, it is not

possible to judge population size from band intensity. Thus, the pattern itself must be

analyzed as a whole. Samples loaded on a single gels included both soil-only and soil +

manure for a given cTc dose (Fig. 10). Over time, the “fingerprint” pattern does not

change in the control microcosms and there seems to be little, if any, difference in the

patterns seen with the soil-only and the soil + manure (Fig. 10A). This result mirrors that

of Heuer et al. (2008) who reported that bacteria originating in manure do not become

dominant in the soil bacterial community. Thus, the addition of air dried manure at a rate

of 20Mg/ha did not contribute unique members of the bacterial community or, if it did,

these organisms were present in very low numbers and did not grow over the course of

the incubation. The same is true of the 1 μg/g soil microcosms (Fig. 10B). In addition to

no manure effect being discernible, comparison with the gel of control microcosms

suggests this extremely low dose had little impact on the overall structure of the

microbial community.

136

4 7

6 2

5 1

38 9

31‐0(+)

14‐0(+)

7‐0(+)

4‐0(+)

1‐0(+)

0‐0(+)

31‐0(‐)

14‐0(‐)

4‐0(‐)

7‐0(‐)

1‐0(‐)

0‐0(‐)

Fig. 11 – Denaturing Gradient Gel Electrophoresis of Soil Only and Soil + Manure

Microcosms Over Time. Lane markers indicate, in order, sampling day –

cTc dose (± manure). Bands to right of numbers were cut and sequenced.

A) No cTc Control Soils

137

12

9

8

7

11

10

5 4

3

6

2

1

31‐1(+)

14‐1(+)

7‐1(+)

4‐1(+)

1‐1(+)

31‐1(‐)

14‐1(‐)

7‐1(‐)

4‐1(‐)

1‐1(‐)

Fig. 11B – 1 μg cTc/g soil.

138

16 17 18

15

14

13

12

11

10

98

7

6

5

4

3

2

1 31

‐10(+)

14‐10(+)

7‐10(+)

4‐10(+)

1‐10(+)

31‐10(‐)

14‐10(‐)

7‐10(‐)

4‐10(‐)

1‐10(‐)

Fig. 11C – 10 μg cTc/g soil.

139

1‐25

(+)

4‐25

(+)

14‐25(+)

31‐25(+)

7‐25

(+)

31‐25(‐)

14‐25(‐)

1‐25(‐)

4‐25(‐)

7‐25(‐)

12

13

11

10

9

8

7

6

5

4

3

2

1

Fig. 11D – 25 μg cTc/g soil.

140

Conversely, there do appear to be differences between these treatments and the

moderate and high cTc treatments. The gel from the 10μg cTc/g soil microcosms shows

fewer defined bands at the top of the gel, representing high A+T sequences (Fig. 10C),

while that from the 25μg cTc/g soil microcosms shows more clearly defined bands in this

region (Fig. 10D). From these gels, there does not appear to be a loss or gain of bands

with manure addition, indicating that the resistant bacteria obtained on plates are native to

the soil and did not originate in the manure. These findings are in keeping those of Heuer

et al. (2008), who found that bacteria originating from manure did not become evident in

profiles of manure amended soil. While Zielezny et al. (2006) reported cTc did not

impact bacterial community structure as assayed with DGGE, bands 2, 8 and 10 on the 25

μg/g gel are much more distinct than on the others (Fig. 10D).

Bands were cut from each gel, as indicated by numbers in Fig. 10, amplified and

sequenced. These sequences were then used to construct a phylogenetic tree (Fig. 11).

Bands from different soil/cTc treatments and from different sampling days within a

soil/cTc treatment grouped together, indicating similarity in bacterial community

composition between treatments and over time. However, bands that clustered with the

Pseudomonas genus of γ-Proteobacteria were all from the 25 μg cTc/g soil microcosms

(bands 2502, 2508, 2509 and 2510). Bands in a similar position to these were cut from

the 0 μg cTc gel (bands 003, 008 and 009); however, they showed relatedness to

Bacteroidetes as did a fifth band from the same region of the 25 μg/g gel (2503). Bands

1004, 1005, 1010 and 1011 were also from the same region but these showed relatedness

to Actinobacteria, Chloroflexi and Acidobacteria (Fig. 11). It is reasonable that

Pseudomonas sp. would be enriched by ethanol addition, which was 2.5 times greater in

141

α-Proteobacteria

γ-Proteobacteria

δ-Proteobacteria

Acidobacteria

Bacteriodetes

Actinobacteria

Chloroflexi

Fig. 12 – Phylogenetic tree of DGGE bands. ID’s refer to microcosm cTc concentration

(first 1-2 digits) and band number (last 2 digits). ♦ = no cTc; ● = 1 μg/g; ▲= 1

μg/g; ■ = 25 μg/g.

142

the 25 μg/g microcosms, given that these bacteria metabolically versatile and are known

to oxidize ethanol to acetyl-CoA through acetate (Arndt et al., 2008). Additionally,

Pseudomonas sp. have been reported to comprise 30% of resistant bacterial communities

from a variety of environmental sources (Esiobu et al, 2002).

CONCLUSIONS

Although the overall number of cultivable antibiotic resistant (AR) bacteria was

generally higher in the manured microcosms, these organisms represented a smaller

percentage of the total community. Contrary to previous reports indicating manure

addition increases in incidence of AR in the soil environment (Onan and LaPara, 2003),

this finding suggests the presence of manure may limit the proliferation of cTc resistance

in the bacterial community. This may be the result of diffusion of the antibiotic to the

interior of the humic matrix (Sithole and Guy, 1987), which may physically remove AB

from target organisms. Moreover, manure is known to increase soil resilience (Wada and

Toyota, 2007) and, therefore, less stress response may have occurred in the manured

microcosms. It has been proposed that much of the environmental antibiotic resistance

seen is a result of stress response and housekeeping genes (Aminov and Mackie, 2007 ).

It must be pointed out that the manure utilized in this study was obtained from

cows not fed antibiotics, thus did not contribute additional AB or AR organisms and

determinants to the soil. Antibiotic-tainted manure would likely render different results

than were seen. By adding nutrients, organic matter, antibiotics and AR bacteria, land

application of AB-tainted manure could create an ideal situation for the proliferation of

AR in the soil environment.

143

Our data additionally suggest at least two different intrinsic cTc resistance

mechanisms: one resulting from stress response, as seen with the soil only control, and

one induced by exposure to cTc in soil, as seen with 25(-). While the resistance

associated with housekeeping and stress response genes is generally believed to be low

level (Esiobu et al., 2002), our results suggest AR resulting from these genes may

contribute high level resistance to environmental bacteria, as seen in the soil only control.

While the induced cTc50R seen in 25(-) was transient, peaking on day 14 then declining,

likely due to reduction of selective pressure, this would be sufficient time for transfer of

resistance genes in the soil environment. On the other hand, AR in 0(-) continued to rise

throughout the incubation. Thus, resistance resulting from stress response may also have

long-term impact on the soil community.

144

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Appendix 1 – ANOVA significance values for total counts

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Appendix 2 – ANOVA significance values for cTc10R counts

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CONCLUSIONS

The studies described in Chapters 1 and 4 indicate that manure amendment can

impact the proliferation of antibiotic resistance (AR) genes in the soil environment, even

years after amendment ceases; however, the direction of change will depend on the

source of manure. Manure amendment was shown to have a residual positive effect on

soil properties associated with increased residence time of antibiotics and naked DNA,

transformation and plasmid mobility including water retention, organic matter content,

microbial activity, cation exchange capacity and pore size distribution while acidic food

processing waste was found to negatively impact these properties. Thus, manure

amendment could encourage AR proliferation.

To further exacerbate the situation, animal husbandry practices that include

prophylactic use of antibiotics could increase the incidence of resistant enteric species in

livestock, which would also be present in the waste products of these animals, resulting in

the environmental spread of resistance determinants as well as unmetabolized antibiotics.

Once introduced into an ecosystem, resistant genotypes will carry the cost of AR gene

maintenance and can successfully compete with susceptible indigenous bacterial

populations in the absence of antibiotic pressure (Aminov & Mackie, 2007), since the

cost of maintaining resistance determinants may be ameliorated by compensatory

mutation (Andersson & Levin, 1999). Thus, it is unlikely that AR genes will be removed

from the environmental genetic pool once introduced and return to susceptibility is

improbable. However, manure free of antibiotics, AR bacteria and AR determinants may

slow the spread of resistance in the soil environment through sequestration of introduced

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antibiotics within the humic matrix and improved soil resilience, as was seen in the soil +

manure microcosms dosed with 25 μg cTc/g soil (dry weight).

It is clear from these results that manure management may be crucial in extending

the life the existent antibiotic compounds. Storteboom et al. (2007) found that the

addition of nitrogen and a bulking agent to feedlot and dairy manures, along with regular

watering and turning, resulted in a significant reduction in both antibiotic levels and AR

determinants while Heinonen-Tanski et al. (2006) suggest several methods of

management to reduced the number of pathogenic bacteria in manure. As it is unlikely

that the United States will follow the European Union’s lead and prohibit the use of

antibiotics for growth promotion, results from the current studies indicate a ban on the

use of raw manure in organic crop production would be prudent, in the interest of public

health.

Principal Component Analysis (PCA) of metabolic diversity data was found to be

insufficient for identification of changes in bacterial community function associated with

organic waste amendment. A new statistical method was developed whereby two

communities are directly compared using a y = x relationship, as described in Chapter 2.

While PCA gives an overall picture of similarity or difference, one-to-one comparison

shows differences in community metabolic capabilities for specific substrates. Thus,

when both analyses were applied to the manure amended and control soils (MA and MC),

one-to-one comparison showed differential utilization of 11 substrates while PCA did not

differentiate between the soils. Additionally, when applied to the cranberry amended

soils, one-to-one comparison showed differential utilization of 25 of the 31 substrates,

clarifying the separation obtained with PCA. Use of the one-to-one comparison method

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with more specific substrates could aid in the classification of indigenous soil bacteria

exhibiting antibiotic resistance.

Traditionally, intrinsic AR in environmental organisms has been thought to offer

low level resistance only. However, our investigations indicate high level cTc resistance,

likely resulting from both stress response and an inducible resistance mechanism (see

Chapter 4) as well as intrinsic multidrug resistance with clinical implications (see Chapter

3). Antibiotic resistance has been linked to several bacterial stress response systems

including superoxidative stress response (SoxRS), SOS DNA damage response, RpoS

general stress response, and omotic stress response (Stewart and Costerton, 2001,

Hastings et al., 2004). Furthermore, DinB has been linked to both SOS and RpoB and

induces adaptive mutation in times of stress, thus could result in a greater number of

resistant genotypes (Hastings et al., 2004). While several studies discuss the introduction

of AR genes into the environment by commensal bacteria, little research has been done

on the introduction of intrinsic resistance genes to clinically relevant bacteria in the

environment. Many indigenous soil bacteria are environmental strains of human and/or

animal pathogens, for example Bacillus anthracis, Escherichia coli, Klebsiella spp.,

Listeria spp., Mycobacterium tuberculosis, Yersinia spp. (Mawdsley et al., 1995),

Clostridia spp., Pseudomonas spp. and Achromobacter xylosoxidans. In addition, transfer

of genetic elements has been shown to take place in soil, for example between

Achromobacter xylosoxidans and Pseudomonas fluorescens (Brokamp and Schmidt,

1991). The study of intrinsic mechanisms that contribute high level antibiotic resistance

to non-pathogenic soil bacteria should be included in the development of new therapeutic

155

compounds to better extend the useful life of these drugs. To this end, further molecular

characterization of the Achromobacter xylosoxidans studied here is warranted.

In conclusion, results from this project indicate that: 1) multiple drug resistant

(MDR) bacteria are indigenous to soil, regardless of prior exposure to antibiotics; 2) this

intrinsic resistance can be very high level; 3) soil conditions controlling the transfer and

acquisition of genetic elements can be altered, either in favor or against, by land

application of wastes; 4) manure free of antibiotics and resistant enteric organisms may

reduce the expression of antibiotic resistance, possibly through lessening of bacterial

stress response. These findings strongly suggest the need for regulation regarding the

application of raw manure to cropland in order to extend the efficacious life of current

antibiotic compounds. Additionally, the results clearly show the limitation in current

knowledge of high-level resistance mechanisms and the importance of investigating these

capabilities beyond clinical strains. If the development of resistance could be anticipated,

next generation antibiotics could be designed to side step these potentially clinically-

relevant mechanisms.

156

References


Andersson, D.I., and Levin, B.R. 1999. The biological cost of antibiotic resistance. Curr. Opin. Microbiol. 2:489-493.

Brokamp, A., and Schmidt, F.R.J. 1991. Survival of Alcaligenes xylosoxidans degrading 2,2-dichloropropionate and horizontal transfer of its halidohydrolase gene in a soil microcosm. Curr. Microbol. 22:299-306.

Hastings, P.J., Rosenberg, S.M., Slack, A. 2004. Antibiotic-induced lateral transfer of antibiotic resistance. Trends Microbiol. 12(9):401-404.

Heinonen-Tanski, H., Mohaibes, M., Karinen, P., Koivunen, J. 2006. Methods to reduce pathogen microorganisms in manure. Livest. Sci. 102:248-255.

Mawdsley, J.L., Bardgett, R.D., Merry, R.J., Pain, B.F., Theodorou, M.K. 1995. Pathogens in livestock waste, their potential for movement through soil and environmental pollution. Appl. Soil Ecol. 2:1-15.

Stewart, P.S., Costerton, J.W. 2001. Antibiotic resistance of bacteria in biofilms. Lancet 358:135-138.

Storteboom, H.N., Kim, S.-C., Doesken, K.C., Carlson, K.H., Davis, J.G., Pruden, A. 2007. Response of antibiotics and resistance genes to high-intensity and low-intensity manure management. J. Environ. Qual. 36:1695-1703.

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CURRICULUM VITAE

Cristiane San Miguel 1985-1989 A.B. in Art History/Visual Arts Princeton University Princeton, NJ 1991-2002 Admissions Associate Hunter College Campus Schools New York, NY 1993-1996 Non-degree Hunter College, The City University of New York New York, NY 1999-2002 Master’s Degree in Environmental Sciences Rutgers, The State University of New Jersey New Brunswick, NJ 1999, 2001-2004 Teaching Assistant Department of Environmental Sciences Rutgers, The State University of New Jersey New Brunswick, NJ 2005-2010 Graduate Assistant Department of Environmental Sciences Rutgers, The State University of New Jersey New Brunswick, NJ 2006-2010 Managing Editor Soil Science New Brunswick, NJ 2007 San Miguel, C., Dulinski, M., Tate, R.L. Direct Comparison of individual

substrate utilization from a CLPP study: a new analysis for metabolic diversity data. Soil Biol. Biochem. 39:1870-1877.

2011 Ph.D. in Environmental Sciences Rutgers, The State University of New Jersey New Brunswick, NJ

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