Nematode Community Structure and Effects on Peanut Production Systems
by
Kassie N. Conner
A dissertation submitted to the Graduate Faculty of
Auburn University
in partial fulfillment of the
Requirements for the Degree of
Doctor of Philosophy
Auburn, Alabama
August 9, 2010
Keywords: aflatoxins, DGGE, molecular, nematology
Copyright 2010 by Kassie N. Conner
Approved by
Robin N. Huettel, Chair, Professor of Plant Pathology
Covadonga R. Arias, Associate Professor of Fisheries and Allied Aquacultures
Kira L. Bowen, Professor of Plant Pathology
Austin Hagan, Extension Specialist of Plant Pathology
ii
Abstract
Understanding the total nematode community in agronomic systems and its impact on
crop health may provide insight into more sustainable management strategies. In this study the
focus was on management of the peanut root-knot nematode, Meloidogyne arenaria race 1 and
the aflatoxigenic fungi Aspergillus flavus and A. parasiticus, a toxic food contaminate that poses
a threat to humans and animals, to increase peanut yields while lowering toxins. The overall
approach of management is to suppress plant-parasitic nematodes that facilitate invasion of the
toxin producing fungi through manipulation of free-living nematode populations that act to
increase plant health. The objectives of this research were 1) evaluate nematode consensus
primers and Denaturing Gradient Gel Electrophoresis (DGGE) techniques for effectiveness in
identification of nematode populations and monitoring community shifts; 2) develop nematode
genetic profiles of selected soil samples, using DGGE fingerprinting, from different rotation
sequences: continuous peanut, continuous bahiagrass, peanut/cotton, and peanut/corn, to
determine if any factors exist that result in nematode population shifts; and 3) identify individual
populations in the nematode community and determine their relationship with peanut yields and
aflatoxin contamination. Nematode populations were established through various methods
including in vitro culturing methods, after which total genomic DNA was extracted from each
species to evaluate the specificity of nematode consensus primers. The primers amplified a wide
trophic range of nematode DNA and fungal DNA, showing that the primers may be universal to
all eukaryotes. DGGE techniques were then evaluated by amplifying a portion of the 18S rDNA
iii
per species collected and subsequently separating the species through denaturing gradient gel
electrophoresis. The DGGE technique successfully separated nematodes at the generic level.
Nematode genetic profiles were created from peanut soils under different cropping sequences
which revealed individual banding patterns, indicating population shifts between rotation
sequences and shifts between sampling periods. Free-living nematodes accounted for the
majority of sequences recovered from profiles, although plant-parasitic, animal-parasitic, and
entomopathogenic nematodes, as well as nematophagus fungi were identified in recovered
sequences. Bahiagrass rotations supported higher population levels of microbivore nematodes
and significantly lower levels of aflatoxins when planted in rotation with peanuts. Negative
correlations occurred between microbivore populations and total aflatoxin levels, suggesting that
free-living nematodes may play a role in the suppression of aflatoxin contamination in peanuts.
iv
Acknowledgements
I would like to express my gratitude to my major advisor, Dr. Robin Huettel, for the
opportunity to conduct this research and for her guidance, not only in my research but also in
life. I am also thankful to Dr. Kira Bowen for her guidance and resources throughout my
research. My sincere gratitude is also due to my other committee members, Dr. Covadonga Arias
and Dr. Austin Hagan for their guidance, suggestions and readiness to help me at all times
throughout my research. I am also grateful to Dr. Fenny Dane for agreeing to be my outside
reader on such short notice.
I am deeply indebted to my husband, John Allen Cannon for his constant support and
understanding during the course of this research. Thanks are also due to my parents, Terry and
Neil Gunter, to my sister, Misty Conner, to my in-laws, Bonnie Gallaher, Ron Toifel, and Terry
and Betty Cannon for their moral support. I am equally thankful to my previous lab member,
Hari Kishan Sudini for his help.
v
Table of Contents
Abstract???????????????????????????????? ??? ii
Acknowledgments??????????????????????????? ???.. iv
List of Tables??????????????????????????? ?? ???. .vi
List of Figures?????????? ???????????????? ??? ??... viii
Chapter I. Introduction and Literature Review????????... .............................................1
Chapter II. Evaluation of DGGE to Monitor Nematode Populations in Agricultural
Soils???????????? ?????????????????... ............25
Chapter III. DGGE Fingerprinting of Nematode Community Structure under Peanut Rotation
Systems???????.... ..............................................................................................51
Chapter IV. Influence of Nematode Community on Aflatoxin Contamination of
Peanuts????????????????? ??????????? ???. ..74
Summary????????????????????????????? ? ??? ?9 8
Cumulative Bibliography???????????????????? ???? ??? .101
vi
List of Tables
Chapter II. Evaluation of DGGE to Monitor Nematode Populations in Agricultural Soils
Table 1. Trophic group and species list in nematode DNA collection used to test specificity of
nematode consensus primers?????????????? ????????.. ?4 1
Table 2. Putative identification of partial 18S rDNA sequences re-amplified from excised bands
from DGGE profile?????????????????? ?????? ??? 42
Chapter III. DGGE Fingerprinting of Nematode Community Structure under Peanut Rotation
Systems
Table 1. Putative identification of nematode partial 18S rDNA sequences re-amplified from
excised bands recovered from 2008 Denaturing Gradient Gel Electrophoresis profiles of
peanut soil samples under various rotations from the Wiregrass Research and Extension
Center?????????????? ?????????????????. ?6 3
Table 2. Putative identification of nematode partial 18S rDNA sequences re-amplified from
excised bands recovered from 2009 Denaturing Gradient Gel Electrophoresis profiles of
peanut soil samples under various rotations from the Wiregrass Research and Extension
Center?????????????? ? ????????????????.? 64
Chapter IV. Influence of Nematode Community on Aflatoxin Contamination of Peanuts
Table 1. Year-wise cropping pattern in different peanut rotations sampled for this study at
Wiregrass Research and Extension Center???... ?????????? ? ??.? 84
Table 2. Spearman rank correlation coefficients calculated among nematode populations
observed at pre-plant, aflatoxin levels detected in peanuts, yield, and visual peanut
evaluations for pod damage in 2007 under various peanut rotations in Headland,
AL????????????????????????????? ? ???. .85
Table 3. Spearman rank correlation coefficients calculated among nematode populations
observed at mid-season, aflatoxin levels detected in peanuts, yield, and visual peanut
evaluations for pod damage in 2007 under various peanut rotations in Headland,
AL??????? ?????????????????? ? ???????.. 86
vii
Table 4. Spearman rank correlation coefficients calculated among nematode populations
observed at harvest, aflatoxin levels detected in peanuts, yield, and visual peanut
evaluations for pod damage in 2007 under various peanut rotations in Headland,
AL?????????????????????????????? ???.. 87
Table 5. Spearman rank correlation coefficients calculated among nematode populations
observed at pre-plant, yield and visual peanut evaluations for pod damage in 2008 under
various peanut rotations in Headland, AL????????????... ......................88
Table 6. Spearman rank correlation coefficients calculated among nematode populations
observed at mid-season, yield and visual peanut evaluations for pod damage in 2008
under various peanut rotations in Headland, AL?????????? ?????.. .89
Table 7. Spearman rank correlation coefficients calculated among nematode populations
observed at harvest, yield and visual peanut evaluations for pod damage in 2008 under
various peanut rotations in Headland, AL???????????? ?????... ..90
Table 8. Spearman rank correlation coefficients calculated among nematode populations
observed at pre-plant, yield and visual peanut evaluations for pod damage in 2009 under
various peanut rotations in Headland, AL???????????? ?? ???? .91
Table 9. Spearman rank correlation coefficients calculated among nematode populations
observed at mid-season, yield and visual peanut evaluations for pod damage in 2009
under various peanut rotations in Headland, AL?????????? ?????... 92
Table 10. Spearman rank correlation coefficients calculated among nematode populations
observed at harvest, yield and visual peanut evaluations for pod damage in 2009 under
various peanut rotations in Headland, AL???????????? ?????? .93
viii
List of Figures
Chapter II. Evaluation of DGGE to Monitor Nematode Populations in Agricultural Soils
Figure 1. Polymerase Chain Reaction amplified product detection of nematode 18S
rDNA??.. ?????????????????????? ???? ???... .43
Figure 2. Denaturing Gradient Gel Electrophoresis image of nematode and fungal 18S rDNA
amplified products????????????????????? ?????? .44
Figure 3. Nematode Denaturing Gradient Gel Electrophoresis profile obtained from different
peanut cropping sequences at the Wiregrass Research and Extension Center at pre-plant
2008?????????????????????? ??????????... 45
Figure 4. Multi-dimensional Scaling of nematode communities from peanut soil samples under
various crop rotations collected at pre-plant from the Wiregrass Research and Extension
Center in 2008, colored by crop rotation????????? ?????????. .46
Chapter III. DGGE Fingerprinting of Nematode Community Structure under Peanut Rotation
Systems
Figure 1. Denaturing Gradient Gel Electrophoresis profile of nematode communities from peanut
soil samples under various crop rotations collected at pre-plant, mid-season and harvest
from the Wiregrass Research and Extension Center in
2008?????????????????????????????? ??. 65
Figure 2. Denaturing Gradient Gel Electrophoresis profile of nematode communities from peanut
soil samples under various crop rotations collected at pre-plant, mid-season and harvest
from the Wiregrass Research and Extension Center in
2009?????????????????????????????? ??. 66
Figure 3. Multi-dimensional Scaling of nematode communities from peanut soil samples under
various crop rotations collected at pre-plant, mid-season and harvest from the Wiregrass
Research and Extension Center in 2008?????????? ????????. 67
Figure 4. Multi-dimensional Scaling of nematode communities from peanut soil samples under
various crop rotations collected at pre-plant, mid-season and harvest from the Wiregrass
Research and Extension Center in 2009?????????? ???????? 68
ix
Figure 5. Dendrogram construction using the unweighted pair-group method with arithmetic
mean (UPGMA) based on nematode community band table data from different peanut
cropping sequences collected at pre-plant, mid-season and harvest from the Wiregrass
Research and Extension Center in 2008. The scale represents % of similarity calculated
by the Dice coefficient.?????? ?... .......................................................................69
Figure 6. Dendrogram construction using the unweighted pair-group method with arithmetic
mean (UPGMA) based on nematode community band table data from different peanut
cropping sequences collected at pre-plant, mid-season and harvest from the Wiregrass
Research and Extension Center in 2009. The scale represents % of similarity calculated
by the Dice coefficient.??????? ??????????????????. 70
Chapter IV. Influence of Nematode Community on Aflatoxin Contamination of Peanuts
Figure 1. Mean microbivore nematode counts observed under various peanut cropping rotations
from the Wiregrass Research and Extension Center sampled at: a) Pre-plant 2007, b)
Harvest 2008, c) Mid-season 2009???? ??????? ????????... ....94
Figure 2. Mean total plant-parasitic nematode counts observed under various peanut cropping
rotations from the Wiregrass Research and Extension Center sampled at: a) Pre-plant
2008, b) Pre-plant 2009, c) Mid-season 2009?????? ?.. ?? ??????... ..95
1
Chapter I. Introduction and Literature Review
There is a complex biotic structure within the soil that affects plant health. Some specific
communities of soil organisms can lead to suppression of detrimental soil-borne bacterial, fungal
and nematode populations, with subsequent alleviation of plant disease (Cook and Baker, 1983;
Dickson et al., 1994). These organisms include bacteria, fungi and non-plant parasitic nematodes
called free-living nematodes (Neher, 2001).
Free-living nematodes consist of bacterial-feeders, fungal-feeders and predatory
nematodes. These free-living nematodes have direct and indirect effects on soil nutrition that can
affect other organisms (Neher, 2001). They can affect the growth of plants and the metabolic
activities of other soil microbes by regulating rates of decomposition and nutrient mineralization
(Ingham et al., 1985). Free-living nematodes are commonly attributed to increased plant growth,
increased nitrogen (N) uptake by plants, decreased or increased bacterial populations, increased
CO2 evolution, increased N and phosphorous (P) mineralization, and increased substrate
utilization (Ingham et al., 1985).
Understanding soil suppression of plant diseases requires understanding the soil
microbial community composition, including interactions between the populations. Traditional
techniques employed to describe the composition and diversity of the nematode community
relies on phenotypic characteristics. Traditional morphological identification by light microscopy
is time-consuming and requires extensive training.Molecular analytical tools can overcome these
limitations by directly exploring the composition present in a soil sample. One method is the
utilization of a set of molecular analytical tools to generate population specific fingerprints by
2
displaying the ribosomal polymorphisms naturally present in microbial communities. Among
these DNA fingerprinting methods, Denatured Gradient Gel Electrophoresis (DGGE) is a staple
in environmental microbiology for studying microbial population structure and dynamics.
Previous studies have used DGGE to determine nematode species biodiversity by
comparing molecular fingerprints (Foucher and Wilson, 2002; Foucher et al., 2004). Other
studies have designed and evaluated nematode primers for DGGE analysis of soil community
DNA (Waite et al., 2003). These previous studies have assessed nematode biodiversity without
making an analysis of the populations present.
Peanuts are an important crop in the southeastern United States. They can be
detrimentally affected by a number of soil-borne organisms, including plant parasitic nematodes
and the ubiquitous Aspergillus flavus fungal group. Aflatoxins, produced by the A. flavus group,
are highly carcinogenic, are strictly regulated to ensure a safe food supply, and can decrease the
economic return from a peanut crop (Dorner et al., 2003). There is no highly effective control for
aflatoxigenic fungi and aflatoxins, but minimization of this problem may be possible through a
greater understanding of the microbial community that influences A. flavus production of
aflatoxins including the nematode community.
The objective of this study was to determine the potential of DGGE to monitor
populations within the nematode community and then apply this analysis to a peanut rotation
system to determine if the nematode community affects plant health and yield quality. Evaluating
the use of DGGE in identifying nematode populations was accomplished by choosing primers
and testing their specificity to determine how robust the analysis is on a trophic level. This was
followed by an evaluation of DGGE efficiency to determine denaturant characteristics of DNA
for common species. The technique was then used to create genetic fingerprints of nematode
3
communities from peanut fields under various crop rotations in order to determine if free-living
nematodes contribute to the health of the peanut crop by decreasing aflatoxin contamination or
increasing yield.
Suppression of Soil-borne Diseases
Agricultural pests, such as microbial pathogens, insects and weeds, infest crops, causing
significant losses in plant yield or quality. Disease suppression is usually achieved through
cultural management practices, including crop rotations, resistant varieties, soil amendments and
solarization. Beyond cultural management practices, growers usually depend on chemicals
including herbicides, insecticides, fungicides and nematicides (Rosas, 2007).
The overuse of chemical pesticides to prevent or decrease pest populations has caused
soil pollution and environmental contamination. Biological control offers answers to the many
serious problems of modern agriculture and it is an essential component in the development of
sustainable agriculture (Rosa, 2007). Baker and Cook (1974) defined biological control as ?the
reduction of inoculum density or disease-producing activities of a pathogen or parasite in its
active or dormant state, by one or more organisms, accomplished naturally or through
manipulation of the environment, host, or antagonist, or by mass introduction of one or more
antagonists.? Biological control answers many agricultural problems such as the need to increase
crop production within existing resources, avoiding development of pathogen resistance to
chemicals, maintaining pollution- and risk-free control, and adopting practices compatible to
sustainable agriculture (Cook and Baker, 1983).
Disease suppressive soils are one type of biological control, and are defined as soils
where the pathogen does not establish or persist, establishes but causes low levels of damage or
4
no damage, or establishes and causes disease for a certain period of time until disease levels
begin to lower and become insignificant (Baker and Cook, 1974). Suppressive soils are known
for many plant pathogens and diseases including: Gaeumannomyces graminis var. tritici
(Raaijmakers and Weller, 1998), Peach Tree Short Life (Kluepfel et al., 2002), root-knot
nematodes (Dickson et al., 1994), and cyst nematodes (Kerry et al., 1982; Meyer et al., 1990;
Carris et al., 1989; Yin et al., 2003).
Disease suppressive soils may be characterized as providing either general or specific
suppression. General suppression is directly related to the total amount of microbial activity at a
time critical to the pathogen (e.g. propagule germination and penetration). The type of
microorganism present during this period is less important than the total active microbial
biomass, which will compete with the pathogen for resources. Specific suppression is an effect of
an individual or select group of microorganisms antagonistic to the pathogen during a stage in its
life cycle (Cook and Baker, 1983).
Take-all decline (TAD) is a classic example of a specific suppressiveness. TAD is the
natural biological control of take-all, caused by the fungus Gaeumannomyces graminis var.
tritici. TAD is defined as the spontaneous reduction in disease and increase in yield with
extended monoculture of wheat or barley (Slope and Cox, 1964). This phenomenon was first
described in the 1930?s (Glynne, 1935), and within 50 years it was recognized worldwide
(Hornby, 1983). In 1976, Cook and Rovira suggested that TAD was based on microbiological
interactions between the take-all pathogen and specific root-associated microorganisms. In 1998,
Raaijmaker and Weller demonstrated that root-associated fluorescent Pseudomonas spp.
producing the antibiotic 2,4-diacetylphoroglucinol (Phl) are the key components of the natural
biological control that operates in TAD soils. This was demonstrated by showing that
5
suppression of take-all was lost when Phl-producing fluorescent Pseudomonas spp. were
eliminated, and conducive soils gained suppressiveness to take-all when Phl-producing
Pseudomonas spp. were introduced to the soil.
Fluorescent Pseudomonas species have also been associated with the suppression of other
plant diseases including Peach Tree Short Life (PTSL) (Kluepfel et al., 2002). PTSL is a
syndrome that results in premature mortality of peach trees in the southeast United States. One
major factor in PTSL is the migratory ectoparasitic nematode Mesocriconema xenoplax (the ring
nematode). Kluepfel et al. (2002) isolated Pseudomonas sp. BG33R and demonstrated its ability
to inhibit M. xenoplax multiplication in vivo and egg hatch in vitro. They also cloned and
sequenced five genes from BG33R that were involved in production of the egg-kill factor. It was
suggested that salicylic acid and a fluorescent siderophore plays a role in egg-kill. This study
showed that shifting the soil microbial community toward Pseudomonas sp. BG33R, through soil
solarization and microbial inoculation, inhibited ring nematode reproduction and suppressed
PTSL.
Plant parasitic nematodes can also be suppressed by soil-borne organisms. Of these
nematode antagonists, Pasteuria spp. have the greatest potential for biological control of plant
parasitic nematodes (Dickson et al., 1994). Pasteuria spp. are Gram-positive, endospore-forming
bacteria that are obligate parasites of several plant parasitic nematodes. Three nematode parasitic
species have been characterized: P. thornei, a parasite of the root lesion nematode Pratylenchus
spp., P. nishizawae, a parasite of cyst nematodes Heterodera spp. and Globodera spp., and P.
penetrans, a parasite of the root-knot nematodes Meloidogyne spp. (Sayre and Starr, 1989).
Pasteuria spp. produce nonmotile endospores that are resistant to desiccation. The
endospores readily attach to the cuticle of host nematodes on contact in soil or water. In root-
6
knot nematodes this usually occurs during the second-stage juvenile (J2). After attachment the
endospore germinates, producing a germ tube that penetrates the nematode?s cuticle. Inside the
nematode?s body, the germ tube develops into a vegetative colony. Sporangia develop; giving
rise to more endospores that will eventually fill the nematode?s body. Parasitized nematodes
usually reach the adult stage, but fecundity is reduced or blocked. Disintegration of the
parasitized nematode?s body occurs, during which the endospores are released back into the soil
(Dickson et al., 1994).
The potential of Pasteuria species as a biological control agent has mainly focused on P.
penetrans (Tzortzakakis et al., 1997). In 1997, Chen et al. showed that peanut fields heavily
infested with Meloidogyne arenaria, race 1, had yield increases and reductions of population
densities of nematodes with continuous planting to peanut when inoculated with P. penetrans.
One of the classical studies on suppressive soils with fungal antagonists of nematodes
was an investigation of cereal monoculture sites at the Rothamstead Experimental Station in
Great Britain. This study showed a continuous decrease in the population levels of the cereal cyst
nematode, Heterodera avenae, after a short population peak. Kerry et al. (1982) reported the
fungi Nematophthora gynophila and Verticillium chlamydosporium as parasites of the cereal cyst
nematode eggs and cysts responsible for the specific soil suppression.
Carris et al. (1989) compared fungal isolates from two soybean fields: one with high
levels of the soybean cyst nematode, Heterodera glycines, and the second with suppressed H.
glycines populations despite years of continuous cropping with susceptible soybean cultivars.
They showed that Fusarium oxysporum and Paraphoma radicina were predominant in the field
with the suppressed nematode population.
7
Meyer et al. (1990) found that a complex of soil-borne fungi could suppress egg hatch
and juvenile mobility of the soybean cyst nematode, H. glycines, under laboratory conditions.
This bioassay was conducted on eggs from nematodes that had been grown monoxenically on
excised root tips. They showed that a combination of Phoma chrysanthemicola, one strain of
Verticillium chlamydosporium, and one strain of V. lecanii decreased the number of viable eggs.
Yin et al. (2003) attempted to identify fungi associated with Heterodera schachtii, the
sugar beet cyst nematode, obtained from soils possessing various levels of suppressiveness in
California. The fungi were identified through an rDNA analysis termed oligonucleotide
fingerprinting of ribosomal RNA genes (OFRG). Cysts obtained from the suppressive soil
predominantly contained fungal rDNA with high sequence identity to Dactylella oviparasitica.
Identification of the biological properties contributing to the function of suppressive soils
is necessary to manage such systems for use in the control of soilborne diseases. The
development and application of molecular methods for monitoring soil microbial properties will
enable a more rapid and detailed assessment of the biological nature of suppressive soils
(Mazzola, 2004).
Dynamics of the Nematode Community
Free-living nematodes have direct and indirect effects on soil nutrition that can affect
other soil organisms (Neher, 2001). Free-living nematodes are commonly attributed to increased
plant growth, increased N uptake by plants, decreased or increased bacterial populations,
increased CO2 evolution, increased N and phosphorous (P) mineralization, and increased
substrate utilization (Ingham et al., 1985).
8
Free-living nematodes indirectly affect the growth of plants and the metabolic activities
of other soil microbes by regulating rates of decomposition and nutrient mineralization. Bacteria
can act as a nutrient sink in soils, immobilizing nutrients from organic compounds (Ingham et al.,
1985). Several studies have shown that microbial grazers, such as bacterial-feeding nematodes,
can mineralize some of these immobilized nutrients, including N and to some extent P (Cole et
al., 1978; Gould et al., 1981; Woods et al., 1982).
Nematodes contribute to nitrogen mineralization specifically by grazing on decomposer
microbes and excreting ammonium (Ingham et al., 1985), which is the main excretory product of
nematodes (Wright and Newall, 1976). De Ruiter et al. (1993) showed that bacterial-feeding and
predatory nematodes contribute 13% and 9% of nitrogen mineralization, respectively, in
conventional management practices.
Ingham et al. (1985) developed a conceptual model in which microfloral grazers were
considered separate variables and evaluated the effects of bacterial-feeding nematodes on
microbial growth, nutrient cycling, plant growth, and nutrient uptake. They showed that plants
grow faster in the presence of microbial grazing nematodes than in their absence, and that the
growth response was caused by the increase in nitrogen mineralization from the nematodes.
Understanding the impact of the nematode community on plant health requires
identifation of the populations present within the community, and identifying interactions
between the populations. Traditional techniques employed to describe the composition and
diversity of nematode populations in the soil relies on phenotypic characteristics, which are
evolutionarily highly conserved. Such a technique provides an incomplete assessment of
diversity, is time-consuming and requires extensive training. In addition, identification of
nematodes at the species level can be problematic in many cases. Most species can only be
9
identified from adult male- or female-specific structures. Van Der Knaap et al. (1993) noted that
Caenorhabditis elegans and C. briggsae can only be differentiated by males (based on the
arrangement of bursal rays at the tail) which can form less than 0.1% of the population.
Researchers usually classify nematodes into trophic groups instead of identifying each
species for community analyses. According to Bernard (1992) soil inhabiting nematodes can be
separated into five trophic groups: microbivores (bacterial-feeders), fungivores (fungal-feeders),
plant-parasites, predators, and omnivores. The problem with using trophic groups when
analyzing functionality in nematode communities is that these categories are not mutually
exclusive. Species placed in one category may have developmental stages that fit another
category (Bernard, 1992). For example, juvenile stages of some species of the predacious orders
Mononchida and Diplogasterida may feed on bacteria in their initial juvenile stages (Yeates,
1987). Yeates also reported maintaining cultures of the predacious nematode Mononchus
propapillatus on bacteria for over eight months.
The total number of nematode species described from a single site can also complicate
identification. Hodda and Wanless (1994) identified 154 nematode species from an English
Chalk Grassland, 44 of which could not be assigned positively to previously described species.
Beier and Traunspurger (2003) identified 113 species from a coarse-grained sub-mountain
carbonate stream in southwest Germany. Baird and Bernard (1984) reported 100 species in two
wheat-soybean fields in Tennessee. Orr and Dickerson (1966) found 228 nematode species,
representing 80 genera, in 61 soil samples taken from a prairie pasture in Kansas. This is the
maximum number of nematode species described from a single soil site (Boag and Yeates,
1998). Furthermore, terrestrial nematodes can easily exceed one million individuals per square
meter of soil (Floyd et al., 2002).
10
It has been recognized that there is a severe shortage of taxonomically oriented
nematologists, especially for free-living nematodes (Bernard, 1992; Coomans, 2002). Nematodes
are mainly studied with the compound light microscope and the observations are usually made
based on numerous fixed specimens, which can take a considerable amount of time to prepare.
The limitations to nematode community analysis may be overcome by using molecular analytical
tools to directly explore the composition in a soil sample based on the nucleic acids present.
Molecular Analytical Techniques
Soil microbial communities affect crop health and in turn affect yields. Monitoring these
communities has become important in sustainable agriculture. The soil microbial community
may be altered to increase beneficial organisms by manipulating cropping conditions. A reliable,
reproducible and sensitive method to profile these populations is needed. Molecular analytical
tools have recently been applied to characterize the biology resident to soil ecosystems and have
provided new insight into the diversity of microbial species found in soil habitats (Mazzola,
2004). These molecular analytical methods can directly explore the microbial composition of a
sample based on the nucleic acids present within that sample.
Various molecular techniques have been used in nematology for diagnostics, estimation
of genetic diversity of populations and inference of phylogenetic relationships between taxa
(Subbotin and Moens, 2006). These techniques include protein electrophoresis (Esbenshade and
Triantaphhllou, 1985), polymerase chain reaction (PCR) (McCuiston et al., 2007), restriction
fragment length polymorphism (RFLP) (Curran et al., 1986), multiplex PCR (Skantar et al.,
2007), random amplified polymorphic DNA (RAPD) (Caswell-Chen et al., 1992), amplified
fragment length polymorphism (AFLP) (Folkertsma et al., 1996), sequencing of DNA (Bae et al.,
11
2008), DNA bar-coding (Floyd et al., 2002), and real-time PCR (Madani et al., 2005). Molecular
nematology, although, has only recently been applied in an ecological context.
In 1993, Van Der Knaap et al. used an arbitrarily primed polymerase chain reaction (ap-
PCR) technique to differentiate closely related bacterial-feeding nematode genera
(Caenorhabditis, Acrobeloides, Cephalobus, and Zeldia), which are difficult to separate into
species. The technique was used to assess biodiversity and required PCR amplification of
individual nematodes with at least three different primer sets. However, the technique could not
identify the nematodes without considerable calibration.
Vrain et al. (1992) separated populations of the Xiphinema americanum group, a plant
parasitic nematode vector of nepoviruses, based on their capability to vector viruses using
restriction fragment length polymorphism (RFLP). This was accomplished using the restriction
fragment length difference in the 5.8S gene and the internal transcribed spacer (ITS) of
ribosomal DNA.
In 2002, Floyd et al. developed a molecular operational taxonomic unit (MOTU) method
using a molecular barcode derived from single-specimen polymerase chain reaction (PCR) and
sequencing of the 5? segment of the small subunit ribosomal RNA (SSU) gene for soil
nematodes. The results indicated that this technique allowed a rapid assessment of nematode
diversity in soils. This method requires sequencing PCR amplified products from individual
nematodes.
Eyualem and Blaxter (2003) used Floyd?s molecular barcode system to identify free-
living nematode species. They attempted to differentiate five cultured isolates of the
taxonomically difficult genus, Panagrolaimus. Their results showed that the five populations
belonged to two different species.
12
Qiu et al. (2006) developed a simple PCR assay protocol for detection of the root-knot
nematode species Meloidogyne arenaria, M. incognita and M. javanica extracted from soil. The
PCR assay was carried out with primers specific for this group of nematodes they developed and
with universal primers spanning the ITS region of rRNA genes (Vrain et al., 1992). This analysis
can detect the presence of second stage juveniles from large numbers of other plant-parasitic and
free-living nematodes.
Griffiths et al. (2005) combined morphology and molecular sequencing to establish the
potential for analyzing nematode communities by molecular biological characterization. From
their study they concluded that DNA from certain groups of nematodes was under-represented
by this analysis. This was attributed to either a mismatch in sequence at the primer site or PCR
inhibition from the secondary structure of the template DNA or co-extracted compounds.
Among the molecular analytical techniques available, molecular fingerprinting methods
can help monitor changes in microbial communities over time with a simplified representation of
the community. These methods generate population specific fingerprints that display the
ribosomal polymorphism naturally present in the community at a given time. Among these
fingerprinting methods, denatured gradient gel electrophoresis (DGGE) has been successfully
applied to study microbial communities from different sources including agricultural soils.
DGGE is used in microbial ecology to investigate population diversity and community
dynamics in response to environmental variations. Recent applications study microbial
communities within soil, rivers, seas, lake water, gastrointestinal tracts of animals, wastewater
treatment bioreactors, insects, clinical samples, and food (Ercolini, 2004).
DGGE separates PCR products based on sequence differences that result in differential
denaturing characteristics of the DNA. PCR products encounter increasingly higher
13
concentrations of chemical denaturants (formamide and urea) as they migrate through a
polyacrylamide gel. Upon reaching a threshold denaturant concentration, the weaker melting
domains of the double-stranded PCR product will begin to denature at which time migration
slows dramatically. Differing sequences of DNA (from different organisms) will denature at
different denaturant concentrations, depending on the % GC composition of the sequence,
resulting in a pattern of bands. Each band theoretically represents a different sequence present in
the community. Fingerprints can be uploaded into an analytical software database in which
similarity can be assessed to determine microbial structural differences between environments or
treatments (Muyzer et al., 1993).
In a previous study by Foucher and Wilson (2002), DGGE was used to distinguish
nematode species from a mixed laboratory culture. This study suggested that DGGE could be
used to measure nematode diversity within the soil. In 2003, Waite et al. used DGGE to analyze
nematode communities from total genomic DNA extracted from the soil. They showed that the
nematode community fingerprint differed between different sites. In 2004, Foucher et al. also
used DGGE to assess nematode biodiversity by comparing nematode community fingerprints.
Previous studies using DGGE to assess nematode biodiversity have not made any
analysis of the taxonomic populations present or their abundance from the molecular data. One
unique characteristic of DGGE is that DNA from each organism can be retrieved, once
molecular fingerprints have been analyzed, by excising individual bands from the gel. After the
DNA has been retrieved it can be reamplified and sequenced. The sequences can be uploaded
into a database and compared to know sequences to identify the populations present in the
community.
14
In this study, DGGE analysis was applied to a peanut rotation cropping system.
Nematode populations from continuous peanut, peanut/cotton, peanut/corn, and continuous
bahiagrass rotations were assessed in order to determine the affect of the total nematode
community on the health of the plant based on aflatoxin contamination and yield.
Peanuts and Aflatoxin Contamination
Peanuts are an important crop in the southeastern United States. They can be
detrimentally affected by a number of soil-borne organisms, including plant parasitic nematodes
and the Aspergillus flavus fungal group. Aflatoxins, produced by the A. flavus fungal group, are
highly carcinogenic, are strictly regulated to ensure a safe food supply, and can decrease the
economic return from a peanut crop (Dorner et al., 2003). Contamination of aflatoxins in peanut
seeds results in a loss of $2.6 million per year to peanut growers (Lamb and Sternitzke, 2001).
There is no highly effective control for aflatoxigenic fungi and aflatoxins, but minimization of
this problem may be possible through a greater understanding of the microbial community that
influences A. flavus production of aflatoxins.
Aflatoxins are polycyclic, unsaturated highly substituted coumarins. Approximately 20
aflatoxins have been identified but only four of them occur naturally: B1, B2, G1, and G2.
Aflatoxin B1 is the most potent. There is no threshold dose below which no tumor formation will
occur when consumed by animals, and only a zero level of exposure will result in no risk.
Besides their liver carcinogenic effect, aflatoxins are also mutagenic, teratogenic, and
hepatogenic. When consumed at low doses they can also be responsible for weight loss, loss of
reproductive capacity, and imparity of immune systems. Unprocessed foods of plant origin are
the most important source of aflatoxins in the diet (Weidenborner, 2001).
15
Aflatoxins are produced in peanut pod tissues by A. flavus and A. parasiticus, which are
commonly referred to as the A. flavus fungal group. The toxins are produced when
environmental conditions are hot and dry, three to six weeks prior to peanut maturity. Damaged
and immature pods are more susceptible to infection by aflatoxin producing fungi than healthy
pods (Hill et al., 1983). Damage to pods is in part due to plant parasitic nematodes. Wounds
caused by nematode feeding are generally superficial, although damage may create conditions
favorable for invasion of A. flavus. The root-knot nematode, Meloidogyne arenaria, race 1, and
the ring nematode, Mesocriconema xenoplax, have been shown to increase aflatoxin
contamination of peanut seeds (Timper et al., 2004; Bowen et al., 2003).
There are no reliable methods for control of aflatoxin contamination in peanuts. Irrigation
has been shown to reduce A. flavus colonization of peanuts, especially in the last 40 to 75 days of
growth, but this is not feasible for most growers (Wilson and Stansell, 1983). Biological control
of aflatoxin contamination was demonstrated using atoxigenic strains of A. flavus and A.
parasiticus. These strains competitively exclude toxigenic strains in the soil and reduce aflatoxin
concentrations. However, these strains can be human allergens (Dorner et al., 1992). Another
biological control strategy includes introducing or enhancing bacteria to reduce or eliminate
colonization of the fungus through competition, although this strategy has not been optimized yet
(Mickler et al., 1995).
Mechanisms by which plant parasitic nematodes increase aflatoxins are unknown. Galls
on peanut pods produced by root-knot nematodes may increase kernel colonization by A. flavus
fungi by serving as entry points for the fungus or by preventing kernel development. Nematodes
could also contribute to aflatoxin production because their damage impairs root function,
predisposing the plant to drought stress. Plant parasitic nematode infection of roots also causes
16
physiological changes in the plant that increase the susceptibility of kernels to infection by the
fungus (Timper et al., 2004).
Nematode populations are usually controlled through crop rotations with a non-host crop.
Bowen et al. (1996) showed that root-knot nematode densities in peanut production fields were
lower following two years of cropping corn, cotton or other non-leguminous crops than when
peanut was planted in alternating years. These observations suggest that root-knot nematodes
limit yields in continuously cropped peanuts. Bahiagrass in rotation with peanuts has also been
shown to reduce number of root-knot nematode juveniles and increase yields 36% higher than
monocultured peanuts, if planted following two years of bahiagrass (Rodriguez-Kabana et al.,
1991).
Dynamics within the nematode community, including the role various free-living
nematodes play, may contribute to general or specific suppression of certain soil-borne diseases.
Understanding the resident nematode community composition as well as the functional
interactions between members present in the population may be the key to understanding how
free-living nematodes affect soil-borne disease complexes.
Monitoring these free-living and plant parasitic nematode profiles using molecular
fingerprinting methods such as DGGE under different crop rotations and then correlating these
results to aflatoxin contamination and yield may shed light on potential interaction that act to
suppress nematode/fungal damage to peanuts. An understanding of these interactions under field
conditions could indicate possible management schemes to change soil microbial profiles by
shifting nematode communities toward beneficial populations and thus ultimately reduce
nematode/aflatoxin contamination and increase yields.
17
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25
Chapter II. Evaluation of DGGE to Monitor Nematode Populations in Agricultural Soils
Abstract
Denaturing Gradient Gel Electrophoresis (DGGE) can be used to monitor communities of
microorganisms by generating population specific fingerprints that display the ribosomal
polymorphism naturally present in the community. The objective of this study was to evaluate
the potential use of DGGE to monitor nematode populations in agricultural soils. This was
accomplished by testing the specificity of nematode consensus primers, determining the
efficiency of DGGE to separate a wide range of nematode DNA and then applying the technique
to analyze soil samples from a peanut rotation system. The nematode consensus primers
amplified a wide trophic range of nematode DNA and fungal DNA, showing that the primers
may be universal to all eukaryotes. DGGE separated most nematode species at separate
denaturant concentrations except Meloidogyne arenaria and M. incognita. Rhabditis sp. and
Tylenchorhynchus sp. samples each yielded two bands which were matched to separate genera
and separate species, respectively. The DGGE profile indicated similarities among community
profiles of replicated plot samples from continuous bahiagrass rotations and continuous peanut
rotation with 73% and 55% similarity, respectively. A total of 37 band classes were observed
between all plots, 15 of which were excised, re-amplified, sequenced, and matched to closely
related sequences held in GenBank?s database. These results show that DGGE can be
successfully applied to analyze populations within the nematode community and to monitor
shifts in the populations due to cropping rotations.
26
Introduction
The nematode community represents a complex structure within the soil that affects plant
health. This community consists of plant-parasitic nematodes and free-living nematodes. Free-
living nematodes include microbivores (bacterial-feeders), fungivores (fungal-feeders) and
predatory nematodes. These free-living nematodes have direct and indirect effects on soil
nutrition that can affect other soil organisms (Neher, 2001). Free-living nematodes are
commonly attributed to increased plant growth, increased nitrogen (N) uptake by plants,
decreased or increased bacterial populations, increased CO2 evolution, increased N and
phosphorous (P) mineralization, and increased substrate utilization (Ingham et al., 1985).
Free-living nematodes indirectly affect the growth of plants and the metabolic activities
of other soil microbes by regulating rates of decomposition and nutrient mineralization. Bacteria
can act as a nutrient sink in soils, immobilizing nutrients from organic compounds (Ingham et al.,
1985). Several studies have shown that microbial grazers, such as bacterial-feeding nematodes,
can mineralize some of these immobilized nutrients, including N and to some extent P (Cole et
al., 1978; Gould et al., 1981; Woods et al., 1982).
Nematodes contribute to nitrogen mineralization specifically by grazing on decomposer
microbes and excreting ammonium (Ingham et al., 1985), which is the main excretory product of
nematodes (Wright and Newall, 1976). De Ruiter et al. (1993) showed that bacterial-feeding and
predatory nematodes contribute 13% and 9% of nitrogen mineralization, respectively, in
conventional management practices.
Understanding the impact the nematode community has on plant health requires
identifying populations present within the community and interactions between those
populations. Traditional techniques employed to describe the composition and diversity of
27
nematode populations in the soil relies on phenotypic characteristics, which are time-consuming
and require extensive training. In addition, identification of nematodes to the species level can be
problematic in many cases. Most species can only be identified from adult male- or female-
specific structures. Limitations to nematode community analysis may be overcome by using
molecular analytical tools to directly explore the composition in a soil sample based on the
nucleic acids present.
Among the molecular analytical techniques available, molecular fingerprinting methods
can help monitor changes in microbial communities over time with a simplified representation of
the community. These methods generate population specific fingerprints that display the
ribosomal polymorphism naturally present in the community at a given time. Among these
fingerprinting methods, denaturing gradient gel electrophoresis (DGGE) has been successfully
applied to study microbial communities from different sources including agricultural soils
(Ampe et al., 2001; Avrahami et al., 2003).
DGGE separates amplified products based on sequence differences that result in
differential denaturing characteristics of the DNA. Amplified products, typically generated
through Polymerase Chain Reaction (PCR), encounter increasingly higher concentrations of
chemical denaturants (formamide and urea) as they migrate through a polyacrylamide gel. Upon
reaching a threshold denaturant concentration, the double-stranded PCR products with lower
melting temperatures will begin to denature at which time migration stops. Differing sequences
of DNA (from different organisms) will denature at different denaturant concentrations,
depending on the % GC composition of the sequence, resulting in a pattern of bands (Muyzer et
al., 1993). Each band theoretically representing a different organism present in the community.
28
Fingerprints can be uploaded into a database and similarity can be assessed to determine
microbial structural differences between environments or treatments.
Foucher and Wilson (2002) developed a PCR-DGGE technique to distinguish nematode
species from a mixed laboratory culture. They were able to separate PCR fragments from all
species tested except those with similar melting behaviors. Waite et al. (2003) designed and
evaluated nematode consensus primers for PCR amplification of soil community DNA. Foucher
et al. (2004) used PCR-DGGE to estimate nematode species richness from grassland soil
samples. Their analysis revealed a relationship between species richness and DGGE estimates
for species that represented more than 1% of the population although they did not make any
analysis of the populations present.
The objective of this study was to evaluate the potential use of DGGE to monitor
nematode populations in agricultural soils by analyzing the populations present within the
nematode community. This was accomplished by testing the specificity of nematode consensus
primers on a wide trophic range of nematode DNA. The primers were then used to determine the
efficiency of DGGE techniques to separate individual nematode species. Finally, DGGE was
used to analyze soil samples from a peanut rotation system to determine if the technique can be
used to identify nematode populations present and monitor shifts in the populations based on
rotation sequence.
Materials and Methods
Primers: Nematode consensus primers designed to amplify a ~630 bp fragment of the 18S
rDNA were used in this experiment: nem1 - forward (5?-GCAAGTCTGGTGCCAGCAGC-3?)
and nem2 - reverse (5?-CCGTGTTGAGTCAAATTAAG-3?) (Foucher and Wilson, 2002). The
29
18S rDNA is a highly conserved gene with somewhat variable regions making it a suitable target
for consensus primers. The forward primer contained a 39 bp GC clamp at the 5? end to prevent
complete denaturation of the amplified product during electrophoresis (Myers et al., 1985).
DNA collection: Soil naturally infested with Meloidogyne arenaria (Neil, 1889) Chitwood, 1949
was collected from peanut fields at the Wiregrass Research and Extension Center (WREC) in
Headland, Alabama. Tomato seeds (Solanum lycopersicum cv Rutgers) were planted in the
nematode infested soil in polystyrene cups to allow nematode colonization of the roots in the
Plant Sciences Research Center (PSRC) in Auburn, Alabama. The roots were removed and
nematode eggs were extracted using the sodium hypochlorite method (Hussey and Barker, 1973)
after 45 days. Eggs were quantified and standardized using a Nikon-T 100 inverted microscope.
Approximately 5,000 eggs were used to inoculate three week old tomato plants in a 3:1 ratio of
autoclaved field soil and autoclaved sand. This was repeated every 3 generations to maintain
populations.
Meloidogyne incognita (Kofoid and White, 1919) Chitwood, 1949 infested soil was
collected from tomato plots at the E.V. Smith Research Center (EVSRC) in Tallassee, Alabama.
The populations were purified and maintained in the same manner as for M. arenaria.
Populations of M. arenaria and M. incognita were identified to race level using the North
Carolina Differential Host Test (Hartman and Sasser, 1985) and protein electrophoresis analysis
using esterase and malate dehydrogenase enzymes (Esbenshade and Triantaphyllou, 1990). The
M. arenaria population was identified as race 1 (peanut root-knot nematode) and the M.
incognita population was identified as race 3 (southern root-knot nematode).
30
Rotylenchulus reniformis (Linford and Oliveira, 1940) populations were collected from
infested cotton soils at EVSRC in Tallassee, Alabama. These populations were maintained on
cotton (Gossypium hirsutum cv Stanville 5599) as described for M. arenaria.
Meloidogyne arenaria, M. incognita and R. reniformis were maintained in monoxenic
cultures on tomato root-explants according to Huettel (1990) with slight modification. Tomato
seeds (Solanum lycopersicum cv Rutgers) were disinfected in 95% EtOH for 3 min. The EtOH
was poured off and replaced with 10% Clorox solution for 10 min. The seeds were transferred
directly to 1% water agar plates and incubated at 27?C in the dark for 3-4 days. Healthy, straight
root tips about 2-3 cm in length were excised by cutting with a sterile dissecting blade, under a
laminar flow hood. The root tips were transferred to Gamborg?s B-5 media (Research Products
International Corp., Mt. Prospect, IL) (3 root tips/plate) and incubated at 27?C for 3 days or until
root tips began to grow. Meloidogyne arenaria and M. incognita egg masses were collected from
tomato roots maintained in the PSRC. Egg masses were hand-picked and placed in sterile
microcentrifuge tubes. The eggs masses were washed with sterile water and pelletized by
centrifugation at 2,000 rpm for 2 min. The supernatant was decanted and 1% streptomycin
sulfate was added. The egg masses sat for 10 min at room temperature then centrifugation was
repeated. The streptomycin sulfate was removed and the egg masses were washed in sterile
water. After the water was decanted, 0.001% mercuric chloride was added and the samples were
immediately centrifuged and decanted. The egg masses were washed two more times with sterile
water. Gamborg?s B-5 plates containing tomato root-explants were inoculated with 3 egg masses
each. Populations were maintained by transferring egg masses to new tomato root-explants every
3 generations.
31
Rotylenchulus reniformis eggs were collected from cotton plants maintained in the PSRC.
Eggs were collected using the sodium hypochlorite method and disinfected as earlier described.
Tomato root-explants, established on Gamborg?s B-5 media, were inoculated with 50 eggs/plate.
Roots were transferred every 3 generations to maintain populations.
Soil samples collected from peanut plots at the WREC in Headland, Alabama were
subjected to a sieving process followed by sugar flotation to extract nematodes (Jenkins, 1964).
Aliquots of 1.0 ?l water solution containing nematodes were placed on 1.5% water agar.
Individual nematodes were transferred to clean 1.5% water agar plates after 3-4 weeks. Plates
with single nematodes were incubated at 27?C for 1-2 months to allow nematode reproduction.
All other nematode specimens were hand-picked on a Nikon SMZ800 dissecting microscope
after extraction from soil by sieving and sugar flotation.
Permanent mounts (20-30) were prepared for all nematode species, except M. arenaria,
M. incognita and R. reniformis. Permanent mounts were prepared according to Seinhorst (1962)
with modifications. Individual nematodes were placed in room temperature water in
microcentrifuge tubes. The tubes were centrifuged at 2000 rpm for one min and the supernatant
was decanted. Boiling 10% formalin was added to the nematode specimens and incubated at
room temperature. After one week the formalin was replaced with 2.5% glycerin EtOH. The
EtOH was allowed to evaporate in a desecrator for one week. The specimens were mounted in
dehydrated glycerin.
Nematode specimens were keyed out to generic level on a Nikon Eclipse 80i using one of
three keys: 1) Interactive Diagnostic Key to Plant Parasitic, Free-living and Predaceous
Nematodes from the UNL Nematology Lab, 2) Identification of Freeliving Nematodes
(Secernentea) from UCR Extension, or 3) Rhabditina Generic Identification from the University
32
of Florida. The nematodes raised in pure culture on 1% water agar were identified as
Panagrolaimus sp. and Prismatolaimus sp. Hand-picked nematode specimens were identified as
Tylenchorhynchus sp., Helicotylenchus sp., Mesocriconema sp. (plant-parasitic nematodes),
Mesorhabditis sp., Acrobeles sp. (microbivorous nematodes), Neoactinolaimus sp. (fungivorous
nematode), and Monochus sp. (predacious nematode).
Two common fungi were isolated from the same peanut soils from which nematodes
were extracted. Sclerotium rolfsii (Sacc. 1911) was cultured on Acidic Potato Dextrose Agar
(APDA). Aspergillus flavus (Link 1809) was cultured on Aspergillus flavus and parasiticus agar
(AFPA) (Pitt et al., 1983).
Total genomic DNA was extracted from all 14 species (Table 1) using the UltraCleanTM
Microbial DNA Isolation Kit (MoBio Laboratories Inc. Carlsbad, CA) following the
manufacturer?s instructions. DNA quality and quantity was assessed using NanoDrop 1000
Spectrophotometer (Thermo Scientific, USA). DNA from each sample was diluted to 20 ng/?L
and stored at -80?C until required for downstream applications.
Primer specificity: Specificity of the nem1/nem2 primer pair was evaluated to determine the
range of nematode trophic group DNA that can be amplified. DNA from the 12 nematode
species and two fungal species, in the previously described DNA collection, was PCR amplified
in 50 ?l volumes consisting of 5 ?l 10X PCR buffer (200 mM Tris-HCl (pH 8.4), 500 mM KCl),
5 ?l 25mM MgCl2, 1 ?l 10 mM dNTP mix, 1 ?l each 10 ?M forward and reverse primers, 0.3 ?l
5 U/?l Platinum? Taq DNA Polymerase (Invitrogen, Carlsbad, CA), and 5 ?l template DNA
(100ng). A negative control sample without template DNA was included. The amplification
process was performed in a Techne TC-312 thermocycler. The PCR program consisted of 94?C
33
for 5 minutes; 35 cycles of 94?C for 45 seconds, 48?C for 45 seconds and 72?C for 1 minute;
followed by a final extension of 72?C for 10 minutes. PCR products were separated on a 1%
agarose gel at 100 V for 1 hour and visualized with UV illumination after staining with ethidium
bromide (EtBr) to determine if amplicons of the correct size were detected.
DGGE efficiency and sequence confirmation: DGGE analysis was carried out using the
Dcode? Universal Mutation Detection System (Bio-Rad, Hercules, CA). A total of 15 samples
were run: 12 nematode species, 2 fungal species and a negative control. PCR amplified products,
as described above, were separated on a 20-50% denaturant solution, 1mm, 6% polyacrylamide
gel. The polyacrylamide gel was prepared by mixing 15 ml of each denaturant solution with 81
?l 10% ammonium persulfate (APS) and 4.5 ?l N,N,N?,N?-tetramethylethylenediamine
(TEMED). The high and low denaturant solutions were mixed using a manual gradient delivery
system (Bio-Rad, Hercules, CA). Electrophoresis was run at 100 V for 16 hours in 1X TAE
buffer at 60?C. After electrophoresis, the gel was stained with GelStar? Nucleic Acid Gel Stain
(Lonza, Rockland, ME) for 30 minutes and rinsed with deionized water. The gel was subjected to
UV illumination using an AlphaImager? HP (Alpha Innothec Corp., San Leandro, CA) to
determine banding position.
Bands from samples intended as standards, M. arenaria, M. incognita and R. reniformis,
as well as bands from samples containing multiple bands were excised from the gel using a wide
borer pipette tip and placed in 30 ?l sterile water. The DNA was allowed to diffuse into the water
at 4?C overnight (Ampe et al., 2001). This DNA was used as template and re-amplified using
PCR conditions as described above, except the forward primer did not contain a GC clamp.
Electrophoresis on a 1% agarose gel was used to confirm the presence of the PCR amplified
34
product. The remaining amplified product was cleaned using Wizard PCR Prep kits (Promega,
Madison, WI) following the manufacturers recommendations. The samples were sequenced by
Lucigen Inc. and the results were compared to known sequences in Genbank?s nucleotide
collection using the basic local alignment search tool (BLASTN) (Altschul et al., 1990).
DGGE analysis of nematode communities from peanut rotation soils: Soil samples were
obtained from the Wiregrass Research and Extension Center in Headland, Alabama from a long
term rotation study. The rotation sequences used in this study included: continuous peanuts,
peanut/cotton, peanut/corn, and continuous bahiagrass. Rotation sequences were arranged in a
randomized complete block design with four replications. Soil sampling was conducted at
planting (May) 2008. Five soil cores were taken randomly across each plot from the root zone in
each replication. Samples were placed in a plastic bag, mixed thoroughly and stored at 10?C until
needed.
Nematodes were extracted from 100 cm3 subsamples from each plot using a sieving
process followed by sugar flotation (Jenkins, 1964). The sugar flotation process was repeated to
ensure that specimens were clean of any debris (Miller, 1957). DNA was extracted from all
samples, a ~630 bp fragment of the 18S rDNA was amplified using the nem1/nem2 primer pair,
and the amplified products were separated using DGGE as previously described.
The DGGE gel image was analyzed with BioNumerics V. 5.0 software program (Applied
Maths, Austin, TX). Following conversion, normalization, and background subtraction with
mathematical algorithms, levels of similarity between profiles were calculated with the band
based Dice coefficient. Cluster analysis was performed with the Unweighted Pair Group Method
using Arithmetic averages (UPGMA). A band matching analysis was performed and a band table
35
was created for polymorphism analysis. Bootstrap analysis of 1000 replicates was performed to
define tree robustness. Multi-Dimensional Scaling (MDS) was completed to compare the clusters
generated over different crop rotations.
Unique bands from each sample as well as common bands in all samples were excised
and re-amplified. The PCR product was re-run on DGGE to confirm that the sample yielded a
single band at the same position from which it was recovered. The samples were then sequenced
as earlier described and compared to known sequences in GenBank using BLASTN. A putative
identification was made for sequences matching those in GenBank with a score greater than 100
bits and an e-value lower than 0.001. Sequences matching the criteria were putatively identified
to the species level for 97% or higher maximum identity and to the generic level for 75-96%
maximum identity. Sequences with a 74% or lower maximum identity where considered as not
significantly matching the sequences held in GenBank.
Results
Primer Specificity: PCR amplification of genomic DNA using the nem1/nem2 primer pair with
GC clamp yielded products ~670 bp for all species tested (Fig 1). Detection of the two fungal
species by the primer pair suggests that the primers may be universal to the small subunit
ribosomal DNA gene (18S rDNA) of eukaryotic organisms, rather than specific to nematodes.
Nematodes were extracted from the soil prior to DNA extraction for further testing in this study.
DGGE efficiency and sequence confirmation: DGGE analysis of the nematode DNA yielded
bands at different positions or different denaturant concentrations for most samples (Fig 2).
Despite different melting profiles obtained from WinMelt software (Bio-Rad, Hercules, CA), M.
36
arenaria and M. incognita yielded bands at the same location. Rhabditis sp. and
Tylenchorhynchus sp. samples yielded two separate bands. Aspergillus flavus and S. rolfsii did
not yield a band at this denaturing concentration range.
Sequence of the partial 18S rDNA was obtained from the three standard samples excised
from the DGGE gel. The R. reniformis sample was closely matched to other R. reniformis
sequences with a maximum identity of 98%. Meloidogyne arenaria and M. incognita samples
were matched with other Meloidogyne sp. sequences with a maximum identity of 95% and 96%,
respectively. The two bands retrieved from the Rhabditis sp. sample were most closely matched
to Rhabdias bufonis and Rhabdolaimus sp. with 97% maximum identity for both samples. The
two bands retrieved from the Tylenchorhynchus sp. sample were matched to Tylenchorhynchus
claytoni with 96% maximum identity and Tylenchorhynchus dubius with 94% maximum
identity.
DGGE analysis of nematode community from peanut rotation soils: The DGGE profile of
nematode populations from different peanut crop rotations showed similarities among
community profiles of replicated plot samples from two separate cropping sequences (Fig 3).
Three distinct groups were defined at 50% or greater similarity with one outlier. Some common
bands were observed between crop rotations irrespective of cropping sequence. DGGE banding
patterns from the continuous peanut cropping system indicated that there was approximately
55% similarity between these plots. In the case of continuous bahiagrass, 73% similarity was
observed between the plots. The peanut/corn plots did not group together but rather grouped with
continuous bahiagrass plots, except one that grouped with the continuous peanut plots. Three
37
peanut/cotton plots grouped together with 75% similarity while the other did not group with
anything and was considered an outlier.
A total of 37 bands were observed between all of the plots. The lowest number of bands
observed in a sample was nine and the highest was 17. The average number of bands in a single
plot was 13. There was no single band observed across all samples. Multi-Dimensional Scaling
(MSD) of the DGGE community profiles from different cropping sequences revealed that
nematode communities pertaining to each crop rotation were located in different clusters with a
few outliers (Fig 4).
The DGGE profile yielded 17 common or unique bands that were excised and re-
amplified. Each DNA sample recovered from the original DGGE gel yielded a single band at the
original position from which it was recovered when again subjected to DGGE. All 17 samples
were sequenced and matched to closely related sequences in GenBank?s database. There were 15
partial 18S rDNA sequences found to have sequence similarities that placed them into known
nematode genera (Table 2). The remaining sequences had no significant similarities within the
nucleotide collection of the GenBank database. There were six sequences aligned with 97% or
higher maximum identity matching the sequences at a species level. The remaining samples had
a 76-96% maximum identity matching them at a generic level to know sequences. The putative
identification made of the 15 partial gene sequences represented 13 separate genera, two
vertebrate parasites, one entomopathogen, one herbivore, two predators, and the remaining
represented microbivores.
38
Discussion
Understanding the soil community and how the populations within the community
interact and affect plant health is important in agriculture. Identifying the populations within the
nematode community to date has been cumbersome. Using molecular fingerprinting methods
such as DGGE and recovering DNA to sequence and analyze these populations has several key
advantages over morphological identification including the savings in time and skill level
required. The results of this current study show that DGGE can be successfully applied to
analyze populations within the nematode community and to monitor shifts in the populations due
to cropping rotations.
The consensus nematode primers used in this study amplified a wide trophic range of
nematode DNA. The primer pair also amplified DNA from the two non-target fungal organisms.
This indicates that the primers may be universal to all eukaryotic organisms rather than specific
to nematodes. To ensure that other non-target eukaryotic organisms are not amplified from soil
samples, nematodes must be extracted from the soil prior to extracting DNA from the nematodes.
DGGE analysis of the DNA collection separated most of the samples at different
denaturant concentrations. The two species that were resolved at the same denaturant
concentration, M. arenaria and M. incognita, have similar melting profiles. This indicates that
DGGE may not reliably separate samples at the species level, at least species that are closely
related. Other DNA samples, Rhabditis sp. and Tylenchorhynchus sp., yielded two separate
denaturant concentration bands. The DNA recovered from these bands were matched to separate
genera and separate species, respectively. This confirms the difficulty in correctly identifying
some nematode species using morphological characteristics. The two fungal species did not yield
bands between 20-50% denaturant concentrations. Other studies using DGGE to profile fungal
39
communities use denaturant concentration ranges between 10-60% (Buesing et al., 2009; Duong
et al., 2006; Anderson et al., 2003), further demonstrating the need to eliminate other eukaryotic
organisms from the sample prior to nematode DNA extraction.
DGGE profiles of nematode communities from peanut soils under different cropping
sequences revealed population shifts between crop rotations. Common and unique bands can be
found throughout the DGGE profile irrespective of cropping sequence. Multi-Dimensional
Scaling of the DGGE profile indicated diversity of the nematode community in soils of different
cropping sequences. Clusters of the profiles were observed with respect to replications and
differed due to cropping sequence.
The band matching analysis revealed 37 bands across the DGGE profile, indicating that
37 different species were detected throughout the plots sampled. Only 17 bands were excised to
recover DNA and match sequences to those previously identified in GenBank. High background
fluorescence inhibited visualization of weaker bands and ultimately inhibited recovery of the
DNA at those positions. There is a limited number of nematode 18S gene sequences compiled in
the nucleotide collection of GenBank?s database. This explains why only six out of 17 sequences
were matched with 97% maximum identity. It has been estimated that there are possibly 500,000
different nematode species in existence, yet only approximately 12,000 have been described
(Myers, 2001). As greater numbers of nematode sequences are identified and deposited in
complied databases, sequence matching will become more precise.
Our results indicate that this DGGE technique combined with DNA recovery and
sequencing can be used to reliably and effectively monitor nematode populations in agricultural
soils. Further sampling throughout the growing season is needed to better understand the effects
the nematode population may have on crop health. Effective plant management practices may be
40
devised with a more thorough understanding of the nematode community accomplished through
constant monitoring with precise and high resolution DNA fingerprinting techniques that can
analyze populations within the nematode community and detect shifts in those populations due to
cultural practices.
41
Table 1. Trophic group and species list in nematode DNA collection used to test specificity of
nematode consensus primers.
Sample Species Trophic group
1 Meloidogyne arenaria Plant-parasitic nematodes
2 Meloidogyne incognita ?
3 Rotylenchulus reniformis ?
4 Tylenchorhynchus sp. ?
5 Helicotylenchus sp. ?
6 Mesocriconema sp. ?
7 Mesorhabditis sp. Bacterial-feeding nematodes
8 Acrobeles sp. ?
9 Prismatolaimus sp. ?
10 Panagrolaimus sp. ?
11 Neoactinolaimus sp. Fungal-feeding nematode
12 Monochus sp. Predatory nematode
13 Sclerotium rolfsii Fungus
14 Aspergillus flavus ?
42
Table 2. Putative identification of partial 18S rDNA sequences re-amplified from excised bands
from DGGE profile.
Rotation Band
position
Closest related
sequence
Max id
%
Trophic group Nematode
order
Putative
Identification
Cont peanut 13.7% Metachromadora
sp.
90% Algivore-
omnivore-predator
Chromadorida Metachromadora
sp.
Peanut/corn 22.8% Prismatolaimus
dolichurus
99% Microbivore Enoplida Prismatolaimus
dolichurus
Cont peanut 29.8% Panagrellus
redivivus
97% Microbivore Rhabditida Panagrellus
redivivus
Peanut/cotton 36.6% Panagrolaimus
rigidus
98% Microbivore Rhabditida Panagrolaimus
rigidus
Peanut/corn 36.6% Alaimus sp. 92% Microbivore Enoplida Alaimus sp.
Peanut/cotton 42.8% Anatonchus
tridentatus
97% Predatory Monochida Anatonchus
tridentatus
Cont bahia 47.2% Mylonchulus
brachyuris
94% Predatory Monochida Mylonchulus sp.
Cont peanut 49.4% Acrobeles ciliatus 99% Microbivore Rhabditida Acrobeles ciliatus
Cont peanut 51.3% Acrobeloides
butschlii
99% Microbivore Rhabditida Acrobeloides
butschlii
Peanut/cotton 55.6 % Panagrolaimus
superbus
90% Microbivore Rhabditida Panagrolaimus sp.
Cont peanut 58.5% Meloidogyne
javanica
95% Herbivore Tylenchida Meloidogyne sp.
Cont peanut 60.4% Caenorhabditis
elegans
96% Microbivore Rhabditida No match
Cont peanut 63.2% Meloidogyne
arenaria
96% Herbivore Tylenchida Meloidogyne sp.
Peanut/cotton 68.2% Gongylonema
pulchrum
95% Vertebrate parasite Spirurida Gongylonema sp.
Peanut/cotton 70.2% No significant
similarity found
No match
Peanut/cotton 73.7% Steinernema
bicornutum
95% Entomopathogen Rhabditida Steinernema sp.
Peanut/cotton 86.6% Toxocara
vitulorum
76% Vertebrate parasite Ascaridida Toxocara sp.
43
Figure 1. Polymerase Chain Reaction amplified product detection of nematode 18S rDNA.
Note: M ? 100 bp marker, N - negative control, Lane 2-7 - samples in nematode DNA collection:
Aspergillus flavus, Panagrolaimus sp., Helioctylenchus sp., Meloidogyne arenaria,
Rotylenchulus reniformis, Mesocriconema sp., and Neoactinolaimus sp.
M 2 3 4 5 6 7 N
700bp
600bp
500bp
44
Figure 2. Denaturing Gradient Gel Electrophoresis image of nematode and fungal 18S rDNA
amplified products.
Note: Lane 1-15 ? samples in DNA collection: Aspergillus flavus, Panagrolaimus sp.,
Helioctylenchus sp., Meloidogyne arenaria, Rotylenchulus reniformis, Mesocriconema sp.,
Neoactinolaimus sp., Rhabditis sp., Prismatolaimus sp., Acrobeles sp., Monochus sp.,
Helicotylenchus sp. (repetition), Tylenchorhynchus sp., Meloidogyne incognita, and Sclerotium
rolfsii.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
45
Figure 3. Nematode Denaturing Gradient Gel Electrophoresis profile obtained from different
peanut cropping sequences at the Wiregrass Research and Extension Center at pre-plant 2008.
DGGE
B a nd T a bl e
100806040
87
96
79
76
92
83
DG G E
100806040
D G G E B an d T ab le
D
G
G
E
:
1
3
.
6
%
D
G
G
E
:
1
5
.
9
D
G
G
E
:
1
7
.
4
%
D
G
G
E
:
2
0
.
4
%
D
G
G
E
:
2
2
.
3
%
D
G
G
E
:
2
3
.
8
%
D
G
G
E
:
2
7
.
0
%
D
G
G
E
:
2
8
.
4
%
D
G
G
E
:
2
9
.
4
%
D
G
G
E
:
3
1
.
6
%
D
G
G
E
:
3
3
.
1
%
D
G
G
E
:
3
4
.
5
%
D
G
G
E
:
3
6
.
6
%
D
G
G
E
:
3
9
.
1
%
D
G
G
E
:
4
1
.
9
%
D
G
G
E
:
4
3
.
5
%
D
G
G
E
:
4
4
.
9
D
G
G
E
:
4
6
.
2
D
G
G
E
:
4
7
.
2
%
D
G
G
E
:
4
9
.
3
%
D
G
G
E
:
5
1
.
8
%
D
G
G
E
:
5
3
.
1
%
D
G
G
E
:
5
4
.
3
%
D
G
G
E
:
5
5
.
7
%
D
G
G
E
:
5
7
.
5
%
D
G
G
E
:
5
8
.
9
%
D
G
G
E
:
6
0
.
4
%
D
G
G
E
:
6
2
.
1
%
D
G
G
E
:
6
3
.
2
%
D
G
G
E
:
6
4
.
6
%
D
G
G
E
:
6
6
.
1
%
D
G
G
E
:
6
8
.
2
%
D
G
G
E
:
7
0
.
5
%
D
G
G
E
:
7
2
.
2
%
D
G
G
E
:
7
3
.
7
%
D
G
G
E
:
7
5
.
0
%
D
G
G
E
:
8
6
.
2
%
A1
C1
A2
C2
C3
A3
A4
D1
D2
D3
D4
B1
B2
B3
C4
B4
119
409
427
205
328
223
304
128
330
215
406
107
237
437
120
316
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
pre- pl an t
Note: The scale represents % of similarity calculated by the Dice correlation. The dendrogram
was constructed using the unweighted pair-group method with arithmetic mean (UPGMA).
Colors are representative of crop rotations: green ? continuous bahiagrass, blue ? peanut/corn,
yellow ? peanut/cotton, and red ? continuous peanut.
46
Figure 4. Multi-dimensional Scaling of nematode communities from peanut soil samples under
various crop rotations collected at pre-plant from the Wiregrass Research and Extension Center
in 2008, colored by crop rotation.
47
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Station. Amherst, Massachusetts. Pg. 163-172.
49
Hussey, R.S. and Barker, K.R. 1973. A comparison of methods of collecting inocula of
Meloidogyne spp., including a new technique. Plant Disease Reporter 57(12):1025-1028.
Ingham, R.E., Trofymow, J.A., Ingham, E.R., and Coleman, D.C. 1985. Interactions of bacteria,
fungi, and their nematode grazers: effects on nutrient cycling and plant growth. Ecological
Monographs 55(1):119-140.
Jenkins, 1964. A rapid centrifugal flotation technique for separating nematodes from soil. Plant
Disease Reporter 48:692.
Miller, P.M. 1957. A method for the quick separation of nematodes from soil samples. Plant
Disease Reporter 41:194.
Muyzer, G., De Waal, E.C., and Uitterlinden, A.G. 1993. Profiling of complex microbial
populations by denatured gradient gel electrophoresis analysis of polymerase chain reaction-
amplified genes coding for 16S rRNA. Applied and Environmental Microbiology 59:695-
700.
Myers, R.M., Fischer, S.G., Lerman, L.S., and Maniatis, T. 1985. Nearly all single base
substitutions in DNA fragments joined to a G-C clamp can be detected by DGGE. Nucleic
Acid Research 13:3131-3145.
Myers, P. 2001. ?Nematoda? (On-line), Animal Diversity Web. Accessed April 29, 2010 at
http://animaldiversity.ummz.umich.edu/site/accounts/information/Nematoda.html.
Neher, D.A., 2001. Role of nematodes in soil health and their use as indicators. Journal of
Nematology 33:161-168.
Pitt, J.I., Hocking, A.D., and Glenn, D.R. 1983. An improved medium for detection of
Aspergillus flavus and A. parasiticus. Journal of Applied Bacteriology 54:109-114.
50
Seinhorst, J.W. 1962. On the killing, fixing and transferring to glycerin of nematodes.
Nematologica 8:29-32.
Waite, I.S., O?Donnell, A.G., Harrison, A., Davies, J.T., Colvan, S.R. Ehschmitt, K., Dogan, H.,
Wolters, V., Bongers, T., Bongers, M., Bakonyi, G., Nagy, P., Papatheodorou, E.M., Stamou,
G.P., and Bostrom, S. 2003. Design and evaluation of nematode 18S rDNA primers for PCR
and denaturing gradient gel electrophoresis (DGGE) of soil community DNA. Soil Biology
and Biochemistry 35:1165-1173.
Woods, L.E., Cole, C.V., Elliott, E.T., Anderson, R.V., and Coleman, D.C. 1982. Nitrogen
transformations in soil as affected by bacterial-microfaunal interactions. Soil Biology and
Biochemistry 14:93-98.
Wright, D.J. and Newall, D.R. 1976. Nitrogen excretion, osmotic and ionic regulation in
nematodes. In The organization of nematodes. N.A. Croll, ed. Academic Press, New York.
Pg 1630-210.
51
Chapter III. DGGE Fingerprinting of Nematode Community Structure under Peanut
Rotation Systems
Abstract
The nematode community within agricultural soils consists of plant-parasitic and free-living
nematodes, both of which can affect plant health. Understanding the nematode community
structure may be important in disease management. The objective of this study was to identify
the populations present in the nematode community of peanut soils under differing crop rotations
using genetic fingerprinting methods to determine if factors exist that result in nematode
population shifts. Genetic profiles of the total nematode community from peanut soils were
generated through extraction of nematode DNA, amplification of partial 18S rDNA using
nematode consensus primers and subsequent separation by denaturing gradient gel
electrophoresis (DGGE). Samples were collected from four different crop rotation patterns
(continuous peanut, peanut/corn, peanut/cotton, and continuous bahiagrass) at three sampling
periods (pre-plant, mid-season, and harvest) for two consecutive years (2008 and 2009). Unique
and common bands in each molecular fingerprint were then excised, re-amplified and sequenced
in order to identify populations within the nematode community. DGGE results indicated
rotation sequence resulted in population shifts, although minimal similarities were found
between replications of crop rotations (51-68%). Sampling time impacted nematode community
structure also. Free-living nematodes accounted for 64% of the recovered DNA sequences,
althoughplant-parasitic nematodes, animal parasitic nematodes, and entomopathogenic
nematodes were present in lower populations. Trends in the data suggest that some microbivore
52
nematode populations may play a role in the suppression of plant-parasitic nematodes. These
results indicate that crop rotation and other environmental factors affect nematode community
structure and certain populations may affect disease management.
Introduction
The nematode community represents a complex structure within the soil that affects plant
health. This community consists of plant-parasitic nematodes and free-living nematodes. Free-
living nematodes include microbivores (bacterial-feeders), fungivores (fungal-feeders) and
predatory nematodes. These free-living nematodes have direct and indirect effects on soil
nutrition that can affect other soil organisms (Neher, 2001). Free-living nematodes are
commonly attributed to increased plant growth, increased nitrogen (N) uptake by plants,
decreased or increased bacterial populations, increased CO2 evolution, increased N and
phosphorous (P) mineralization, and increased substrate utilization (Ingham et al., 1985).
In order to understand the entire nematode community and the effects it has on crop
health nematode populations need to be identified. Traditional techniques employed to describe
the composition and diversity of nematode populations in the soil rely on phenotypic
characteristics, which is time-consuming and requires extensive training. Limitations to
nematode community analysis may be overcome by using molecular analytical tools to directly
explore the composition in a soil sample based on the nucleic acids present.
Among the molecular analytical techniques available, molecular fingerprinting methods,
such as denaturing gradient gel electrophoresis (DGGE), can help monitor changes in microbial
communities over time with a simplified representation of the community. These methods
53
generate population specific fingerprints that display the ribosomal polymorphism naturally
present in the community.
Previous research has shown that nematode populations can be analyzed using a
combination of DGGE and DNA recovery for sequencing (Conner and Huettel, unpublished).
Using nematode consensus primers, nematodes that have previously been extracted from the soil
can be amplified by Polymerase Chain Reaction (PCR) and the amplified products can be
separated by DGGE. Bands within the genetic profiles, representing nematode genera, can be
excised and DNA can be reamplified for sequencing. Sequences can then be compared to those
previously identified in GenBank to putatively identify the populations present in the nematode
community. Genetic profiles can also be analyzed to observe trends between samples.
The objective of the current research was to identify the populations present within the
nematode community of peanut soils under differing crop rotations to determine if rotation
sequence results in a shift in nematode populations. This was accomplished by generating
population specific fingerprints using DGGE to monitor nematode populations throughout the
growing season under different crop rotations. DNA was recovered from these fingerprints to
identify nematode populations present in the peanut soils.
Materials and Methods
Soil samples: Soil samples were obtained from the Wiregrass Research and Extension Center in
Headland, Alabama (31? 21? N, 85? 20? W) from a long term rotation study. The rotation
sequences used in this study included: continuous peanuts, peanut/cotton, peanut/corn, and
continuous bahiagrass. The soil is a Dothan sandy loam (OM<1%). Rotation sequences are
arranged in a randomized complete block design with four replications. Each plot is 50 ft long
54
with 12 rows per plot and three ft between each row. Samples were collected at pre-plant, mid-
season or pegging and harvest for 2 consecutive years (2008 and 2009). Seven soil cores (6 inch
depth) were taken randomly across each plot from the root zone in each replication. Samples
were placed in a plastic bag, mixed thoroughly and stored at 10?C until needed.
Nematode and DNA extraction: Nematodes were extracted from a 100 cm3 sub-sample from
each plot using a sieving process followed by sugar flotation (Jenkins, 1964) prior to DNA
extraction. The sugar flotation process was completed twice to ensure clean specimens (Miller,
1957). Total genomic DNA was extracted using the UltraCleanTM Microbial DNA Isolation Kit
(MoBio Laboratories Inc. Carlsbad, CA) following the manufacturer?s instructions. DNA quality
and quantity was assessed using a NanoDrop 1000 Spectrophotometer (Thermo Scientific,
USA). DNA from each sample was diluted to 20 ng/?L and stored at -80?C until required for
downstream applications.
PCR amplification of 18S rDNA: Nematode consensus primers designed to amplify a ~630 bp
fragment of the 18S rDNA were used in this experiment: nem1 - forward (5?-
GCAAGTCTGGTGCCAGCAGC-3?) and nem2 - reverse (5?-CCGTGTTGAGTCAAATTAAG-
3?) (Foucher and Wilson, 2002). The forward primer contained a 39 bp GC clamp at the 5? end to
prevent complete denaturation of the amplified product during electrophoresis (Myers et al.,
1985).
DNA extracted from each sample was PCR amplified in 50 ?l volumes consisting of 5 ?l
10X PCR buffer (200 mM Tris-HCl (pH 8.4), 500 mM KCl), 5 ?l 25mM MgCl2, 1 ?l 10 mM
dNTP mix, 1 ?l each 10 ?M forward and reverse primers, 0.3 ?l 5 U/?l Platinum? Taq DNA
55
Polymerase (Invitrogen, Carlsbad, CA), and 5 ?l template DNA (100 ng). A negative control
sample without template DNA was included in each run. The amplification process was
performed in a Techne TC-312 thermocycler. The PCR program consisted of 94?C for 5 minutes;
35 cycles of 94?C for 45 seconds, 48?C for 45 seconds and 72?C for 1 minute; followed by a final
extension of 72?C for 10 minutes. PCR products were separated on a 1% agarose gel at 100 V for
1 hour and visualized on a 312 nm Variable Intensity Transilluminator (Fisher Scientific,
Pittsburgh, PA) after staining with ethidium bromide (EtBr) to confirm the presence of the PCR
amplified product.
DGGE analysis: DGGE analysis was performed on amplified DNA using the Dcode? Universal
Mutation Detection System (Bio-Rad, Hercules, CA). PCR amplified products were separated on
a 20-50% denaturant solution, 1 mm, 6% polyacrylamide gel. The polyacrylamide gel was
prepared by mixing 15 ml of each denaturant solution with 81 ?l 10% ammonium persulfate
(APS) and 4.5 ?l N,N,N?,N?-tetramethylethylenediamine (TEMED). The high and low
denaturant solutions were mixed using a manual gradient delivery system (Bio-Rad, Hercules,
CA). Electrophoresis was run at 100 V for 16 hours in 1X TAE buffer at 60?C. After
electrophoresis, the gel was stained with GelStar? Nucleic Acid Gel Stain (Lonza, Rockland,
ME) for 30 minutes and rinsed with deionized water. The gel was subjected to UV illumination
and photographed using an Olympus C-4000 digital camera with the GelStar? Photographic
Filter (Lonza, Rockland, ME).
DNA sequencing: Unique bands from each sample and common bands in all samples were
excised from the gel using a wide borer pipette tip and placed in 30 ?l sterile water. The DNA
56
was allowed to diffuse into the water at 4?C overnight (Ampe et al., 2001). This DNA was used
as template and re-amplified using PCR conditions as described above. Electrophoresis on a 1%
agarose gel was used to confirm the presence of the PCR amplified product. Prior to sequencing,
purity of the re-amplified DNA was checked using another DGGE run to confirm that the sample
yielded a single band at the same position it was recovered from. The samples were then
sequenced by Lucigen Inc. in both directions. The partial 18S sequences were compared to know
sequences in Genbank?s nucleotide collection using the basic local alignment search tool
(BLASTN) (Altschul et al., 1990). A putative identification was made for sequences matching
those in GenBank with a score greater than 100 bits and an e-value lower than 0.001. Sequences
matching the criteria were putatively identified to the species level for 97% or higher maximum
identity and to the generic level for 75-96% maximum identity. Sequences with a 74% or lower
maximum identity where considered as not significantly matching the sequences held in
GenBank.
Phylogenetic analysis: The DGGE gel images were analyzed with BioNumerics V. 5.0 software
program (Applied Maths, Austin, TX). Following conversion, normalization, and background
subtraction with mathematical algorithms, levels of similarity between profiles were calculated
with the band based Dice coefficient. Cluster analysis was performed with the Unweighted Pair
Group Method using Arithmetic averages (UPGMA). A band matching analysis was performed
and a band table was created for polymorphism analysis. Bootstrap analysis of 1000 replicates
was performed to define tree robustness. Multi-Dimensional Scaling (MDS) was completed to
compare the clusters generated over different crop rotations and between different sampling
periods. The band table was exported from BioNumerics and subjected to Principle Component
57
Analysis (PCA) using SAS version 9.1.3 (SAS Institute, Cary, NC) to determine if any
relationships existed between band classes.
Results
DNA sequencing: DGGE analysis of nematode communities extracted from soil samples under
different crop rotations revealed individual banding patterns with a number of distinguishable
bands representing different nematode taxa. In total, 121 partial 18S rDNA sequences were
recovered from the DGGE gels. There were 103 sequences that matched previously identified
sequences held online at GenBank (Tables 1-2). Sequences that showed 97?100% maximum
identity accounted for 41% of the total recovered DNA sequences and those that showed 75?
96% maximum identity accounted for 59% of the total recovered DNA sequences. There were
18 sequences that did not meet the criteria previously described for putative identification.
A total of 93 recovered DNA sequences were matched to nematodes representing 29
genera within 10 nematode orders, and 10 sequences were matched to fungi representing 3
fungal genera (Nematoctonus sp., Paeilomyces sp. and Fusarium sp.). Putative identification of
free-living nematodes accounted for 64%, plant-parasitic nematodes accounted for 14.5%,
animal parasitic nematodes accounted for 8.7%, entomopathogenic nematodes accounted for
2.9%, and fungi accounted for 9.7% of the recovered DNA sequences.
Phylogenetic analysis: The nematode DGGE profiles showed similarities among communities
of some replicates sampled from the same crop rotation (Fig 1-2). Common bands were observed
among the majority of samples irrespective of sampling period and cropping sequence.
Similarities within a range of 51-68% were observed among the plots of different cropping
58
sequences in 2008 (Fig 5) and 52-59% in 2009 (Fig 6). DGGE banding patterns in continuous
peanut plots indicated that there were approximately 58% similarities in all samples that were
taken at pre-plant 2008, 64% at mid-season 2008, 55% at harvest 2008 and 52% at harvest 2009.
In the continuous bahiagrass rotation, 68% similarities were observed in plots that were sampled
during pre-plant 2008. In the peanut/cotton rotation plots sampled at mid-season 2008, 66%
similarities were observed whereas the plots sampled at harvest 2008 had 60% and harvest 2009
had 59%. The peanut/corn rotation plots sampled at harvest 2008 showed 51% similarity and
those sampled at harvest 2009 showed 59% similarities. In 2009, peanut plots were divided into
two varieties Florida 07 and Tifguard. DGGE profiles of nematode communities showed
similarities between the varieties ranging from 51-93% similarity.
Multi-dimensional Scaling (MDS) based on DGGE community profiles of different
cropping sequences revealed that nematode communities pertaining to each cropping sequence
had fewer similarities in general with greater scattering in 2008 and 2009, indicating the impact
of cropping sequence on nematode diversity is minimal (Fig 3a & 4a). More similarities were
observed with respect to nematode composition in the plots that were sampled during identical
sampling periods irrespective of the cropping sequence in practice (Fig 3b & 4b).
In 2008, there was a total of 50 bands across all samples; whereas, there were 57 in 2009.
The mean number of bands from a single sample in 2008 was 17 and in 2009 the mean number
of bands was 20. In both years, sampling period significantly affected total bands within a
sample (p=<0.0001). In 2008, pre-plant samples contained fewer bands (mean=13) than mid-
season samples (mean=19), and harvest samples contained the most bands (mean=21). A similar
tend occurred in 2009, pre-plant samples contained the fewest bands (mean=14), mid-season
59
samples contained a mean of 20 bands and harvest samples contained the most bands (mean=26).
Crop rotation did not affect total bands.
Principal component analysis of band table data created from nematode communities in
2008 and 2009 revealed loadings of similar value (>0.20 or <-0.30) between band classes that
were putatively identified from recovered DNA from genetic profiles. The data from both years
indicated that certain microbivore populations (putatively identified as Panagrellus redivivus,
Panagrolaimus rigidus and Prismatolaimus dolichurus) consistently had positive loadings
ranging from 0.20-0.29 while the putatively identified band class Meloidogyne sp. consistently
had negative loadings ranging from -0.21 to -0.26 in the first component. The second component
revealed positive loadings on band classes putatively identified as Rhabdolaimus sp. and
Acrobeles ciliatus (0.21-0.27). Anatonchus tridentatus and Mylonchulus sp. putative band classes
consistently had a positive load (0.22) in the third component. These three components explained
only 33% of the variance in the data in both years.
Discussion
The combined use of Denaturing Gradient Gel Electrophoresis (DGGE) and sequencing of DNA
recovered from genetic profiles was applied in this study to identify the populations present
within the nematode community of peanut soils under differing crop rotations to determine if
rotation sequences resulted in a shift in nematode populations. Our results indicated nematode
populations did shift based on peanut cropping sequence, although similarities were minimal
between replications within crop rotations. Multi-dimensional scaling revealed scattering based
on nematode community profiles of different crop rotations indicating the impact of cropping
60
sequence on nematode diversity was minimal. These results show a wide range of nematode
community polymorphisms were present irrespective of crop rotation.
Similarities were also observed between plots sampled at the same period. Multi-
dimensional scaling revealed clustering of nematode communities based on sampling period
indicating the period when samples were taken had a greater impact on nematode composition
than did cropping sequence. Since the sampling periods were set at prescribed times through the
growing season based on crop age, the specific crop age and environmental factors could be
playing an important role in the nematode community rather than plant species. Sampling period
also significantly affected the total number of bands from a sample. Pre-plant samples contained
fewer bands than mid-season samples, and harvest samples contained the most bands. Pre-plant
samples were expected to support lower biodiversity because all plots were fallowed through the
winter. This follows the rate of reproduction of most nematodes with populations increasing in
fall.
Results of DNA recovery from genetic profiles and DNA sequencing revealed that free-
living nematodes accounted for the majority of populations present in the nematode community.
Plant-parasitic nematodes, animal parasitic nematodes, entomopathogenic nematodes, and
nematophagus fungi were also present in the plots sampled but at much lower population levels.
Sequences that showed 97-100% maximum identity with those in the nucleotide collection of the
GenBank database accounted for 41% of the total recovered sequences. The GenBank database
only contains approximately 20,000 nematode 18S sequences. It has been estimated that there
are possibly 500,000 different nematode species in existence, yet only approximately 12,000
have been described (Myers, 2001). As more nematode sequences are identified and deposited in
complied databases, sequence matching will become more precise.
61
There were 29 nematode genera and three fungal genera putatively identified out of the
57 total bands. High background fluorescence inhibited visualization of weaker bands and
ultimately inhibited recovery of the DNA at those positions.
Previous studies have reported 100 nematode species belonging to 48 genera present per
sampling site in agricultural settings (Baird and Bernard, 1984). In this study we found 29
nematode genera. It has been well documented that DGGE techniques only display populations
that make up 1% or more of the total community (Murray et al., 1996; Muyzer et al., 1993;
Foucher et al., 2004). It is possible that more genera were present in these samples but were
omitted because they represented <1% of the total nematode biomass.
Three fungal genera were identified within the samples sequenced. These were putatively
identified as Nematoctonus sp., Paeilomyces sp. and Fusarium sp. These fungal genera have
been reported to parasitize nematodes and are classified as nematophagus fungi (Jaffee et al.,
1998; Dickson et al., 1994; Olatinwo et al., 2006). When extracting nematodes from the soil, the
sugar flotation process was performed twice in order to ensure specimens were clean of debris
because the primers used to amplify nematode DNA also amplify fungal DNA. This indicates
these three fungal organisms were most likely present inside the nematode body.
Four vertebrate parasites were identified in the samples sequenced. These were putatively
identified as Gongylonema sp., Thelazia sp., Toxocara sp., and Passalurus sp. Gongylonema sp.
is a nematode parasite of birds and other mammals transmitted by insects (Kudo et al., 2005).
Thelazia sp. is a genus of nematodes parasitic in the eyes of mammals transmitted by species of
Diptera (Otranto and Traversa, 2005). Toxocara sp. is the genera of animal parasitic nematodes
that cause infections in pets known as round worms (Samuel et al., 2001). Passalurus sp. is a
62
nematode parasite of rabbits (Erickson, 1944). Juveniles of animal parasitic nematodes may be
found in the soil.
Principal component analysis of putatively identified band classes revealed a trend
between Panagrellus redivivus, Panagrolaimus rigidus and Prismatolaimus dolichurus, all of
which are microbivorous nematodes, and Meloidogyne populations. These results suggest that
the presence of microbivouous nematodes may suppress herbivorous populations.
Using DGGE techniques combined with nematode DNA recovery from genetic profiles
followed by sequencing as an alternative method to monitoring nematode communities and
identifying nematode populations proved beneficial in studying the impact of long term crop
rotations on resident nematode communities. The four peanut cropping sequences selected in this
study are widely used in agriculture for the management of several peanut diseases (Rodriguez-
Kabana et al, 1991; Timper et al., 2001; Bowen et al., 1996). This research demonstrates that
peanut crop rotations affect nematode community profiles and that sampling period has the
greatest influence on nematode population composition. The data generated from DNA recovery
and sequencing also suggested that microbivore nematode populations may play a role in the
suppression of herbivore nematodes. Further sampling refinement is needed to better understand
the nematode biodiversity in these soils. Plant disease management practices may only be
devised through constant monitoring of factors that influence the nematode community and the
impact individual populations may have on plant health.
63
Table 1. Putative identification of nematode partial 18S rDNA sequences re-amplified from
excised bands recovered from 2008 Denaturing Gradient Gel Electrophoresis profiles of peanut
soil samples under various rotations from the Wiregrass Research and Extension Center.
Putative identification of
genus
Nematode order Trophic group
Acrobeles Rhabditida Microbivore
Acrobeloides Rhabditida Microbivore
Alaimus Enoplida Microbivore
Anatonchus Monochida Predator
Aphelenchoides Tylenchida Fungivore
Cephalobus Rhabditida Microbivore
Fusarium Ascomycete Fungus
Gongylonema Spirurida Vertebrate parasite
Helicotylenchus Tylenchida Herbivore
Meloidogyne Tylenchida Herbivore
Metachromadora Chromadorida Algivore-omnivore-predator
Mylonchulus Monochida Predator
Nematoctonus Basidiomycete Fungus
Paeilomyces Hypocreomycetidae Fungus
Panagrellus Rhabditida Microbivore
Panagrolaimus Rhabditida Microbivore
Panagrolaimus Rhabditida Microbivore
Paratrichodorus Triplonchida Herbivore
Pratylenchus Tylenchida Herbivore
Prismatolaimus Enoplida Microbivore
Rhabdolaimus Araeolaimida Microbivore
Steinernema Rhabditida Entomopathogen
Thelazia Spirurida Vertebrate parasite
Toxocara Ascaridida Vertebrate parasite
64
Table 2. Putative identification of nematode partial 18S rDNA sequences re-amplified from
excised bands recovered from 2009 Denaturing Gradient Gel Electrophoresis profiles of peanut
soil samples under various rotations from the Wiregrass Research and Extension Center.
Putative identification of
genus
Nematode order Trophic group
Acrobeloides Rhabditida Microbivore
Alaimus Enoplida Microbivore
Anatonchus Monochida Predator
Bathyodontus Monochida Predator
Bunonema Rhabditida Microbivore
Bursaphelenchus Tylenchida Herbivore-fungivore
Eucephalobus Rhabditida Microbivore
Fusarium Ascomycete Nematophagus fungus
Gongylonema Spirurida Vertebrate parasite
Heterocephalobus Rhabditida Microbivore
Meloidogyne Tylenchida Herbivore
Mylonchulus Monochida Predator
Nematoctonus Basidiomycete Nematophagus fungus
Odontophora Araeolaimida Algivore-omnivore-predator
Paeilomyces Ascomycete Nematophagus fungus
Panagrellus Rhabditida Microbivore
Panagrobelus Rhabditida Microbivore
Panagrolaimus Rhabditida Microbivore
Paratrichodorus Triplonchida Herbivore
Passalurus Oxyurida Vertebrate parasite
Pratylenchus Tylenchida Herbivore
Prismatolaimus Enoplida Microbivore
Rhabdolaimus Araeolaimida Microbivore
Steinernema Rhabditida Entomopathogen
Toxocara Ascaridida Vertebrate parasite
Trischistoma Enoplida Predator
65
Figure 1. Denaturing Gradient Gel Electrophoresis profile of nematode communities from peanut soil samples under various crop
rotations collected at pre-plant, mid-season and harvest from the Wiregrass Research and Extension Center in 2008.
D1
D2
D3
C1
D4
C2
B1
D5
C3
B2
C4
C5
C6
C7
C8
B3
B4
D6
D7
B5
A1
B6
A2
A3
A4
B7
B8
D8
A5
A6
A7
A8
A9
B9
A10
A11
A12
C9
C10
C11
D9
B10
D10
B11
B12
D11
D12
C12
223
304
119
330
427
128
120
304
406
409
215
215
330
406
128
328
409
119
223
205
437
328
237
316
107
120
205
427
107
316
437
237
437
120
107
237
316
128
330
215
119
409
427
205
328
223
304
406
mid-season
mid-season
mid-season
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
cont bahia
cont bahia
cont bahia
peanut/cotton
cont bahia
peanut/cotton
peanut/corn
cont bahia
peanut/cotton
peanut/corn
peanut/cotton
peanut/cotton
peanut/cotton
peanut/cotton
peanut/cotton
peanut/corn
peanut/corn
cont bahia
cont bahia
peanut/corn
cont peanut
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/corn
peanut/corn
cont bahia
cont peanut
cont peanut
cont peanut
cont peanut
cont peanut
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut/cotton
peanut/cotton
cont bahia
peanut/corn
cont bahia
peanut/corn
peanut/corn
cont bahia
cont bahia
peanut/cotton
bahia
bahia
bahia
peanut
bahia
peanut
peanut
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahia
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahia
peanut
bahia
peanut
peanut
bahia
bahia
peanut
D1
D2
D3
C1
D4
C2
B1
D5
C3
B2
C4
C5
C6
C7
C8
B3
B4
D6
D7
B5
A1
B6
A2
A3
A4
B7
B8
D8
A5
A6
A7
A8
A9
B9
A10
A11
A12
C9
C10
C11
D9
B10
D10
B11
B12
D11
D12
C12
223
304
119
330
427
128
120
304
406
409
215
215
330
406
128
328
409
119
223
205
437
328
237
316
107
120
205
427
107
316
437
237
437
120
107
237
316
128
330
215
119
409
427
205
328
223
304
406
mid-season
mid-season
mid-season
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
cont bahia
cont bahia
cont bahia
peanut/cotton
cont bahia
peanut/cotton
peanut/corn
cont bahia
peanut/cotton
peanut/corn
peanut/cotton
peanut/cotton
peanut/cotton
peanut/cotton
peanut/cotton
peanut/corn
peanut/corn
cont bahia
cont bahia
peanut/corn
cont peanut
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/corn
peanut/corn
cont bahia
cont peanut
cont peanut
cont peanut
cont peanut
cont peanut
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut/cotton
peanut/cotton
cont bahia
peanut/corn
cont bahia
peanut/corn
peanut/corn
cont bahia
cont bahia
peanut/cotton
bahia
bahia
bahia
peanut
bahia
peanut
peanut
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahia
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahia
peanut
bahia
peanut
peanut
bahia
bahia
peanut
D1
D2
D3
C1
D4
C2
B1
D5
C3
B2
C4
C5
C6
C7
C8
B3
B4
D6
D7
B5
A1
B6
A2
A3
A4
B7
B8
D8
A5
A6
A7
A8
A9
B9
A10
A11
A12
C9
C10
C11
D9
B10
D10
B11
B12
D11
D12
C12
223
304
119
330
427
128
120
304
406
409
215
215
330
406
128
328
409
119
223
205
437
328
237
316
107
120
205
427
107
316
437
237
437
120
107
237
316
128
330
215
119
409
427
205
328
223
304
406
mid-season
mid-season
mid-season
harves
harves
harves
harves
harves
harves
harves
harves
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
harves
harves
harves
harves
harves
harves
harves
harves
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
cont bahia
cont bahia
cont bahia
peanut/cotton
cont bahia
peanut/cotton
peanut/corn
cont bahia
peanut/cotton
peanut/corn
peanut/cotton
peanut/cotton
peanut/cotton
peanut/cotton
peanut/cotton
peanut/corn
peanut/corn
cont bahia
cont bahia
peanut/corn
cont peanut
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/corn
peanut/corn
cont bahia
cont peanut
cont peanut
cont peanut
cont peanut
cont peanut
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut/cotton
peanut/cotton
cont bahia
peanut/corn
cont bahia
peanut/corn
peanut/corn
cont bahia
cont bahia
peanut/cotton
bahia
bahia
bahia
peanut
bahia
peanut
peanut
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahia
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahia
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bahi
peanut
bahi
peanut
peanut
bahi
bahi
peanut
DGGE
Ban d T a b le
10090807060504030
100
27
19
43
19
28
74
54
16
64
48
34
35
26
2
12
21
7
73
58
10
6
4
77
25
3
66
35
43
12
9
91
83
75
80
13
99
64
37
27
87
30
47
76
14
16
DG G E
66
Figure 2. Denaturing Gradient Gel Electrophoresis profile of nematode communities from peanut soil samples under various crop
rotations collected at pre-plant, mid-season and harvest from the Wiregrass Research and Extension Center in 2009.
A23
C12
C11
A24
A21
A22
B12
A4
A5
C9
B10
B9
A8
A7
A6
D4
D5
D6
A9
A11
A10
C4
C1
D3
C2
C3
B1
D10
D11
A18
A17
A19
A20
C10
B11
D12
D9
B4
B2
B3
C8
C7
A15
A16
B7
D8
A13
A14
B8
C6
B5
B6
D7
D1
D2
A1
A2
A3
A12
C5
237f
215
128
237t
107f
107t
223
316t
107f
406
427
304
107t
437t
437f
328
409
205
237f
316f
237t
215
406
120
128
330
427
205
328
316f
437t
316t
437f
330
119
120
409
304
223
119
128
215
237t
237f
119
120
107f
107t
223
406
427
304
409
205
328
316f
316t
437f
437t
330
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
harvest
harvest
mid-season
mid-season
mid-season
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
mid-season
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
cont peanut
peanut/cotton
peanut/cotton
cont peanut
cont peanut
cont peanut
cont bahia
cont peanut
cont peanut
peanut/cotton
cont bahia
cont bahia
cont peanut
cont peanut
cont peanut
peanut/corn
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut/cotton
peanut/corn
peanut/cotton
peanut/cotton
cont bahia
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
cont peanut
peanut/cotton
cont bahia
peanut/corn
peanut/corn
cont bahia
cont bahia
cont bahia
peanut/cotton
peanut/cotton
cont peanut
cont peanut
cont bahia
peanut/corn
cont peanut
cont peanut
cont bahia
peanut/cotton
cont bahia
cont bahia
peanut/corn
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut
cotton
cotton
peanut
peanut
peanut
bahia
peanut
peanut
cotton
bahia
bahia
peanut
peanut
peanut
corn
corn
corn
peanut
peanut
peanut
cotton
cotton
corn
cotton
cotton
bahia
corn
corn
peanut
peanut
peanut
peanut
cotton
bahia
corn
corn
bahia
bahia
bahia
cotton
cotton
peanut
peanut
bahia
corn
peanut
peanut
bahia
cotton
bahia
bahia
corn
corn
corn
peanut
peanut
peanut
peanut
cotton
A23
C12
C11
A24
A21
A22
B12
A4
A5
C9
B10
B9
A8
A7
A6
D4
D5
D6
A9
A11
A10
C4
C1
D3
C2
C3
B1
D10
D11
A18
A17
A19
A20
C10
B11
D12
D9
B4
B2
B3
C8
C7
A15
A16
B7
D8
A13
A14
B8
C6
B5
B6
D7
D1
D2
A1
A2
A3
A12
C5
237f
215
128
237t
107f
107t
223
316t
107f
406
427
304
107t
437t
437f
328
409
205
237f
316f
237t
215
406
120
128
330
427
205
328
316f
437t
316t
437f
330
119
120
409
304
223
119
128
215
237t
237f
119
120
107f
107t
223
406
427
304
409
205
328
316f
316t
437f
437t
330
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
harvest
harvest
mid-season
mid-season
mid-season
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
mid-season
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
cont peanut
peanut/cotton
peanut/cotton
cont peanut
cont peanut
cont peanut
cont bahia
cont peanut
cont peanut
peanut/cotton
cont bahia
cont bahia
cont peanut
cont peanut
cont peanut
peanut/corn
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut/cotton
peanut/corn
peanut/cotton
peanut/cotton
cont bahia
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
cont peanut
peanut/cotton
cont bahia
peanut/corn
peanut/corn
cont bahia
cont bahia
cont bahia
peanut/cotton
peanut/cotton
cont peanut
cont peanut
cont bahia
peanut/corn
cont peanut
cont peanut
cont bahia
peanut/cotton
cont bahia
cont bahia
peanut/corn
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut
cotton
cotton
peanut
peanut
peanut
bahia
peanut
peanut
cotton
bahia
bahia
peanut
peanut
peanut
corn
corn
corn
peanut
peanut
peanut
cotton
cotton
corn
cotton
cotton
bahia
corn
corn
peanut
peanut
peanut
peanut
cotton
bahia
corn
corn
bahia
bahia
bahia
cotton
cotton
peanut
peanut
bahia
corn
peanut
peanut
bahia
cotton
bahia
bahia
corn
corn
corn
peanut
peanut
peanut
peanut
cotton
A23
C12
C11
A24
A21
A22
B12
A4
A5
C9
B10
B9
A8
A7
A6
D4
D5
D6
A9
A11
A10
C4
C1
D3
C2
C3
B1
D10
D11
A18
A17
A19
A20
C10
B11
D12
D9
B4
B2
B3
C8
C7
A15
A16
B7
D8
A13
A14
B8
C6
B5
B6
D7
D1
D2
A1
A2
A3
A12
C5
237f
215
128
237t
107f
107t
223
316t
107f
406
427
304
107t
437t
437f
328
409
205
237f
316f
237t
215
406
120
128
330
427
205
328
316f
437t
316t
437f
330
119
120
409
304
223
119
128
215
237t
237f
119
120
107f
107t
223
406
427
304
409
205
328
316f
316t
437f
437t
330
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
harvest
harvest
mid-season
mid-season
mid-season
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
mid-season
harvest
harvest
harvest
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
mid-season
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
pre-plant
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
cont peanut
peanut/cotton
peanut/cotton
cont peanut
cont peanut
cont peanut
cont bahia
cont peanut
cont peanut
peanut/cotton
cont bahia
cont bahia
cont peanut
cont peanut
cont peanut
peanut/corn
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut/cotton
peanut/corn
peanut/cotton
peanut/cotton
cont bahia
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
cont peanut
peanut/cotton
cont bahia
peanut/corn
peanut/corn
cont bahia
cont bahia
cont bahia
peanut/cotton
peanut/cotton
cont peanut
cont peanut
cont bahia
peanut/corn
cont peanut
cont peanut
cont bahia
peanut/cotton
cont bahia
cont bahia
peanut/corn
peanut/corn
peanut/corn
cont peanut
cont peanut
cont peanut
cont peanut
peanut/cotton
peanut
cotton
cotton
peanut
peanut
peanut
bahia
peanut
peanut
cotton
bahia
bahia
peanut
peanut
peanut
corn
corn
corn
peanut
peanut
peanut
cotton
cotton
corn
cotton
cotton
bahia
corn
corn
peanut
peanut
peanut
peanut
cotton
bahia
corn
corn
bahia
bahia
bahia
cotton
cotton
peanut
peanut
bahia
corn
peanut
peanut
bahia
cotton
bahia
bahia
corn
corn
corn
peanut
peanut
peanut
peanut
cotton
D G G E
Ba n d T a b le
10090807060504030
54
70
52
96
28
17
17
55
81
3
56
28
1
69
28
46
49
3
41
36
98
15
5
1
1
64
33
60
56
40
8
40
10
4
0
67
27
1
59
98
86
85
10
93
27
16
5
8
50
66
30
100
35
96
79
36
5
20
D G G E
67
Figure 3. Multi-dimensional Scaling of nematode communities from peanut soil samples under
various crop rotations collected at pre-plant, mid-season and harvest from the Wiregrass
Research and Extension Center in 2008:
a) Colored by rotation
b) Colored by sampling period
68
Figure 4. Multi-dimensional Scaling of nematode communities from peanut soil samples under
various crop rotations collected at pre-plant, mid-season and harvest from the Wiregrass
Research and Extension Center in 2009:
a) Colored by rotation
b) Colored by sampling period
69
Figure 5. Dendrogram construction using the unweighted pair-group method with arithmetic
mean (UPGMA) based on nematode community band table data from different peanut cropping
sequences collected at pre-plant, mid-season and harvest from the Wiregrass Research and
Extension Center in 2008. The scale represents % of similarity calculated by the Dice coefficient.
DGGE
B a n d T a b l e
10090807060504030
100
26
19
45
19
29
73
56
16
65
50
36
37
27
2
13
24
5
74
57
10
8
4
77
24
2
64
36
40
11
11
92
85
74
79
14
99
62
36
25
84
33
54
78
14
18
D1
D2
D3
A1
D4
A2
B1
D5
A3
B2
A4
A5
A6
A7
A8
B3
B4
D6
D7
B5
C1
B6
C2
C3
C4
B7
B8
D8
C5
C6
C7
C8
C9
B9
C10
C11
C12
A9
A 10
A 11
D9
B 10
D10
B 11
B 12
D11
D12
A 12
223
304
119
330
427
128
120
304
406
409
215
215
330
406
128
328
409
119
223
205
437
328
237
316
107
120
205
427
107
316
437
237
437
120
107
237
316
128
330
215
119
409
427
205
328
223
304
406
m i d- s eas on
m i d- s eas on
m i d- s eas on
harv es t
harv es t
harv es t
harv es t
harv es t
harv es t
harv es t
harv es t
m i d- s eas on
m i d- s eas on
m i d- s eas on
m i d- s eas on
m i d- s eas on
m i d- s eas on
harv es t
harv es t
harv es t
harv es t
harv es t
harv es t
harv es t
harv es t
m i d- s eas on
m i d- s eas on
m i d- s eas on
m i d- s eas on
m i d- s eas on
m i d- s eas on
m i d- s eas on
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
pr e- pl ant
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
c ont ba hi a
c ont ba hi a
c ont ba hi a
peanut/c otton
c ont ba hi a
peanut/c otton
peanut/c or n
c ont ba hi a
peanut/c otton
peanut/c or n
peanut/c otton
peanut/c otton
peanut/c otton
peanut/c otton
peanut/c otton
peanut/c or n
peanut/c or n
c ont ba hi a
c ont ba hi a
peanut/c or n
c ont pe anu t
peanut/c or n
c ont pe anu t
c ont pe anu t
c ont pe anu t
peanut/c or n
peanut/c or n
c ont ba hi a
c ont pe anu t
c ont pe anu t
c ont pe anu t
c ont pe anu t
c ont pe anu t
peanut/c or n
c ont pe anu t
c ont pe anu t
c ont pe anu t
peanut/c otton
peanut/c otton
peanut/c otton
c ont ba hi a
peanut/c or n
c ont ba hi a
peanut/c or n
peanut/c or n
c ont ba hi a
c ont ba hi a
peanut/c otton
bah i a
bah i a
bah i a
peanut
bah i a
peanut
peanut
bah i a
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bah i a
bah i a
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bah i a
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
peanut
bah i a
peanut
bah i a
peanut
peanut
bah i a
bah i a
peanut
bh/ m i d/2 .
bh/ m i d/2 .
bh/ m i d/2 .
pc t/ha r /2.
bh/ har/2.
pc t/ha r /2.
pc r /ha r /2.
bh/ har/2.
pc t/ha r /2.
pc r /ha r /2.
pc t/ha r /2.
pc t/m i d/2 .
pc t/m i d/2 .
pc t/m i d/2 .
pc t/m i d/2 .
pc r /m i d/2 .
pc r /m i d/2 .
bh/ har/2.
bh/ har/2.
pc r /ha r /2.
c p/h ar /20 .
pc r /ha r /2.
c p/h ar /20 .
c p/h ar /20 .
c p/h ar /20 .
pc r /m i d/2 .
pc r /m i d/2 .
bh/ m i d/2 .
c p/m i d/2 .
c p/m i d/2 .
c p/m i d/2 .
c p/m i d/2 .
c p/p r e/2 0.
pc r /pr e/2 .
c p/p r e/2 0.
c p/p r e/2 0.
c p/p r e/2 0.
pc t/pre/2.
pc t/pre/2.
pc t/pre/2.
bh/ pr e/2 .
pc r /pr e/2 .
bh/ pr e/2 .
pc r /pr e/2 .
pc r /pr e/2 .
bh/ pr e/2 .
bh/ pr e/2 .
pc t/pre/2.
70
Figure 6. Dendrogram construction using the unweighted pair-group method with arithmetic
mean (UPGMA) based on nematode community band table data from different peanut cropping
sequences collected at pre-plant, mid-season and harvest from the Wiregrass Research and
Extension Center in 2009. The scale represents % of similarity calculated by the Dice coefficient.
D G G E
Ba n d T a b le
1009590858075706560555045403530
49
70
55
96
28
20
15
58
85
3
54
28
1
66
27
45
46
4
39
35
98
13
4
1
1
62
31
60
57
41
9
40
8
4
1
64
23
1
57
97
86
85
11
94
28
17
5
7
52
68
30
99
36
96
77
37
4
18
A1
D1
D2
A2
A3
A4
B1
A5
A6
D3
B2
B3
A7
A8
A9
C1
C2
C3
A 1 0
A 1 1
A 1 2
D4
D5
C4
D6
D7
B4
C5
C6
A 1 3
A 1 4
A 1 5
A 1 6
D8
B5
C7
C8
B6
B7
B8
D9
D 1 0
A 1 7
A 1 8
B9
C9
A 1 9
A 2 0
B 1 0
D 1 1
B 1 1
B 1 2
C 1 0
C 1 1
C 1 2
A 2 1
A 2 2
A 2 3
A 2 4
D 1 2
237f
215
128
237t
107f
107t
223
316t
107f
406
427
304
107t
437t
437f
328
409
205
237f
316f
237t
215
406
120
128
330
427
205
328
316f
437t
316t
437f
330
119
120
409
304
223
119
128
215
237t
237f
119
120
107f
107t
223
406
427
304
409
205
328
316f
316t
437f
437t
330
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
h a r v e s t
h a r v e s t
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
h a r v e s t
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
p r e - p l a n t
p r e - p l a n t
m i d - s e a s o n
h a r v e s t
h a r v e s t
h a r v e s t
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
m i d - s e a s o n
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
p r e - p l a n t
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
c o n t p e a n u t
p e a n u t / c o t t o n
p e a n u t / c o t t o n
c o n t p e a n u t
c o n t p e a n u t
c o n t p e a n u t
c o n t b a h i a
c o n t p e a n u t
c o n t p e a n u t
p e a n u t / c o t t o n
c o n t b a h i a
c o n t b a h i a
c o n t p e a n u t
c o n t p e a n u t
c o n t p e a n u t
p e a n u t / c o r n
p e a n u t / c o r n
p e a n u t / c o r n
c o n t p e a n u t
c o n t p e a n u t
c o n t p e a n u t
p e a n u t / c o t t o n
p e a n u t / c o t t o n
p e a n u t / c o r n
p e a n u t / c o t t o n
p e a n u t / c o t t o n
c o n t b a h i a
p e a n u t / c o r n
p e a n u t / c o r n
c o n t p e a n u t
c o n t p e a n u t
c o n t p e a n u t
c o n t p e a n u t
p e a n u t / c o t t o n
c o n t b a h i a
p e a n u t / c o r n
p e a n u t / c o r n
c o n t b a h i a
c o n t b a h i a
c o n t b a h i a
p e a n u t / c o t t o n
p e a n u t / c o t t o n
c o n t p e a n u t
c o n t p e a n u t
c o n t b a h i a
p e a n u t / c o r n
c o n t p e a n u t
c o n t p e a n u t
c o n t b a h i a
p e a n u t / c o t t o n
c o n t b a h i a
c o n t b a h i a
p e a n u t / c o r n
p e a n u t / c o r n
p e a n u t / c o r n
c o n t p e a n u t
c o n t p e a n u t
c o n t p e a n u t
c o n t p e a n u t
p e a n u t / c o t t o n
peanut
c o t t o n
c o t t o n
peanut
peanut
peanut
b a h i a
peanut
peanut
c o t t o n
b a h i a
b a h i a
peanut
peanut
peanut
c o r n
c o r n
c o r n
peanut
peanut
peanut
c o t t o n
c o t t o n
c o r n
c o t t o n
c o t t o n
b a h i a
c o r n
c o r n
peanut
peanut
peanut
peanut
c o t t o n
b a h i a
c o r n
c o r n
b a h i a
b a h i a
b a h i a
c o t t o n
c o t t o n
peanut
peanut
b a h i a
c o r n
peanut
peanut
b a h i a
c o t t o n
b a h i a
b a h i a
c o r n
c o r n
c o r n
peanut
peanut
peanut
peanut
c o t t o n
c p / p r e / 2 0 0 9
p c t / p r e / 2 0 0 9
p c t / p r e / 2 0 0 9
c p / p r e / 2 0 0 9
c p / p r e / 2 0 0 9
c p / p r e / 2 0 0 9
b h / p r e / 2 0 0 9
c p / h a r / 2 0 0 9
c p / h a r / 2 0 0 9
p c t / m i d / 2 0 0 9
b h / m i d / 2 0 0 9
b h / m i d / 2 0 0 9
c p / h a r / 2 0 0 9
c p / h a r / 2 0 0 9
c p / h a r / 2 0 0 9
p c r / h a r / 2 0 0 9
p c r / h a r / 2 0 0 9
p c r / h a r / 2 0 0 9
c p / h a r / 2 0 0 9
c p / h a r / 2 0 0 9
c p / h a r / 2 0 0 9
p c t / h a r / 2 0 0 9
p c t / h a r / 2 0 0 9
p c r / h a r / 2 0 0 9
p c t / h a r / 2 0 0 9
p c t / h a r / 2 0 0 9
b h / h a r / 2 0 0 9
p c r / m i d / 2 0 0 9
p c r / m i d / 2 0 0 9
c p / m i d / 2 0 0 9
c p / m i d / 2 0 0 9
c p / m i d / 2 0 0 9
c p / m i d / 2 0 0 9
p c t / m i d / 2 0 0 9
b h / p r e / 2 0 0 9
p c r / p r e / 2 0 0 9
p c r / m i d / 2 0 0 9
b h / h a r / 2 0 0 9
b h / h a r / 2 0 0 9
b h / h a r / 2 0 0 9
p c t / m i d / 2 0 0 9
p c t / m i d / 2 0 0 9
c p / m i d / 2 0 0 9
c p / m i d / 2 0 0 9
b h / m i d / 2 0 0 9
p c r / m i d / 2 0 0 9
c p / m i d / 2 0 0 9
c p / m i d / 2 0 0 9
b h / m i d / 2 0 0 9
p c t / p r e / 2 0 0 9
b h / p r e / 2 0 0 9
b h / p r e / 2 0 0 9
p c r / p r e / 2 0 0 9
p c r / p r e / 2 0 0 9
p c r / p r e / 2 0 0 9
c p / p r e / 2 0 0 9
c p / p r e / 2 0 0 9
c p / p r e / 2 0 0 9
c p / p r e / 2 0 0 9
p c t / p r e / 2 0 0 9
71
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Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990. Basic local alignment
search tool. Journal of Molecular Biology 215:403-410.
Ampe, F., Sirvent, A., and Zakhia, N. 2001. Dynamics of the microbial community responsible
for traditional sour cassava starch fermentation studied by denaturing gradient gel
electrophoresis and quantitative rRNA hybridization. International Journal of Food
Microbiology 65:45-54.
Baird, S.M., and Bernard, E.C. 1984. Nematode population and community dynamics in
soybean-wheat cropping and tillage regimes. Journal of Nematology 16:379-386.
Bowen, K.L., Hagan, A.K., and Weeks, J.R. 1996. Soil-borne pests of peanuts in growers? fields
with different cropping histories in Alabama. Peanut Science 23:36-42.
Dickson, D.W., Oostendorp, M., Giblin-Davis, R.M., and Mitchell, D.J. 1994. Control of plant
parasitic nematodes by biological antagonists. In Pest Management of the Tropic, Biological
Control-A Florida Perspective. D. Rosen, F.D. Bennett and J.L. Capinera, eds. Intercept
LTD, U.K. Pg. 575-601.
Erickson, A.B. 1944. Helminth infection in relation to population fluctuations in snowshoe hares.
Journal of Wildlife Management 8:134-153.
Foucher, A.L., Bongers, T., Noble, L.R., and Wilson, M.J. 2004. Assessment of nematode
biodiversity using DGGE of 18S rDNA following extraction of nematodes from soil. Soil
Biology and Biochemistry 36:2027-2032.
Foucher, A. and Wilson, M. 2002. Development of a polymerase chain reaction-based
denaturing gradient gel electrophoresis technique to study nematode species biodiversity
using the 18S rDNA gene. Molecular Ecology Notes 2:45-48.
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Ingham, R.E., Trofymow, J.A., Ingham, E.R., and Coleman, D.C. 1985. Interactions of bacteria,
fungi, and their nematode grazers: effects on nutrient cycling and plant growth. Ecological
Monographs 55(1):119-140.
Jaffee, B.A., Ferris, H., Scow, K.M. 1998. Nematode-trapping fungi in organic and conventional
cropping systems. Phytopathology 88:344-350.
Jenkins, 1964. A rapid centrifugal flotation technique for separating nematodes from soil. Plant
Disease Reporter 48:692.
Kudo, N., Kuratomi, K., Hatada, N., Ikadai, H. and Oyamada, T. 2005. Further observations on
the development of Gongylonema pulchrum in rabbits. Journal of Parasitology 91:750-755.
Miller, P.M. 1957. A method for the quick separation of nematodes from soil samples. Plant
Disease Reporter 41:194.
Murray, A.E., Hollibaugh, J.T., and Orrego, C. 1996. Phylogenetic composition of
bacterioplankton from two California estuaries compared by denaturing gradient gel
electrophoresis of 16S rDNA fragments. Applied and Environmental Microbiology 62:2676-
2680.
Muyzer, G., De Waal, E.C., and Uitterlinden, A.G. 1993. Profiling of complex microbial
populations by denatured gradient gel electrophoresis analysis of polymerase chain reaction-
amplified genes coding for 16S rRNA. Applied and Environmental Microbiology 59:695-
700.
Myers, P. 2001. ?Nematoda? (On-line), Animal Diversity Web. Accessed April 29, 2010 at
http://animaldiversity.ummz.umich.edu/site/accounts/information/Nematoda.html.
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Myers, R.M., Fischer, S.G., Lerman, L.S., and Maniatis, T. 1985. Nearly all single base
substitutions in DNA fragments joined to a G-C clamp can be detected by DGGE. Nucleic
Acid Research 13:3131-3145.
Neher, D.A. 2001. Role of nematodes in soil health and their use as indicators. Journal of
Nematology 33:161-168.
Olatinwo, R., Borneman, J., and Becker, J.O. 2006. Induction of beet-cyst nematode
suppressiveness by the fungi Dactylella oviparasitica and Fusarium oxysporium in field
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Rodriguez-Kabana, R., Robertson, D.G., Weaver, C.F., and Wells, L. 1991. Rotations of
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74
Chapter IV. Influence of Nematode Community on Aflatoxin Contamination of Peanuts
Abstract
The nematode community within peanut soils, or any agricultural soils, consists of plant-parasitic
and free-living nematodes, the latter of which can be attributed to increases in plant health. The
peanut root-knot nematode can detrimentally affect peanut yields and may facilitate invasion by
aflatoxigenic fungi. The objective of this study was to determine if beneficial free-living
nematodes act to increase peanut yields or decrease aflatoxin contamination. Samples were
collected from four different cropping rotations (continuous peanut, peanut/corn, peanut/cotton,
and peanut/bahiagrass) at three sampling periods (pre-plant, mid-season, and harvest) for three
consecutive years (2007 - 2009). Nematodes were microscopically identified, after which peanut
pods were collected from each rotation for a visual examination of damage or fungi and tested
for aflatoxin contamination. Bahiagrass rotations supported higher populations of microbivore
nematodes and lower levels of aflatoxin contamination than continuous peanut monocropping.
Significant negative correlations occurred between microbivores and total aflatoxins as well as
microbivores and plant-parasitic nematodes. These results suggest that free-living nematodes
may play a role in the suppression of plant-parasitic nematodes and subsequent aflatoxin
contamination in peanuts.
75
Introduction
Peanut (Arachis hypogaea L.) is an important crop in Alabama and throughout the
southeastern United States. In 2009, 155,000 acres of peanuts were grown for a value of $104.5
million in Alabama alone, while 1.1 million acres were grown for a value of $835 million
throughout the United States (NASS, 2010). This high value crop can be detrimentally affected
by a number of soil-borne organisms, including the peanut root-knot nematode Meloidogyne
arenaria (Neil, 1889) Chitwood, 1949, race 1. The peanut root-knot nematode can reduce yields
3-15% annually (Holbrook et al., 2008). Damage from this nematode has been shown to
facilitate invasion by the aflatoxigenic fungi Aspergillus flavus Link and A. parasiticus Speare
(Timper et al., 2004). Aflatoxins, produced by the A. flavus fungal group, are highly
carcinogenic, strictly regulated to ensure a safe food supply, and can decrease the economic
return from a peanut crop (Dorner et al., 2003). Contamination of aflatoxins in peanut seeds
results in a loss of $2.6 million per year to peanut growers (Lamb and Sternitzke, 2001). There is
no highly effective control for aflatoxigenic fungi, but minimization of this problem may be
possible through a greater understanding of the microbial community that influences A. flavus
production of aflatoxins.
Management of nematodes to reduce yield loss and potential aflatoxin contamination is
generally obtained with chemical control. However, few nematicides are currently available to
treat peanut crops (ACES, 2010). Crop rotations with non-host crops are currently the main
method used to control the peanut root-knot nematode, including corn (Zea mays L.), sorghum
(Sorghum bicolor (L.) Moench), cotton (Gossypium hirsutum L.), soybean (Glycine max (L.)
Merr.), and bahiagrass (Paspalum notatum Flugge) (Rodriguez-Kabana et al, 1991; Timper et al.,
2001; Bowen et al., 1996).
76
There are some resistant peanut cultivars to the root-knot nematode, however breeding
for resistance has been slowed by the occurrence of tomato spotted wilt virus (TSWV). Since
1985, TSWV has become the most important disease problem for many growers in the southern
United States. Until recently, peanut cultivars were available with resistance to either the peanut
root-knot nematode or TSWV but not both. In 2008, the USDA released the cultivar Tifguard,
which has resistance to TSWV and the root-knot nematode (Holbrook et al., 2008). Continuous
planting of this cultivar in the same fields is likely to eventually lead to resistance-breaking
nematodes (Rich and Tillman, 2009).
To better maintain resistance in peanuts to root-knot nematodes, a greater understanding
of the nematode community and the impact it has on plant health is needed. The nematode
community consists not only of plant-parasitic nematodes but also free-living nematode
(microbivores, fungivores and predators). Free-living nematodes are commonly attributed to
increased plant growth, increased nitrogen (N) uptake by plants, decreased or increased bacterial
populations, increased CO2 evolution, increased N and phosphorous (P) mineralization, and
increased substrate utilization (Ingham et al., 1985).
This objective of the current research is to identify the populations present within the
nematode community of peanut soils under differing crop rotations to determine if nematode
populations, especially beneficial free-living nematodes act to increase peanut production. This
was accomplished by identifying nematode populations present in the peanut soils, collecting
yield data, rating peanut pods for physical damage and testing pods for the presence of aflatoxin
contamination. Nematode populations were compared to various plant health factors, including
yield and aflatoxin levels, to determine if any interactions exist that increase plant health.
77
Materials and Methods
Soil samples and nematode identification: Soil samples were obtained from the Wiregrass
Research and Extension Center in Headland, Alabama (31? 21? N, 85? 20? W) from a long term
rotation study established in 1988. A total of 34 cropping sequences have been established at this
site. The rotation sequences used in this study included: continuous peanuts, peanut/cotton,
peanut/corn, and peanut/bahiagrass (Table 1). The soil is a Dothan Sandy Loam (OM<1%).
Rotation sequences are arranged in a randomized complete block design with four replications.
Each plot is 50 ft long with 12 rows per plot and three ft between each row. Samples were
collected at pre-plant, mid-season or pegging and harvest for 3 consecutive years (2007-2009).
Seven soil cores (6 inch depth) were taken randomly across each plot from the root zone in each
replication. Samples were placed in a plastic bag, mixed thoroughly and stored at 10?C until
needed.
Nematodes were extracted from 100 cm3 sub-samples from each plot using a sieving
process followed by sugar flotation (Jenkins, 1964). Nematodes were counted and
microscopically identified to trophic level for free-living and genus level for plant parasites on a
Nikon Eclipse TS100 inverted microscope for each year of the study using the Interactive
Diagnostic Key to Plant Parasitic, Free-living and Predaceous Nematodes from the UNL
Nematology Lab.
Peanut health assessment: Peanut pods were collected after harvest from each plot (described
above) planted to peanuts for all 3 years of the study (2007-2009). After yields were determined,
150 peanut pods per plot were rated for physical damage including: small or immature pods, pod
rot, discoloration, insect scars, nematode damage, insect holes, cracks, and visible fungi.
78
The pods were then shelled by hand, ground and tested for the presence of aflatoxins.
Toxin assays were performed using High Pressure Liquid Chromatography (HPLC) methods
described by Wilson and Romer (1991) with modifications. The aflatoxins were extracted from a
50 g sample of ground peanuts from each plot. Each sample was added to 100 ml of 90%
acetonitrile and incubated for 12 hours at room temperature. The solution was then filtered and 5
ml filtrate was purified using a Mycosep Multifunctional Cleanup Column (Romer Labs, Inc.,
Washington, MO). The purified extract was added to a derivatizing solution and incubated at
55?C for 30 minutes. This purified extract was then used to determine B1, B2, G1 and G2
aflatoxin concentrations. Aflatoxin levels were recorded for each sample.
Statistical analysis: Aflatoxin levels and nematode counts were transformed in order to
normalize data and eliminate zero values. Aflatoxin levels, visual peanut pod evaluation of
physical damage ratings, nematode counts, rainfall observations, rotation sequences, and yields
were compared for a total of 3 years. Spearman?s rank correlation coefficients was calculated
using SAS version 9.1.3 (SAS Institute, Cary, NC) to determine if any correlations exist
between variables.
Results
Aflatoxin levels (B1 and total aflatoxins) in 2007 were higher in the continuous peanut plots than
in peanuts cropped after bahiagrass. The mean total aflatoxin and B1 aflatoxin content in the
peanut/bahiagrass rotation was 19.8 ppb and 2.0 ppb, respectively. The mean total aflatoxin and
B1 aflatoxin content of the continuous peanut rotation was 41.8 ppb and 2.8 ppb, respectively.
79
There were no aflatoxins detected in 2008 or 2009 for any of the cropping sequences planted to
peanut.
Microbivore nematode populations were significantly affected by crop rotation at pre-
plant 2007, harvest 2008 and mid-season 2009 (Fig 1). Microbivore nematode populations were
similar for peanut/bahiagrass and peanut/corn rotations and significantly higher in continuous
peanut and peanut/cotton rotations at pre-plant 2007. In 2008, at the harvest sampling period,
peanut/bahiagrass plots supported significantly higher populations of microbivores than all other
rotations. Peanut/bahiagrass, peanut/cotton and peanut /corn rotations supported higher
populations of microbivore nematodes than the continuous peanut rotation in 2009 at mid-
season.
Total plant parasitic populations were also significantly affected by crop rotation based
on samples collected at pre-plant 2008, pre-plant 2009 and mid-season 2009 (Fig 2). The
continuous peanut and peanut/bahiagrass rotations (which where both planted to peanut the
previous year) supported similar and significantly higher levels of plant-parasitic nematodes than
the peanut/corn and peanut/cotton rotations (which were both previously planted to cotton) at
pre-plant 2008. Pre-plant 2009 total plant-parasitic nematode populations for the continuous
peanut rotation were higher than all other rotations. In 2009, at mid-season, total plant-parasitic
nematodes in the continuous peanut and peanut/corn rotation were significantly higher than the
peanut/bahiagrass and peanut/cotton rotation.
Spearman?s rank correlation coefficient revealed a significant positive correlation
between total free-living nematode populations and microbivore nematodes at each sampling
period (pre-plant, mid-season and harvest) for each year correlations were calculated (2007-
2009) (Tables 2-10). Microbivore nematode populations were also negatively correlated to root-
80
knot nematodes at pre-plant and mid-season in 2007. Furthermore, microbivore nematodes were
negatively correlated to G2 aflatoxin levels at pre-plant and mid season 2007 and negatively
correlated to total aflatoxins at mid-season 2007. A positive correlation also occurred between
root-knot nematodes and G2 aflatoxin levels at pre-plant and mid-season. Other factors that
affected aflatoxin contamination included positive correlations between insect holes in pods and
B1 aflatoxins, and total plant-parasitic nematodes at pre-plant on B2 aflatoxins.
Nematode damage to pods was positively correlated to discolored pods in 2007 and 2008,
and positively correlated with visible fungi on pods in 2007. Visible fungi on pods were also
positively correlated to cracked pods and pod rot in 2009, whereas pod rot was positively
correlated to cracked pods in 2009. A negative correlation occurred between discolored pods and
yield in 2007. Microbivore nematode populations, at harvest 2007, were positively and
significantly correlated to total plant parasitic nematodes indicating that nematode populations
late in the growing season may increase to a level at which they do not adversely affect each
other. In 2007, at pre-plant, fungivore nematodes were negatively correlated to immature pods
and total plant-parasitic nematodes were positively correlated to pod rot. In 2008 and 2009,
fungivore nematodes were positively correlated to total free-living populations and negatively
correlated to visible fungi on pods at harvest. Yield was negatively correlated to total plant-
parasites at pre-plant and lesion nematodes at harvest in 2008. In 2009, lesion nematodes were
negatively correlated to total free-living nematode populations at pre-plant.
Discussion
Aflatoxin contamination was only present in 2007 during the course of this study. Hill et al.,
1983, reported that aflatoxins are more likely to be produced when environmental conditions are
81
hot and dry, three to six weeks prior to peanut maturity. In 2007, drought conditions were present
with a total rainfall level during the growing season of approximately 17 inches, while rainfall
levels in the last six weeks of the growing season totaled approximately 4.51 inches. In 2008
drought conditions were only present in the beginning of the season. Total rainfall levels during
the 2008 growing season were 19.5 inches, although rainfall levels in the last six weeks of the
growing season were 8.78 inches. In 2009 drought conditions were not present and the total
rainfall level during the growing season totaled approximately 29 inches. In 2007, only two
cropping rotations were planted to peanuts, continuous peanut and peanut/bahiagrass. B1 and
total aflatoxin levels were lower in the peanut/bahiagrass rotation. No inference could be made
about the peanut/cotton and peanut/corn rotations and their ability to suppress aflatoxin
contamination.
Relationships were observed between microbivore nematode populations and rotation
sequence. The data suggests that bahiagrass planted in rotation with peanuts supported a higher
population level of microbivore nematodes except following the year when peanuts were
planted, than continuously planted peanuts. Bahiagrass rotations might have contributed to soil
organic matter thereby increasing the food source and the population levels of microbivorous
nematodes. Also, a relationship was discovered between plant-parasitic nematode populations
and crop rotation. Continuously cropped peanut monocultures resulted in higher levels of plant
parasitic nematode populations than the bahiagrass rotation except in the year when bahiagrass
plots were planted to peanuts. It has been well documented that peanut monoculture increases M.
arenaria populations, which are the main nematode parasites of peanuts, and decreases yields
(Katsvairo et al., 2007; Rodriguez-Kabana et al., 1991; Bowen et al., 1996).
82
Visual evaluation of physical damage to peanut pods and microscopic identification of
nematodes revealed some interesting correlations. Negative correlations occurred between
microbivore nematode populations and aflatoxin contamination including G2 and total aflatoxins,
although these correlations were observed at pre-plant and mid-season. This suggests that
nematode populations found earlier in the growing season have the most influence on aflatoxin
contamination. In addition aflatoxin contamination was influenced by root-knot nematodes, total
plant-parasitic nematode populations and insect damage to pods. Nematode damage to pods was
also correlated to visible fungi on pods, although visible fungi on pods were not correlated with
aflatoxin levels. Timper et al., 2004, reported that aflatoxins occurred more frequently in pods
that had more nematode damage. It was believed that nematode damage to pods may have
provided a site where A. flavus could enter the pod and subsequently lead to aflatoxin
contamination. Our results indicate that a combination of factors may play a role in aflatoxin
contamination including nematode damage to pods, insect holes or any other form of damage
leading to entry points for the fungus during periods of drought.
Negative correlations occurred between free-living nematodes (microbivores and
fungivores) and plant parasitic nematodes including root-knot nematodes. This suggests that
higher levels of free-living nematode populations could lead to suppression of herbivore
populations. This relationship may be due to an increase in plant health free-living nematodes are
commonly attributed to, helping the plant tolerate nematode infection.
The results of this study indicate that free-living nematodes tend to have a negative effect
on plant parasitic nematode populations. Decreases in B1 and total aflatoxin levels were observed
when peanut was cropped following several years of bahiagrass compared to continuously
cropped peanuts. Further testing is needed to determine the effects of nematode populations in
83
peanut/cotton and peanut/corn rotations and confirm the effects of bahiagrass and continuous
peanut rotations on nematode populations in years when environmental factors are conducive for
aflatoxin contamination. Overall, when considering crops to plant in succession with peanuts to
maintain crop health bahiagrass is preferable to peanut monocropping. Bahiagrass rotations in
peanut fields increase microbivore nematode populations, which may in turn decrease aflatoxin
levels.
84
Table 1. Year-wise cropping pattern in different peanut rotations sampled for this study at
Wiregrass Research and Extension Center.
Crop rotation 2006 2007 2008 2009
Continuous peanut
(P-P-P-P)
Peanut Peanut Peanut Peanut
Peanut/bahiagrass
(B-P-B-B)
Bahiagrass Peanut Bahiagrass Bahiagrass
Peanut/cotton
(P-Ct-P-Ct)
Peanut Cotton Peanut Cotton
Peanut/corn
(Cr-Ct-P-Cr)
Corn Cotton Peanut Corn
85
Table 2. Spearman rank correlation coefficients calculated among nematode populations observed at pre-plant, aflatoxin levels
detected in peanuts, yield, and visual peanut evaluations for pod damage in 2007 under various peanut rotations in Headland, AL.
Total free-
living
Root-knot Spiral G2
Aflatoxins
B1
Aflatoxins
B2
Aflatoxins
Total
Aflatoxins
Yield Immature
pods
Pod rot Nematode
damage to
pods
Microbivores r=0.98802
p=<0.0001
r= -0.72380
p=0.0424
r=0.76509
p=0.0270
r= -0.91146
p=0.0016
- - - - - - -
Fungivores - - - - - - - - r= -0.76509
p=0.0270
- -
Total free-
living
- r= -0.72790
p=0.0406
r=0.75593
p=0.0300
r= -0.88786
p=0.0032
- - r= -0.70660
p=0.05
- - - -
Root-knot r= -0.72790
p=0.0406
- - r=0.74832
p=0.0327
- - - - - - -
Total plant-
parasites
- - - - - r= -0.86603
p=0.0054
- - - r=0.77442
p=0.0241
-
G1 Aflatoxins - - - - - - r=0.76835
p=0.0259
- - - -
Discolored
pods
- - - - - - - r= -0.71429
p=0.0465
- - r=0.90476
p=0.0020
Insect holes in
pods
- - - - r=0.72405
p=0.0423
- - - - - -
Visible fungi
on pods
- - - - - - - - r= -0.80608
p=0.0157
- r=0.73055
p=0.0396
- Correlation not significant at P <0.05.
86
Table 3. Spearman rank correlation coefficients calculated among nematode populations observed at mid-season, aflatoxin levels
detected in peanuts, yield, and visual peanut evaluations for pod damage in 2007 under various peanut rotations in Headland, AL.
Total free-
living
Root-knot G2
Aflatoxins
B1
Aflatoxins
B2
Aflatoxins
Total
Aflatoxins
Yield Insect holes
in pods
Immature
pods
Nematode
damage to
pods
Microbivores r=0.98795
p=<0.0001
r= -0.76087
p=0.0283
r= -0.75921
p=0.0289
- - r= -0.96386
p=0.0001
- - - -
Total free-living - - r= -0.83577
p=0.0098
- - r= -0.95181
p=0.0003
- - - -
Root-knot r= -0.83450
p=0.0100
- r=0.85779
p=0.0064
- - - - r= -0.79768
p=0.0177
- -
Ring - - - - - - r=0.76980
p=0.0255
- - -
Total plant-
parasitic
- - - - r= -0.92778
p=0.0009
- - - - -
G1 Aflatoxins - - - - - r=0.76835
p=0.0259
- - - -
Discolored pods - - - - - - r= -0.71429
p=0.0465
- - r=0.90476
p=0.0020
Insect holes in
pods
- - - r=0.72405
p=0.0423
- - - - - -
Visible fungi on
pods
- - - - - - - - r= -0.80608
p=0.0157
r=0.73055
p=0.0396
- Correlation not significant at P <0.05.
87
Table 4. Spearman rank correlation coefficients calculated among nematode populations observed at harvest, aflatoxin levels detected
in peanuts, yield, and visual peanut evaluations for pod damage in 2007 under various peanut rotations in Headland, AL.
Total free-
living
Fungivores Predators Ring Total plant-
parasites
Total
Aflatoxins
Discolored
pods
Insect holes
in pods
Visible
fungi on
pods
Insect scars
on pods
Microbivores r=0.87831
p=0.0041
- - r=0.93541
p=0.0006
r=0.72500
p=0.0419
- - - - -
Total free-
living
- - - r=0.73030
p=0.0397
- - - - - -
Total plant-
parasites
- - - r=0.78842
p=0.0201
- r= -0.72405
p=0.0423
- - - -
Lesion - - r=0.75593
p=0.0300
- - - - - - -
Root-knot r=0.85192
p=0.0072
- - - - - - - - -
Stunt - r=0.71714
p=0.0453
- r=0.71714
p=0.0453
- - - - - -
Yield - - - r=0.73030
p=0.0397
r=0.85391
p=0.0070
- r= -0.71429
p=0.0465
- - -
G1 Aflatoxins - - - - - r=0.76835
p=0.0259
- - - r= -0.66643
p=0.0711
B1 Aflatoxins - - - - - - - r=0.72405
p=0.0423
- -
Immature
pods
- - - - - - - - r= -0.80608
p=0.0157
-
Nematode
damage to
pods
- - - - - - r=0.90476
p=0.0020
- r=0.73055
p=0.0396
-
- Correlation not significant at P <0.05.
88
Table 5. Spearman rank correlation coefficients calculated among nematode populations observed at pre-plant, yield and visual peanut
evaluations for pod damage in 2008 under various peanut rotations in Headland, AL.
Total free-living Total plant-
parasite
Immature pods Discolored pods Cracks in pods
Microbivores r=0.96085
p=<0.0001
r=0.67204
p=0.0167
- - -
Root-knot - r=0.81942
p=0.0011
- - -
Reniform - - r=0.66058
p=0.0194
- -
Yield - r= -0.61231
p=0.0343
- - -
Insect scars on pods - - r= -0.59716
p=0.0403
- -
Cracks in pods - - r= -0.65368
p=0.0211
r=0.68366
p=0.0142
-
Nematode damage to
pods
- - - r=0.88908
p=0.0001
r=0.76802
p=0.0035
- Correlation not significant at P <0.05.
89
Table 6. Spearman rank correlation coefficients calculated among nematode populations observed at mid-season, yield and visual
peanut evaluations for pod damage in 2008 under various peanut rotations in Headland, AL.
Total free-
living
Total plant-
parasites
Lesion Reniform Spiral Immature
pods
Visible fungi
on pods
Discolored
pods
Cracks in
pods
Microbivores r=0.95775
p=<0.0001
- - - - - - - -
Fungivores r=0.61379
p=0.0338
- - - - - - - -
Ring - - - - r=0.67420
p=0.0162
- r= -0.64775
p=0.0228
- -
Root-knot - r=0.70438
p=0.0105
- r=0.62253
p=0.0306
- - - - -
Stubbyroot - - r=0.57735
p=0.0493
- - - - - -
Stunt - r=0.64826
p=0.0226
- - - - - - -
Insect scars on
pods
- - - - - r= -0.59716
p=0.0403
- - -
Cracks in pods - - - - - r= -0.65368
p=0.0211
- r=0.68366
p=0.0142
-
Nematode
damage to pods
- - - - - - - r=0.88908
p=0.0001
r=0.76802
p=0.0035
- Correlation not significant at P <0.05.
90
Table 7. Spearman rank correlation coefficients calculated among nematode populations observed at harvest, yield and visual peanut
evaluations for pod damage in 2008 under various peanut rotations in Headland, AL.
Total free-
living
Total plant-
parasites
Lesion Stunt Immature pods Visible fungi on
pods
Discolored pods Cracks in pods
Microbivores r=0.92933
p=<0.0001
- r=0.59882
p=0.0397
- - - - -
Fungivores r=0.69113
p=0.0128
- - - - r=0.61043
p=0.0350
- -
Reniform - - r=0.58596
p=0.0453
r=0.62313
p=0.0304
- - - -
Root-knot - r=0.86620
p=0.0003
- - - - - -
Ring - - - r=0.68442
p=0.0141
r=0.65057
p=0.0220
- - -
Yield - - r= -0.61461
p=0.0335
- - - - -
Insect scars on
pods
- - - - r= -0.59716
p=0.0403
- - -
Cracks in pods - - - - r= -0.65368
p=0.0211
- r=0.68366
p=0.0142
-
Nematode
damage to pods
- - - - - - r=0.88908
p=0.0001
r=0.76802
p=0.0035
- Correlation not significant at P <0.05.
91
Table 8. Spearman rank correlation coefficients calculated among nematode populations observed at pre-plant, yield and visual peanut
evaluations for pod damage in 2009 under various peanut rotations in Headland, AL.
Total free-
living
Fungivores Reniform Root-knot Yield Pod rot Discolored
pods
Cracks in pods
Microbivores r=0.91566
p=0.0014
- - - - - - -
Lesion r= -0.76047
p=0.0285
- - - - - - -
Spiral - - r=1.00000
p=<0.0001
- - - - -
Total plant
parasites
- - - - - r= -0.72123
p=0.0435
- -
Immature pods - - - - r=0.71199
p=0.0476
- r= -0.70820
p=0.0493
-
Pod rot - - - - - - - r=0.95759
p=0.0002
Visible fungi on
pods
- r=0.75955
p=0.0288
- - - r=0.79768
p=0.0177
- r=0.82722
p=0.0113
- Correlation not significant at P <0.05.
92
Table 9. Spearman rank correlation coefficients calculated among nematode populations observed at mid-season, yield and visual
peanut evaluations for pod damage in 2009 under various peanut rotations in Headland, AL.
Total free-
living
Predators Root-knot Yield Pod rot Discolored
pods
Cracks in pods
Microbivores r=0.89822
p=0.0024
- - - - - -
Fungivores - r=0.79286
p=0.0189
- - - - -
Total plant-
parasites
- - r=0.80013
p=0.0171
- - - -
Immature pods - - - r=0.71199
p=0.0476
- r= -0.70820
p=0.0493
-
Pod rot - - - - - - r=0.95759
p=0.0002
Visible fungi on
pods
- - - - r=0.79768
p=0.0177
- r=0.82722
p=0.0113
- Correlation not significant at P <0.05.
93
Table 10. Spearman rank correlation coefficients calculated among nematode populations observed at harvest, yield and visual peanut
evaluations for pod damage in 2009 under various peanut rotations in Headland, AL.
Total free-
living
Root-knot Yield Pod rot Discolored
pods
Cracks in pods
Microbivores r=0.93415
p=0.0007
- - - - -
Fungivores r=0.77801
p=0.0230
- - - - -
Predators - - r= -0.91287
p=0.0015
- - -
Dagger - r= -0.87149
p=0.0048
- - - -
Immature pods - - r=0.71199
p=0.0476
- r= -0.70820
p=0.0493
-
Pod rot - - - - - r=0.95759
p=0.0002
Visible fungi on
pods
- - - r=0.79768
p=0.0177
- r=0.82722
p=0.0113
- Correlation not significant at P <0.05.
94
Figure 1. Mean microbivore nematode counts observed under various peanut cropping rotations
from the Wiregrass Research and Extension Center sampled at:
0
50
100
150
200
250
300
350
c o
n t
p
e a
n
u t
p
e a
n
u
t /
b
a
h i
a
p
e a
n
u
t /
c o
t t
o
n
p
e a
n
u
t /
c o
r n
0
100
200
300
400
500
600
700
cont
peanut
pe
anut
/b
ahi
a
pe
anut
/cott
on
pe
anut
/cor
n
0
100
200
300
400
500
600
700
800
900
cont
peanut
pe
anut
/b
ahi
a
pe
anut
/cott
on
pe
anut
/cor
n
Columns with the same letter are not significantly different at P ?0.05 according to Fisher?s LSD
Test.
b
a
b
a) Pre-plant 2007 b) Harvest 2008
c) Mid-season 2009
b
a
b b b
a
b
a a a a
95
Figure 2. Mean total plant-parasitic nematode counts observed under various peanut cropping
rotations from the Wiregrass Research and Extension Center sampled at:
0
20
40
60
80
100
120
140
cont
peanut
pe
anut
/b
ahi
a
pe
anut
/cott
on
pe
anut
/cor
n
0
20
40
60
80
100
120
140
160
cont
peanut
pe
anut
/b
ahi
a
pe
anut
/cott
on
pe
anut
/cor
n
0
50
100
150
200
250
cont
peanut
pe
anut
/b
ahi
a
pe
anut
/cott
on
pe
anut
/cor
n
Columns with the same letter are not significantly different at P ?0.05 according to Fisher?s LSD
Test.
a
a
b b
a) Pre-plant 2008 b) Pre-plant 2009
c) Mid-season 2009
a
b b
b
a
b b
a
96
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Alabama Cooperative Extension System (ACES). 2010. Alabama pest management handbook
volume 1. Circular ANR 500, Auburn University, Auburn, Alabama.
Bowen, K.L., Hagan, A.K., and Weeks, J.R. 1996. Soil-borne pests of peanuts in growers? fields
with different cropping histories in Alabama. Peanut Science 23:36-42.
Dorner, J.W., Cole, R.J., Connick, W.J., Daigle, D.J., McGuire, M.R., and Shasha, B.S. 2003.
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Hill, R.A., Blankenship, P.D., Cole, R.J., and Sanders, T.H. 1983. Effects of soil moisture and
temperature on preharvest invasion of peanuts by the Aspergillus flavus group and
subsequent aflatoxin development. Applied and Environmental Microbiology 45:628-633.
Holbrook, C.C., Timper, P., Dong, W., Kvien, C.K., and Culbreath, A.K. 2008. Development of
near-isogenic peanut lines with and without resistance to the peanut root-knot nematode.
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Ingham, R.E., Trofymow, J.A., Ingham, E.R., and Coleman, D.C. 1985. Interactions of bacteria,
fungi, and their nematode grazers: effects on nutrient cycling and plant growth. Ecological
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Jenkins, 1964. A rapid centrifugal flotation technique for separating nematodes from soil. Plant
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Katsvairo, T.W., Wright, D.L., Marois, J.J., Hartzog, D.L., Balkcom, K.B., Wiatrak, P.P., and
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99:1245-1251.
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Lamb, M.C. and Sternitzke, D.A. 2001. Cost of aflatoxin to the farmer, buying point, and sheller
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Rich, J. and Tillman, B. 2009. Root-knot nematode resistance in peanut. Circular ENY-057,
Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences,
University of Florida, Gainesville, FL.
Rodriguez-Kabana, R., Robertson, D.G., Weaver, C.F., and Wells, L. 1991. Rotations of
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Timper, P., Minton, N.A., Johnson, A.W., Brenneman, T.B., Culbreath, A.K., Burton, G.W.,
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Timper, P., Wilson, D.M., Holbrook, C.C., and Maw, B.W. 2004. Relationship between
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98
Summary
A Denaturing Gradient Gel Electrophoresis (DGGE) technique was adapted for
identifying nematode populations and monitoring shifts in those populations. Nematode
consensus primers were evaluated for specificity based on a DNA collection containing a
wide range of nematode trophic groups and non-target fungal organisms. The amplified 18S
rDNA from all species in the DNA collection was confirmed indicating that the nematode
consensus primers may be universal to all eukaryotic organisms and not specific to
nematodes. To ensure other non-target eukaryotic organisms were not amplified from soil
samples, nematodes were extracted from the soil prior to extracting DNA from the
nematodes for the remainder of this study. DGGE successfully separated all nematodes in the
DNA collection at the generic level indicating this molecular fingerprinting technique is
sensitive enough to separate nematode populations in soil samples. This genetic profiling
technique was then applied to peanut soil samples to determine if individual nematode
populations can be identified and if profiles can reveal individual banding patterns for
samples under different rotations. Through a band matching analysis, 37 different band
classes were observed, although only 17 bands were recovered and sequenced. High
background fluorescence inhibited recovery of DNA from weak bands. These results
demonstrate that the populations with the highest level of DNA can successfully be
recovered and putatively identified. The genetic profile also revealed similarities between
replications of the same crop rotation indicating that rotation causes a shift in nematode
populations.
99
Nematode community structure was evaluated using the DGGE technique adapted for
identifying nematode populations and monitoring shifts in those populations in combination
with DNA recovery from genetic profiles followed by sequence identification. Important
peanut cropping sequences in the southeastern United States were chosen for this study
including: continuous peanuts, continuous bahiagrass, peanut/corn, and peanut/cotton.
Nematode DGGE profiles indicated that up to 68% similarities were observed among the
replicated plots of the same peanut cropping sequences. Although these results were not
consistent among all rotations and sampling periods, similarities could be the result of plant
species effect on nematode communities. Results show a wide range of nematode community
polymorphisms were present irrespective of crop rotation indicating the impact of cropping
sequence on nematode diversity was minimal. Multi-dimensional scaling of DGGE profiles
indicated closer clustering or less scattering among nematode communities with respect to
sampling period rather than cropping sequence. Since the sampling periods were set at
prescribed times through the growing season based on crop age, the specific crop age and
environmental factors could be playing an important role in the nematode community rather
than plant species.
Nematode DNA was recovered from genetic profiles by excising bands, re-
amplifying the DNA and sequencing. Results from DNA sequencing revealed that free-living
nematodes accounted for the majority of populations present in the nematode community.
Plant-parasitic nematodes, animal parasitic nematodes, entomopathogenic nematodes, and
nematophagus fungi were also present in the plots sampled in much lower proportions. Only
41% of the sequences were identified to species level with a maximum identity of 97-100%
based on those in the nucleotide collection of the GenBank database, which only contains
100
approximately 20,000 nematode 18S sequences. The more nematode sequences identified
and deposited in complied databases, the more precise sequence matching will become.
There were 29 nematode genera and three fungal genera putatively identified, which may be
an underestimation of the biodiversity. DGGE techniques only display populations that make
up 1% or more of the total community. It is possible that more genera were present but were
omitted because they represented <1% of the total nematode biomass.
Aflatoxins were only present in one year of this three year study. Results from this
year (2007) showed that planting peanut following several years of bahiagrass significantly
reduced B1 and total aflatoxin levels compared to continuously planted peanuts. No inference
could be made about the peanut/cotton and peanut/corn rotations because they were not
planted to peanut the year aflatoxins were present. Microbivore nematode populations were
negatively correlated to aflatoxin contamination. A negative correlation also occurred
between free-living nematode populations and plant-parasitic nematodes. Bahiagrass
rotations supported significantly higher levels of microbivore nematodes than did
continuously planted peanuts, while continuously planted peanuts supported significantly
higher levels of plant-parasitic nematodes than peanuts planted in rotation with bahiagrass.
These results suggest that microbivore nematodes may suppress plant-parasitic nematodes,
possibly through increases in plant health, and may play an important role in the suppression
of aflatoxin contamination in peanuts. In order to increase microbivore populations, peanuts
can be planted in rotation with bahiagrass.
101
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