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APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Oct. 2007, p. 5982–5989
0099-2240/07/$08.00ϩ0 doi:10.1128/AEM.00709-07Copyright 2007, American Society for Microbiology. All Rights Reserved.
Influence of Antibiotic Selection on Genetic Composition of
Populations from Conventional and
Seth T. Walk,1 Janice M. Mladonicky,1 Jaclyn A. Middleton,1 Anthony J. Heidt,1
Julie R. Cunningham,1 Paul Bartlett,2 Kenji Sato,3 and Thomas S. Whittam1*
Microbial Evolution Laboratory, National Food Safety and Toxicology Center, Michigan State University, East Lansing, Michigan
1; Department of Large Animal Clinical Sciences, National Food Safety and Toxicology Center, Michigan State University,
East Lansing, Michigan 48824
2; and Department of Veterinary Sciences, University of Wyoming, Laramie, Wyoming 82070
Received 28 March 2007/Accepted 30 July 2007
The widespread agricultural use of antimicrobials has long been considered a crucial influence on the
prevalence of resistant genes and bacterial strains. It has been suggested that antibiotic applications in
agricultural settings are a driving force for the development of antimicrobial resistance, and epidemiologic
evidence supports the view that there is a direct link between resistant human pathogens, retail produce, farm
animals, and farm environments. Despite such concerns, little is understood about the population processes
underlying the emergence and spread of antibiotic resistance and the reversibility of resistance when antibiotic
selective pressure is removed. In this study, hierarchical log-linear modeling was used to assess the association
between farm type (conventional versus organic), age of cattle (calf versus cow), bacterial phenotype (resistant
versus susceptible), and the genetic composition of Escherichia coli populations (E. coli Reference Collection
[ECOR] phylogroup A, B1, B2, or D) among 678 susceptible and resistant strains from a previously published
study of 60 matched dairy farms (30 conventional and 30 organic) in Wisconsin. The analysis provides evidence
for clonal resistance (ampicillin resistance) and genetic hitchhiking (tetracycline resistance [Tetr]), estimated
the rate of compositional change from conventional farming to organic farming (mean, 8 years; range, 3 to 15
years), and discovered a significant association between low multidrug resistance, organic farms, and strains
of the numerically dominant phylogroup B1. These data suggest that organic farming practices not only change
the frequency of resistant strains but also impact the overall population genetic composition of the resident E.
coli flora. In addition, the results support the hypothesis that the current prevalence of Tetr loci on dairy farms
has little to do with the use of this antibiotic.
is an indicator species for a variety of an-
tion, it is hypothesized that deleterious effects on fitness are
thropogenic effects on microbial populations, such as the
compensated by changes elsewhere in the genome (21, 24, 25,
emergence and spread of antibiotic resistance in agriculture (1,
32, 33, 37). The occurrence of such compensatory fitness mu-
5–10, 14, 20, 30, 35, 36, 38, 39). Although most strains are
tations makes it difficult to determine whether the abundance
commensal bacteria and nonpathogenic to humans and ani-
and distribution of resistant strains are results of direct selec-
mals, there are well-recognized pathogenic strains that can
tion on the original mutation that caused resistance, selection
cause a variety of human and zoonotic diseases, and some
on compensatory changes, or other ecological factors that limit
commensal populations are known to carry high levels of an-
population diversity (environmental selection, bottlenecks, ge-
tibiotic resistance (4, 28, 29). Such resistant populations pose a
netic drift, etc.). There is sound evidence that antibiotic use
public and veterinary health risk because of the potential trans-
increases the abundance of resistant phenotypes (34), but it is
fer of genetic resistance determinants to pathogens. In addi-
not clear if the cessation of antibiotic use will decrease abun-
tion, certain virulence factors may be mobilized on genetic
dance after compensatory changes have occurred (2, 22). In
elements and transferred to normally commensal but antibiotic-
addition, resistance loci may be genetically linked to loci under
resistant strains via horizontal exchange (31, 42, 43).
strong selection and be carried at a high frequency in the
During antibiotic selection in the laboratory, resistance-con-
ferring mutations often have measurable deleterious effects
Antibiotic use in dairy cattle provides an ideal opportunity to
(i.e., a resistance cost) due to a reduction in the function of
assess the role of natural selection in bacterial populations for
genes in which resistance mutations arise. In order to maintain
several reasons. The source of the antibiotic selective pressure
a competitive advantage over other members of the popula-
is known, and the dosage is often recorded. The commongenetic determinants for certain resistant phenotypes havebeen characterized, and high-throughput assays are available
* Corresponding author. Mailing address: Microbial Evolution Lab-
oratory, 165 Food Safety and Toxicology Building, Michigan State
for their identification. Hypotheses generated under labora-
University, East Lansing, MI 48824. Phone: (517) 432-3100, ext. 178.
tory conditions can be tested in vivo by comparing bacteria
Fax: (517) 432-2310. E-mail: firstname.lastname@example.org.
from farms that regularly use antibiotics (conventional) and
† Supplemental material for this article may be found at http://aem
bacteria from farms that rarely use antibiotics (organic) (34).
ᰔ Published ahead of print on 17 August 2007.
Finally, a number of studies have previously characterized re-
GENETIC COMPOSITION OF E. COLI
POPULATIONS ON FARMS
sistance dynamics on both farm types and have identified vari-
A separate PCR was run with primers targeting the TspE4.C2 anonymous DNA
ables that significantly influence the abundance of resistant
locus using published conditions (12).
Resistance loci and class 1 integron PCR.
Ampr and Tetr strains were
phenotypes (1, 5, 7, 14, 30, 34–36).
screened for the presence of six previously identified resistance loci. A multiplex
The purpose of the present study was to assess the influence
PCR was used to detect the presence of bla
SHV, and bla
OXA-1 in Ampr
of antibiotic selection on the genetic composition of E
strains by the method of Colom et al. (13). Fragments of the tetA
, and tetC
populations from conventional and organic dairy farms. First,
genes were targeted in Tetr strains using the primers and conditions published byBoerlin et al. (9). Three primer sets were used to determine the presence of class
we used a PCR-based assay (12) to quantify the abundance and
1 integrons in the resistant strains. Primer sets targeting the class 1 integrase
distribution of four phylogenetic groups in populations cul-
, the conserved region cassette regions A and B, and the quaternary
tured during a longitudinal sampling of cattle from matched
ammonium compound resistance gene qacE
are given along with the reaction
conventional and organic dairy farms in Wisconsin (34). We
conditions in reference 23. Integron presence was defined as amplification of all
then assessed the pattern of statistical dependence for farm
Strains were categorized for analysis as follows: F
type (conventional versus organic), age of cattle (cows versus
type (conventional versus organic), A
for age of cattle (calf versus cow), D
calves), bacterial phenotype (resistant versus susceptible), and
resistant phenotype (resistant versus susceptible) or drug susceptibility level
bacterial genetic composition (E
(high or medium versus low), and E
for ECOR group (A, B1, B2, and D). The
[ECOR] groups A, B1, B2, and D) using hierarchical log-linear
numbers of strains in each category were recorded in the cells of contingencytables. Hierarchical log-linear modeling with nested effects was used to assess
dependent associations using the CATMOD procedure and SAS statistical soft-ware (SAS Institute, Cary, NC). Nonsignificant, higher-order interactions wereremoved until the most parsimonious model was found based on the likelihood
MATERIALS AND METHODS
ratio chi-square statistic for testing goodness of fit (G
2). Nonsignificant G
2 valuesindicated that the fit model was not significantly different from the saturated
E. coli strain collection.
A total of 678 E
strains (367 random susceptible
model. Odds ratios were calculated based on parameter estimates from the most
and 311 resistant strains) were assembled from a collection of 1,121 strains of a
longitudinal sampling of 10 randomly selected cows and calves from a matched
We chose to use log-linear modeling because the categorical variables in the
set of 30 conventional and 30 organic dairy farms in Wisconsin (34). Briefly, a
analysis were observed simultaneously and also because no assumptions or dis-
cluster of organic farms was selected, and the geographically closest conventional
tinctions needed to be made about whether variables were response or explan-
farm was selected for purposes of comparison so as to minimize the effects of
atory (40). This methodology is different from other approaches, like the chi-
distance (cline effects). All organic farms were certified by a USDA-accredited
square test, in that it tests the strength of associations between categorical
certification agency as not having treated adult cows with an antibiotic for at least
(Poisson distributed) variables and not significance. There are no assumptions
3 years (mean, 8 years; range, 3 to 15 years) prior to this study. More information
that the dependent and independent variables be linearly related or that the
about these farms is available (34).
relationship between variables be the same along the entire range (homoscedas-
In the original study (34), fecal samples were taken from five lactating cows
ticity). All variables were assumed to be independent. The expected counts in
and five calves (Ͻ6 months of age) at each of two visits (once in March and once
each cell of the contingency tables were above the rule-of-thumb cutoff of Ն1 and
in September) and conducted with aseptic technique. Laboratory isolation was
no more than 20% of cells Ͻ5. In addition, residuals were small (standardized
begun within 72 h, and a single E
colony was isolated from each fecal sample
residuals of Ͻ1.96) and were normally distributed.
so as to exclude any single farm or within-animal bias. All isolates were con-
Higher-order (three-way) interactions for multidrug-resistant phenotypes
firmed by standard biochemical assays. MICs of 17 antibiotics were determined
were visualized in mosaic displays for multiway contingency tables (15), which
for each strain as recommended by the CLSI (formerly NCCLS) (26) using a
were obtained online at http://euclid.psych.yorku.ca/cgi/mosaics. The original
commercially available semiautomatic broth microdilution test (Sensititre; Trek
plots were redrawn and shaded with respect to the significant (␣ ϭ 0.05) asso-
Diagnostic Systems Inc., Cleveland, OH) and appropriate quality control strains.
These antibiotics included ampicillin, amoxicillin-clavulanic acid, cephalothin,cefoxitin, ceftiofur, ceftriaxone, streptomycin, kanamycin, gentamicin, apramy-cin, amikacin, tetracycline, sulfamethoxazole, trimethoprim-sulfamethoxazole,
nalidixic acid, and ciprofloxacin. Ampicillin resistance (Ampr) and tetracyclineresistance (Tetr) phenotypes were confirmed by the presence of overnight growth
Overall abundance of E. coli phylogroups.
on LB broth (Lennox; Becton, Dickinson, and Company, Sparks, MD) agar
to all four ECOR phylogroups were identified (Fig. 1) among
containing antibiotic at the CLSI cutoff concentrations (32 g/ml and 16 g/ml,
the 678 E
strains from calves and cows on dairy farms. The
respectively). More details about the strain collection and isolation procedures
relative phylogroup composition of these bacterial populations
was used to compare different patterns of antibiotic resistance.
ECOR phylogrouping by multiplex PCR.
Strains were grouped into one of
four phylogenetic lineages (A, B1, B2, or D) based on methods adapted from
The populations analyzed here represent the natural variation
those of Clermont et al. (12). Genomic DNA was isolated from 2 ml of overnight
in farm type (conventional versus organic), age of cattle (calf
culture in LB broth (Lennox; Becton, Dickinson, and Company, Sparks, MD)
versus cow), and resistance phenotype (resistant versus suscep-
using the Puregene DNA isolation kit (Gentra Systems Inc., Minneapolis, MN.).
tible). It is clear that phylogroup abundance was not evenly
DNA preparations were quantified with a NanoDrop ND-1000 UV-visible spec-
distributed among the different types of dairy farms (Fig. 1; see
trophotometer (NanoDrop Technologies, Wilmington, DE), diluted to a finalconcentration of 100 ng/l, and stored at 4°C. Genomic DNA preparations were
Table S1 in the supplemental material). The most abundant
tested using primers targeting a 650-bp region of the conserved housekeeping
phylogenetic groups were B1 (58.3%) and A (27.4%), whereas
(see www.shigatox.net/stec/mlst-new/index.html for primer sequences
groups D (11.5%) and B2 (2.8%) were rare. B2 strains were
and reaction conditions), and AmpliTaq Gold DNA polymerase (Applied Bio-
not sampled at each variable level (no resistant B2 genotypes
systems). This protocol has produced a positive amplicon in strains representing
were found on organic farms), so these data were combined
the genotypic diversity of the species as well as E
’s most recent commonancestor, Escherichia albertii
. Genomic DNA was reisolated if the assay was
with group D strain data (B2D) for statistical analyses.
negative. Strains that were negative for duplicate, independent genomic isola-
Genetic composition of antibiotic-susceptible and -resistant
tions were considered members of species other than E
and excluded from
E. coli populations.
Our initial goal was to test for dependent
further analysis. Representative ECOR strains were used as template controls
associations among three nominal variables (F
for farm type, A
for a duplex PCR targeting the genes chuA
. We found that the following
for age of cattle, and E
for ECOR phylogroup) by analyzing
duplex conditions yielded higher PCR specificity with AmpliTaq Gold than thepublished triplex: denaturation at 94°C for 10 min; 35 cycles of 92°C for 1 min,
the number of strains in these categories. The tests for asso-
59°C for 1 min, and 72°C for 30 seconds; and a final elongation at 72°C for 5 min.
ciations in the susceptible population (susceptible to 17 anti-
FIG. 1. Histogram plots of ECOR phylogroups for susceptible and resistant E
populations from conventional (A) and organic (B) farms
(strains from calves [black bars] and strains from cows [gray bars]).
microbials) by log-linear modeling of the 376 susceptible
revealed a significant association between farm type and
strains revealed no significant interactions with ECOR phylo-
ECOR phylogrouping (Table 1). Based on parameter esti-
grouping (Table 1). In other words, the distribution of phylo-
mates, the odds of recovering resistant E
of phylogroup A
groups in antibiotic-susceptible E
sampled from calves
were significantly greater on conventional farms than on or-
and cows on conventional and organic dairy farms was similar
ganic farms (df ϭ 1, 2 ϭ 21.1, probability [Pr] Ͼ 2 Ͻ 0.0001).
and not significantly different. A significant negative associa-
This overabundance of phylogroup A strains was not seen in
tion was found between conventional farms and the number of
the susceptible or resistant population from organic farms. In
susceptible calf strains [i.e., the F
ϭ calf) interaction in
addition, there were no significant farm-phylogroup (i.e., F
Table 1[). This result was expected because the abundance of
interactions when the susceptible populations from both farms
resistant strains was higher in calves on conventional farms
and the resistant population from organic farms were analyzed
than in calves on organic farms. Despite this discrepancy in
together (model not shown). These data suggest that resistance
abundance, however, these data indicate that susceptible
determinants on conventional farms were linked to the genetic
strains of the four phylogroups were circulating at similar fre-
backgrounds of phylogroup A and that these strains increased
quencies on both farm types in young and adult animals.
in frequency as a result of antibiotic use. Interestingly, animal
A similar analysis was applied to the 311 resistant strains and
age was not associated with the distribution of phylogroups in
TABLE 1. Best-fit models explaining the frequency of antibiotic-susceptible and -resistant E
from conventional and organic dairy farmsa
ϭ ECOR group A)
The analysis is based on testing hierarchical log-linear models with nested effects in parentheses. Nominal categorical variables are designated as follows: A
animal age (calf or cow), E
for ECOR phylogroup (A, B1, B2, or D), and F
for farm type (conventional or organic).
designates the overall main effect.
The likelihood ratio chi-square statistic was used to test for goodness of fit of the final population model (compared to the saturated model).
GENETIC COMPOSITION OF E. COLI
POPULATIONS ON FARMS
TABLE 2. Log-linear modeling of significant associations between farm type, multidrug resistance, and ECOR phylogrouping
ϭ ECOR group B1)
ϭ high) ϩ
ϭ medium) ϩ F
Model of farm type (F
), multidrug resistance (D
), and ECOR phylogrouping (E
Three separate parameterizations of the F
model are given to show statistical dependence as a function of nested effects. designates the overall main effect.
Significant interactions (three-way) with E
the resistant population, suggesting that similar phylogroups
tional farms are associated with medium and highly resistant
circulate at similar frequencies in young and adult dairy cattle.
phylogroup A and B1 strains, whereas in contrast, organic
Influence of multidrug resistance on genetic composition.
farms with virtually no antibiotic use are associated with low
Strains were categorized according to their level of drug sus-
and highly resistant phylogroup B1 and D strains.
) as defined by the number of antimicrobial-re-
The association between the age of cattle (A
sistant phenotypes (low, one or two antimicrobial-resistant
), and ECOR phylogrouping (E
) was also found
phenotypes; medium, three or four antimicrobial-resistant
to be heterogeneous. The high abundance of resistant calf
phenotypes; high, five or more antimicrobial-resistant pheno-
strains and limited overall resistance on organic farms resulted
types). A log-linear model fit to the data according to farm type
in sampling zeros for three of the nine possible categories in
), multidrug resistance level (D
), and ECOR phylogrouping
cows (no medium resistant ECOR B2D strains, highly resistant
) revealed significant heterogeneity in the association be-
phylogroup A strains, or highly resistant phylogroup B1 strains
tween these variables (Table 2), including the presence of a
were sampled). After correction for sampling zeros in the
significant three-way (F
) interaction. In other words, the
resistant cow categories, the three-way interaction term (A
best-fit model to these data included all three variables. The
) was not significant in the model. These data suggest that
model was simplified slightly by reparameterizing and nesting
the age of the cattle influences the abundance of multidrug-
the variables (F
models I, II, and III in Table 2), which
resistant strains but does not influence the genetic composition
allowed nonsignificant levels to be removed.
To illustrate the complexity of the interactions affecting bac-
Tetracycline and ampicillin resistance determinants.
terial multidrug resistance, we summarized the components of
311 resistant strains analyzed, 129 (41.5%) were Ampr, 281
populations using mosaic plots of three different
(90.4%) were Tetr, and 112 (36.0%) were resistant to both
parameterizations of the F
log-linear model (Fig. 2). Odds
drugs. Based on PCR screening for three common E
ratios were estimated for significant interactions with respect
to a fixed (nested) factor. For example, when the effect of
(92.2%) Ampr strains carried the bla
multidrug resistance was nested, a significant two-way interac-
maining 10 (7.8%) strains did not produce an amplicon for any
tion between farm type (F
) and phylogroup (E
) was found and
of the targeted loci. Similarly, for three genes known to confer
can be seen by comparing the size of the shaded box to the size
tetracycline resistance (tetA
, and tetC
), 268 (95.4%)
of the nonshaded boxes for a given level of drug susceptibility
Tetr strains carried at least one of these loci, while 13 (4.5%)
). When the low multidrug resistance level is considered
did not. The tetB
genes were the most abundant
(top row of panel B), it is clear that the shaded box represent-
(64.8% and 28.1%), while the tetC
gene was rarely sampled
ing group B1 strains on organic farms is larger than the one for
conventional farms. The opposite is true for phylogroup A or
Genetic composition and resistance determinants.
B2D strains (larger boxes for the conventional farm category);
ated four data sets according to the four genetic determinants
hence, a significant interaction is represented by the shaded
present in the resistant population. Data from the susceptible
organic B1 box (df ϭ 2, 2 ϭ 6.3, Pr Ͼ 2 ϭ 0.044). The odds
population analyzed above were added to each to create a
of isolating phylogroup A strains with medium multidrug re-
two-level factor (G
) for log-linear modeling. Factor G
sistance were significantly higher on conventional farms (df ϭ
rized strains that carried a resistance gene (bla
2, 2 ϭ 8.3, Pr Ͼ 2 ϭ 0.016), and the odds of isolating highly
) or did not (susceptible). Due to the low occurrence in
resistant, phylogroup B2D strains were significantly higher on
the sample, data for tetC
ϩ strains (n
ϭ 7) were pooled with
organic farms (df ϭ 2, 2 ϭ 10.3, Pr Ͼ 2 ϭ 0.006). As
data for strains that were negative for all three loci (n
mentioned above, there were no resistant phylogroup B2
and called tetC
/other. Data for strains that were negative for
strains isolated from organic farms, so the shaded B2D box on
the three ␤-lactamase loci (n
ϭ 10) were omitted. Log-linear
organic farms represents group D strains only. Phylogroup-
models were fit to each of the four data sets to test for asso-
specific interactions were also found when model effects were
ciations between F
, and E
fixed for E
(df ϭ 2, 2 ϭ 9.6, Pr Ͼ 2 ϭ 0.008) and F
(df ϭ 4,
Interactions between the resistance loci and ECOR phylo-
2 ϭ 18.64, Pr Ͼ 2 Ͻ 0.001). These data suggest that conven-
groups were not dependent on farm type (no F
FIG. 2. Mosaic plots of the dependent associations between farm type (organic [o] and conventional [c]) (F
), multidrug resistance (D
ECOR phylogrouping (E
). Shaded boxes mark significant odds ratio estimates (positive odds only). (A) Overall mosaic plot for F
. (B) F
interactions at fixed levels of D
. (C) F
interactions at fixed levels of E
. (D) D
interactions at fixed levels of F
tions). The genetic composition of the susceptible population
based on the presence of three loci (intI1
, and the
was not significantly different from those of the tetA
conserved cassette region). Of the total 298 Ampr and/or Tetr
/other populations (models not shown). The only signifi-
strains, 59 (19.8%) carried a class I integron. We created a
association was found in the bla
three-level factor called “integron populations” that was com-
(Table 3), where the odds of sampling the bla
prised of resistant, integron-positive strains (intϩ); resistant,
conventional farms was significantly associated with ECOR
integron-negative strains (intϪ); and susceptible, integron neg-
phylogroup A (df ϭ 1, 2 ϭ 5.0, Pr Ͼ 2 ϭ 0.025). These data
ative strains (susceptible). Log-linear models were then used to
suggest that the genetic composition of resistant E
test for significant associations between farm type (F
lations on dairy farms is dependent on individual resistance
), and integron populations (I
There was no significant phylogroup-integron (E
Genetic composition and class I integrons.
All Ampr and
tion with farm type (no F
interaction). However, the dis-
Tetr strains were screened for the presence of class I integrons
tribution of ECOR phylogroups was dependent on integron
TABLE 3. Log-linear modeling of farm type, resistance gene, and ECOR phylogrouping
Model of farm type (F
), resistance gene (G
), and ECOR phylogrouping (E
). Resistant populations were defined by the determinant they carried. Note that G-E
was a significant term in the bla
TEM population only. designates the overall main effect.
(two-way) interaction for the bla
GENETIC COMPOSITION OF E. COLI
POPULATIONS ON FARMS
presence in these populations (df ϭ 4, 2 ϭ 12.4, Pr Ͼ 2 ϭ
ferred. For example, we expected to find a significant differ-
0.015). The intϪ and susceptible populations were composi-
ence between the genetic composition of resistant and suscep-
tionally the same (B1 Ͼ A Ͼ B2D), but phylogroups in the intϩ
tible populations if a resistant clone swept to high frequency
population were evenly sampled. This analysis suggests that the
during drug use on conventional farms (clonal expansion). We
intϩ population had significantly more group A and B2D
had the same expectation if clonal interference were operating
strains than the other (intϪ and susceptible) populations.
between multiple resistant clones of the same phylogroup. Anadditional possibility was that clonal interference was operat-ing between clones of different phylogroups. The expectation
here was a more even distribution of phylogroups compared to
In this study, we examined the dynamics of antibiotic selec-
the susceptible population. The significant association between
tion on conventional and organic dairy farms by comparing the
conventional farms, antibiotic resistance, and phylogroup A
relative frequencies of four phylogenetic groups (genetic com-
strains supports the expectation that strains of this phylogroup
position) of antibiotic-resistant and susceptible E
were being selected. This observation does not exclude the
tions. It is noteworthy that this definition of genetic composi-
possibility that resistant strains of other phylogroups were se-
tion does not closely measure the amount and distribution of
lected but suggests that selection was stronger for group A
genetic diversity at the gene and genotype levels but instead is
strains. Further characterization is needed, however, to differ-
susceptible to the phylogroup level so that the dynamics are
entiate between the spread of one clone or multiple closely
indicative of broad changes in population genetic structure. A
number of studies use this compositional definition to look for
The compositional similarity between susceptible popula-
patterns in complex systems with many interacting variables.
tions and resistant populations on organic farms suggests that
For example, phylogroup B1 strains were found to be common
there is an optimal genetic composition (OGC) for the farms in
in a variety of host species (16), but they were not numerically
this study. In other words, these data suggest that there is a
dominant in healthy swine (11) or some human populations
stable relative frequency of ECOR phylogroups in dairy cattle
(27). In short, these data allow observational inferences to be
in the absence of antibiotic selection. Interestingly, populations
made about the ecology of certain phylogenetic groups.
from freshwater beaches also appear to have an
The genetic composition of E
from dairy farms was not
OGC, as defined by a stable composition at six separate beach
similar (B1 Ͼ A Ͼ D Ͼ B2), suggesting that phylogroup B1
sites (41). Although defining the precise mechanism of resis-
strains colonize at a higher abundance and, therefore, have a
tance requires further work, the overabundance of phylogroup
higher relative fitness in dairy cattle. This result is different
A strains on conventional dairy farms was significantly associ-
than what is seen in Australian herbivores where group B2
strains dominate, although climate and proximity to human
presence of class I integrons in the overall resistant population.
activity can also influence the relative distribution of phyloge-
linked to phylogroup A strains during selection on conven-
Rate of compositional change in antibiotic-resistant popu-
tional farms and resulted in a departure from OGC. If this
lations on conventional farms.
A key finding of this study is
interpretation is correct, we predict that a more discriminant
that there is an overabundance of resistant phylogroup A
genetic characterization of Ampr strains from conventional
strains on conventional diary farms compared to phylogroup A
farms will reveal less genetic diversity in group A strains than
strains on organic farms where antibiotic use has been limited.
in resistant strains of phylogroups B1 and B2D.
Based on two observations, we are confident that this over-
Ampicillin and other ␤-lactamase antibiotics are commonly
abundance has been a consequence of antibiotic use and not
used on dairy farms. In a survey of conventional dairy opera-
some other conventional farm management practice. First, sus-
tions from July 2001 to June 2002 in Pennsylvania, about half
ceptible populations on conventional farms and organic farms
of the farms (n
ϭ 17), for which records were available (n
are nearly identical in genetic composition and are not statis-
33), reported using ampicillin to treat pneumonia in calves
tically different, suggesting that these E
(36). A wider survey of dairy farms (n
ϭ 131; 99 conventional
rience similar selective pressures in both agricultural environ-
and 32 organic) from Michigan, Minnesota, New York, and
ments. Second, the composition of the resistant populations on
Wisconsin found no ampicillin use on organic farms compared
organic farms was not significantly different from the compo-
to 8%, 22%, 26%, 12%, and 4% use on conventional farms for
sition of the susceptible populations. These observations also
treatment of calf respiratory disease, adult respiratory disease,
suggest the possibility that the resistant population on conven-
clinical mastitis, metritis, and foot problems, respectively (44).
tional farms will evolve to that of the susceptible populations
This difference in the use of ampicillin between conventional
on organic farms if antibiotic use was stopped. Given that
and organic dairy practices may be responsible for the higher
organic farms in this study were certified as having not used
frequency of resistant phylogroup A strains we observed.
antibiotics for at least 3 years (mean, 8 years; range, 3 to 15
Evidence for hitchhiking of resistance loci.
In contrast to the
years), we estimate that, when antibiotic selective pressure is
Ampr population, there was no evidence supporting an under-
removed, it takes at least this long for the compositional tran-
lying clonal model for the dynamics in the Tetr population.
Populations carrying Tetr determinants (tetA
, and tetC
Evidence for clonal resistance dynamics.
other) were at OGC on both farm types. This observation is
data do not address questions about the acquisition of resis-
difficult to explain if antibiotic selection and clonal spread were
tance determinants by susceptible strains, the observations may
occurring on a single farm type. One explanation is that the
adequately describe general dynamics after resistance is con-
organic farms received an occasional flux of Tetr strains from
conventional farms, and the migration was sufficient to main-
reported for such change may be longer than the average life
tain the observed similarity. However, this explanation seems
unlikely because the occasional flux would likely bring Ampr
Effect of multidrug resistance on genetic composition.
strains from conventional farms as well, which in turn would
found a rather complicated interaction between farm type,
ameliorate the differences discussed above. If Tetr loci were
multidrug resistance, and ECOR phylogrouping (Fig. 2). Sig-
linked to other compensatory, beneficial mutations, then the
nificant associations depended on the way our log-linear model
composition of these populations might appear similar regard-
was parameterized. However, all three possible parameteriza-
tions resulted in a significant association between low multi-
Several lines of evidence support the hypothesis for the role
drug resistance, group B1 strains, and organic farms. These
of hitchhiking or compensatory mutations in Tetr antibiotic
data suggest an inverse relationship between multidrug resis-
resistance spread. Bartoloni et al. initially described a resistant
tance and fitness for group B1 strains on organic farms. Since
population from humans living in a remote Guarani
phylogroup B1 strains were the numerically dominant group
Indian community in Bolivia (3). Individuals of the village had
overall, this result should be encouraging for those seeking to
little contact with outsiders and no veterinary or agricultural
reduce the amount of multidrug-resistant strains in dairy cattle
antibiotic use, relied on rainwater for survival, and had limited
available health care (every 3 months). Nevertheless, tetracy-
Two of the parameterizations showed an association with
cline resistance was found in 64% (69 of 108) of the individuals
high multidrug resistance, group D strains, and organic
tested. Pallecchi et al. recently characterized the underlying
farms. This result is important because a number of human
genetic determinants and ECOR phylogroups for 113 resistantstrains of the original collection (28). The authors found that
pathogens, including the strain most associated with human
of the 103 Tetr strains analyzed, 52 carried tetA
and 51 carried
hemorrhagic colitis, O157:H7, belong to this group (accord-
. These loci were distributed among all four E
ing to the PCR method used here). However, we are cau-
groups (same procedure used in this study) and were found on
tious to base generalizations on this analysis because (i) we
all five conjugative plasmids identified in this study. The abun-
did not design our sampling study to directly address this
dance and distribution of Tetr strains in this remote community
question and (ii) the abundance of strains used for these
support the hypothesis that naturally occurring Tetr deter-
comparisons were low. For example, the F
minants circulate in hosts for reasons other than selection by
association (Table 2) between highly resistant phylogroup D
strains and organic farms becomes nonsignificant if two
Support of the hitchhiking hypothesis for Tetr loci is also
fewer strains were sampled on organic farms and two addi-
consistent with the description of a “calf-adapted” E
tional strains were sampled on conventional farms. Simi-
ulation that was multiply resistant to streptomycin, sulfadia-
larly, we are cautious about the association between highly
zine, and tetracycline (20). Almost all strains (49 of 50) ana-
multidrug-resistant phylogroup B1 strains and medium mul-
lyzed shared a ϳ140-kb chromosomal location and the same
tidrug-resistant phylogroup A strains on conventional farms
resistance loci (strA
, and tetB
) and were genetically
because the significance of the association depends on the
diverse by pulsed-field gel electrophoresis. Khachatryan and
colleagues showed that on average the streptomycin-, sulfadi-
The genetic composition for the antibiotic-
azine-, and tetracycline-resistant population outcompeted sus-
populations on conventional farms, suscep-
ceptible strains in vitro and in neonatal calves (18). They also
populations on organic farms, and resistant E
showed that the resistance loci themselves do not influence this
populations on organic farms was the same, suggesting a rel-
selective advantage (19). Their main conclusion was that the
ative steady-state genetic composition for the farms in this
combination of strA
, and tetB
in the original resistant
study. In contrast, the resistant population on conventional
population had hitchhiked with some other fitness-conferring
farms had an overabundance of Ampr, group A strains that
could be explained by linked loci (bla
Effect of age on genetic composition.
Sato et al. showed that
during a selective sweep or clonal interference among closely
the resistant strains examined here were most prevalent in
related strains. Given the amount of time since organic farms
calves on conventional farms (34). A similar positive associa-
had abandoned conventional practices, the rate of composi-
tion has been reported in other studies of preweaned calves
tional change was estimated to be between 3 and 15 years
and adult cattle (5, 7, 20). These observations suggest thatantibiotic-resistant strains are better at colonizing calves than
(mean, 8 years). In contrast to the Ampr population, the Tetr
adult cows. One explanation for these observations is that the
populations analyzed here showed no clonal dynamics and
cost of resistance (fitness cost) becomes too great as the host
appeared to achieve a steady-state genetic composition. These
gastrointestinal tract matures and competition with other
data add support to the hypothesis that the abundance and
microbes increases. Regardless of its influence on preva-
distribution of Tetr determinants are weakly influenced by
lence, the age of cattle had little effect on the distribution of
antibiotic use. We found that the age of cattle had little influ-
phylogenetic groups in either the susceptible or resistant
ence on the genetic composition of the resistant or susceptible
populations of this study. These data suggest that resistant
populations. Finally, phylogroup B1 strains with low multidrug
strains decrease in abundance as the cattle age, while the
resistance were significantly associated with organic farms, sug-
genetic composition of the population remains stable. Other
gesting that these dairy farming practices have a proportion-
analyses of human strains showed a significant association
ately large, negative effect on the prevalence of multidrug-
between host age and genetic composition, but the time
GENETIC COMPOSITION OF E. COLI
POPULATIONS ON FARMS
21. Levin, B. R., and C. T. Bergstrom.
2000. Bacteria are different: observations,
interpretations, speculations, and opinions about the mechanisms of adap-
This work was supported in part by MRU matching funds from the
tive evolution in prokaryotes. Proc. Natl. Acad. Sci. USA 97:
College of Veterinary Medicine and the Graduate College of Michigan
22. Levy, S. B.
2002. The 2000 Garrod lecture. Factors impacting on the problem
of antibiotic resistance. J. Antimicrob. Chemother. 49:
23. Lindstedt, B. A., E. Heir, I. Nygard, and G. Kapperud.
tion of class I integrons in clinical strains of Salmonella enterica
enterica serovars Typhimurium and Enteritidis from Norwegian hospitals.
1. Anderson, M. A., J. E. Whitlock, and V. J. Harwood.
2006. Diversity and
J. Med. Microbiol. 52:
distribution of Escherichia coli
genotypes and antibiotic resistance pheno-
24. Maisnier-Patin, S., and D. I. Andersson.
2004. Adaptation to the deleterious
types in feces of humans, cattle, and horses. Appl. Environ. Microbiol.
effects of antimicrobial drug resistance mutations by compensatory evolu-
tion. Res. Microbiol. 155:
2. Barbosa, T. M., and S. B. Levy.
2000. The impact of antibiotic use on
25. Maisnier-Patin, S., O. G. Berg, L. Liljas, and D. I. Andersson.
resistance development and persistence. Drug Resist. Updates 3:
pensatory adaptation to the deleterious effect of antibiotic resistance in
3. Bartoloni, A., F. Bartalesi, A. Mantella, E. Dell’Amico, M. Roselli, M.
. Mol. Microbiol. 46:
Strohmeyer, H. G. Barahona, V. P. Barron, F. Paradisi, and G. M. Rossolini.
2002. Performance standard for antimicrobial disk and dilution
2004. High prevalence of acquired antimicrobial resistance unrelated to
susceptibility tests for bacteria isolated from animals. Approved standard,
heavy antimicrobial consumption. J. Infect. Dis. 189:
4. Bartoloni, A., L. Pallecchi, M. Benedetti, C. Fernandez, Y. Vallejos, E.
27. Nowrouzian, F. L., I. Adlerberth, and A. E. Wold.
2006. Enhanced persis-
Guzman, A. L. Villagran, A. Mantella, C. Lucchetti, F. Bartalesi, M. Stro-
tence in the colonic microbiota of Escherichia coli
strains belonging to phy-
hmeyer, A. Bechini, H. Gamboa, H. Rodriguez, T. Falkenberg, G. Kronvall,
logenetic group B2: role of virulence factors and adherence to colonic cells.
E. Gotuzzo, F. Paradisi, and G. M. Rossolini.
Microbes Infect. 8:
commensal Escherichia coli
in children, Peru and Bolivia. Emerg. Infect. Dis.
28. Pallecchi, L., C. Lucchetti, A. Bartoloni, F. Bartalesi, A. Mantella, H.
5. Berge, A. C., E. R. Atwill, and W. M. Sischo.
2005. Animal and farm influ-
Gamboa, A. Carattoli, F. Paradisi, and G. M. Rossolini.
ences on the dynamics of antibiotic resistance in faecal Escherichia coli
structure and resistance genes in antibiotic-resistant bacteria from a remote
young dairy calves. Prev. Vet. Med. 69:
community with minimal antibiotic exposure. Antimicrob. Agents Che-
6. Berge, A. C., P. Lindeque, D. A. Moore, and W. M. Sischo.
2005. A clinical
trial evaluating prophylactic and therapeutic antibiotic use on health and
29. Pallecchi, L., M. Malossi, A. Mantella, E. Gotuzzo, C. Trigoso, A. Bartoloni,
performance of preweaned calves. J. Dairy Sci. 88:
F. Paradisi, G. Kronvall, and G. M. Rossolini.
2004. Detection of CTX-M-
7. Berge, A. C., D. A. Moore, and W. M. Sischo.
2006. Field trial evaluating the
type ␤-lactamase genes in fecal Escherichia coli
isolates from healthy chil-
influence of prophylactic and therapeutic antimicrobial administration on
dren in Bolivia and Peru. Antimicrob. Agents Chemother. 48:
antimicrobial resistance of fecal Escherichia coli
in dairy calves. Appl. Envi-
30. Raymond, M. J., R. D. Wohrle, and D. R. Call.
2006. Assessment and
ron. Microbiol. 72:
promotion of judicious antibiotic use on dairy farms in Washington State. J.
8. Bjorkman, J., I. Nagaev, O. G. Berg, D. Hughes, and D. I. Andersson.
Dairy Sci. 89:
Effects of environment on compensatory mutations to ameliorate costs of
31. Reid, S. D., C. J. Herbelin, A. C. Bumbaugh, R. K. Selander, and T. S.
antibiotic resistance. Science 287:
2000. Parallel evolution of virulence in pathogenic Escherichia coli
9. Boerlin, P., R. Travis, C. L. Gyles, R. Reid-Smith, N. Janecko, H. Lim, V.
Nicholson, S. A. McEwen, R. Friendship, and M. Archambault.
32. Reynolds, M. G.
2000. Compensatory evolution in rifampin-resistant Esch-
microbial resistance and virulence genes of Escherichia coli
. Genetics 156:
swine in Ontario. Appl. Environ. Microbiol. 71:
33. Sander, P., B. Springer, T. Prammananan, A. Sturmfels, M. Kappler, M.
10. Brinas, L., M. A. Moreno, T. Teshager, M. Zarazaga, Y. Saenz, C. Porrero,
Pletschette, and E. C. Bottger.
2002. Fitness cost of chromosomal drug
L. Dominguez, and C. Torres.
2003. Beta-lactamase characterization in Esch-
resistance-conferring mutations. Antimicrob. Agents Chemother. 46:
isolates with diminished susceptibility or resistance to extended-
spectrum cephalosporins recovered from sick animals in Spain. Microb.
34. Sato, K., P. C. Bartlett, and M. A. Saeed.
2005. Antimicrobial susceptibility
Drug Resist. 9:
of Escherichia coli
isolates from dairy farms using organic versus conven-
11. Chapman, T. A., X. Y. Wu, I. Barchia, K. A. Bettelheim, S. Driesen, D. Trott,
tional production methods. J. Am. Vet. Med. Assoc. 226:
M. Wilson, and J. J. Chin.
2006. Comparison of virulence gene profiles of
35. Sawant, A. A., N. V. Hegde, B. A. Straley, S. C. Donaldson, B. C. Love, S. J.
strains isolated from healthy and diarrheic swine. Appl.
Knabel, and B. M. Jayarao.
2007. Antimicrobial-resistant enteric bacteria
Environ. Microbiol. 72:
from dairy cattle. Appl. Environ. Microbiol. 73:
12. Clermont, O., S. Bonacorsi, and E. Bingen.
2000. Rapid and simple deter-
36. Sawant, A. A., L. M. Sordillo, and B. M. Jayarao.
2005. A survey on antibi-
mination of the Escherichia coli
phylogenetic group. Appl. Environ. Micro-
otic usage in dairy herds in Pennsylvania. J. Dairy Sci. 88:
37. Schrag, S. J., V. Perrot, and B. R. Levin.
1997. Adaptation to the fitness costs
13. Colom, K., J. Perez, R. Alonso, A. Fernandez-Aranguiz, E. Larino, and R.
of antibiotic resistance in Escherichia coli
. Proc. Biol. Sci. 264:
2003. Simple and reliable multiplex PCR assay for detection of
38. Sherley, M., D. M. Gordon, and P. J. Collignon.
2004. Evolution of multi-
SHV and bla
OXA-1 genes in Enterobacteriaceae. FEMS Microbiol.
resistance plasmids in Australian clinical isolates of Escherichia coli
14. Donaldson, S. C., B. A. Straley, N. V. Hegde, A. A. Sawant, C. DebRoy, and
39. Singh, R., C. M. Schroeder, J. Meng, D. G. White, P. F. McDermott, D. D.
B. M. Jayarao.
2006. Molecular epidemiology of ceftiofur-resistant Esche-
Wagner, H. Yang, S. Simjee, C. Debroy, R. D. Walker, and S. Zhao.
isolates from dairy calves. Appl. Environ. Microbiol. 72:
Identification of antimicrobial resistance and class 1 integrons in Shiga toxin-
producing Escherichia coli
recovered from humans and food animals. J.
15. Friendly, M.
1994. Mosaic displays for multi-way contingency tables. J. Am.
Antimicrob. Chemother. 56:
Stat. Assoc. 89:
16. Gordon, D. M., and A. Cowling.
2003. The distribution and genetic structure
40. Stokes, M. E., C. S. Davis, and G. G. Koch.
2000. Categorical data analysis
of Escherichia coli
in Australian vertebrates: host and geographic effects.
using the SAS system, 2nd ed. SAS Institute Inc., Cary, NC.
41. Walk, S. T., E. W. Alm, L. M. Calhoun, J. M. Mladonicky, and T. S. Whittam.
17. Gordon, D. M., S. E. Stern, and P. J. Collignon.
2005. Influence of the age
2007. Genetic diversity and population structure of Escherichia coli
and sex of human hosts on the distribution of Escherichia coli
from freshwater beaches. Environ. Microbiol. 9:
and virulence traits. Microbiology 151:
42. Whittam, T. S., M. L. Wolfe, I. K. Wachsmuth, F. Orskov, I. Orskov, and
18. Khachatryan, A. R., T. E. Besser, D. D. Hancock, and D. R. Call.
R. A. Wilson.
1993. Clonal relationships among Escherichia coli
of a nonmedicated dietary supplement correlates with increased prevalence
cause hemorrhagic colitis and infantile diarrhea. Infect. Immun. 61:
of streptomycin-sulfa-tetracycline-resistant Escherichia coli
on a dairy farm.
Appl. Environ. Microbiol. 72:
43. Wirth, T., D. Falush, R. Lan, F. Colles, P. Mensa, L. H. Wieler, H. Karch,
19. Khachatryan, A. R., D. D. Hancock, T. E. Besser, and D. R. Call.
P. R. Reeves, M. C. Maiden, H. Ochman, and M. Achtman.
2006. Sex and
Antimicrobial drug resistance genes do not convey a secondary fitness ad-
virulence in Escherichia coli
: an evolutionary perspective. Mol. Microbiol.
vantage to calf-adapted Escherichia coli
. Appl. Environ. Microbiol. 72:
44. Zwald, A. G., P. L. Ruegg, J. B. Kaneene, L. D. Warnick, S. J. Wells, C.
20. Khachatryan, A. R., D. D. Hancock, T. E. Besser, and D. R. Call.
Fossler, and L. W. Halbert.
2004. Management practices and reported an-
of calf-adapted Escherichia coli
in maintenance of antimicrobial drug resis-
timicrobial usage on conventional and organic dairy farms. J. Dairy Sci.
tance in dairy calves. Appl. Environ. Microbiol. 70:
Capítulo 2: A ANTROPOLOGIA APLICADA E AS SUAS PERSPECTIVAS Xerardo Pereiro (UTAD- Pólo de Miranda do Douro) -Pereiro, X. (2005): “A Antropologia Aplicada e as suas perspectivas”, em Pereiro, X. e Mendes, P. (coordenadores) (2006): Textos de Antropologia Aplicada. UTAD: Miranda do Douro, pp. 3-13. INTRODUÇÃO (*) Apresento neste texto uma reflexão sobre as posturas relativas à
*Cold dark Massachusetts winter, January, 1692. *Eight young girls began to take il , beginning with 9-year-old Elizabeth Parris, thedaughter of Reverend Samuel Parris, as well as his niece, 11-year-old Abigail Wil iams. But theirs was a strange sickness: the girls suffered from delirium, violent convulsions,incomprehensible speech, trance-like states, and odd skin sensations. The worriedvil