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In this study, we evaluated two biomolecular techniques for discriminating between strains of Escherichia coli isolated form a variety of sources. The DNA of 211 strains of E. coli collected from dairy farms, calves, feces, pigs, primates, humans, and food products was analyzed by pulsed-field gel electrophoresis (PFGE) and repetitive-element polym...
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Context 1
... O157 and non-O157:H7 E. coli strains used in this study (n ¼ 211) were obtained from dairy farms, calves, feces, pigs, primates, hu- mans, and food products (Table 1). Biochemical and serological confirmation and identification of these strains were reported previously by Murinda et al. (2004). ...
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... molecular typing methods, PFGE and BOX-PCR, were evaluated for grouping 211 E. coli strains based on their origin (Table 1). PFGE patterns of DNA digested with XbaI produced fragments in the 36.3 to 582.6 kb size range, and distinguished 154 different E. coli strain patterns with a similarity percentage of 77%. ...
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... results demonstrated that it was not possible to correlate how strains were grouped with the source of isolation. However, it should be noted that most of the strains used in our study were isolated from bovine sources: almost 74% of strains were collected from bovine dairy farms, bovine feed lot, cull dairy cow feces, or bulk tank milk, while 26% were isolated from diverse sources such as human, pig, primate, or food (Table 1) which might have biased the results obtained in our study. ...
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Citations
... To genotype E. coli samples, we used a BOX-PCR (primer BOX-AIR 5′-CTACGGCAAGGCGACGCTGACG-3′), which is an established method for discriminating between genetically similar E. coli genotypes (Cesaris et al., 2007;Goldberg, Gillespie, & Singer, 2006;Mohapatra et al., 2008). BOX-PCRs were performed in 20 µl volumes containing 1X Qiagen Buffer, 0.25 mM dNTPs, 0.75 µM BOX-AIR primer, 0.2 U Taq (Qiagen Hot Start) and 2 µl DNA template. ...
Social network analysis has been postulated as a tool to study potential pathogen transmission in wildlife but is resource‐intensive to quantify. Networks based on bacterial genotypes have been proposed as a cost‐effective method for estimating social or transmission network based on the assumption that individuals in close contact will share commensal bacteria. However, the use of network analysis to study wild populations requires critical evaluation of the assumptions and parameters these models are founded on.
We test (a) whether networks of commensal bacterial sharing are related to hosts’ social associations and hence could act as a proxy for estimating transmission networks, (b) how the parameters chosen to define host associations and delineate bacterial genotypes impact inference and (c) whether these relationships change across time. We use stochastic simulations to evaluate how uncertainty in parameter choice affects network structure.
We focused on a well‐studied population of eastern grey kangaroos (Macropus giganteus), from Sundown National Park, Australia. Using natural markings, each individual was identified and its associations with other kangaroos recorded through direct field observations over 2 years to construct social networks. Faecal samples were collected, Escherichia coli was cultured and genotyped using BOX‐PCR, and bacterial networks were constructed. Two individuals were connected in the bacterial network if they shared at least one E. coli genotype. We determined the capacity of bacterial networks to predict the observed social network structure in each year.
We found little support for a relationship between social association and dyadic commensal bacterial similarity. Thresholds to determine host associations and similarity cut‐off values used to define E. coli genotypes had important ramifications for inferring links between individuals. In fact, we found that inferences can show opposite patterns based on the chosen thresholds. Moreover, no similarity in overall bacterial network structure was detected between years.
Although empirical disease transmission data are often unavailable in wildlife populations, both bacterial networks and social networks have limitations in representing the mode of transmission of a pathogen. Our results suggest that caution is needed when designing such studies and interpreting results.
... From these, we generated bacterial DNA fingerprint profiles, or pulsotypes, from each E. coli isolate using PulseNet Pulsed Field Gel Electrophoresis (PFGE: using the CDC protocol). PFGE is a well-established method for typing bacteria and has been shown to perform well for comparing large numbers of isolates of commensal gut E. coli (Cesaris et al., 2007;Kilonzo, Atwill, Mandrell, Garrick, & Villanueva, 2011;Kondo, Hoar, & Mandrell, 2010;Ribot et al., 2006;VanderWaal, Atwill et al., 2014). While we acknowledge that more novel techniques like whole genome sequencing (WGS) and metagenomic processing would add additional discriminatory power to our analyses, the prohibitory increase in cost would significantly reduce sample size and therefore prevent a network analysis. ...
In social animals, affiliative behaviours bring many benefits, but also costs such as disease risk. The ways in which affiliation may affect the risk of infectious agent transmission remain unclear. Moreover, studies linking variation in affiliative interactions to infectious agent incidence/diversity have speculated that disease transmission may have occurred, rather than revealing that transmission did occur. We address these gaps using the phylogenetics of commensal gut Escherichia coli to determine whether affiliative grooming and huddling social networks mediated microbial transmission among rhesus macaques. We collected behavioural and microbial data from adult macaques across a 12-week period that was split into two 6-week phases to better detect dyadic transmission. We reconstructed undirected social networks from affiliative interactions and reconstructed microbial transmission networks from the pairwise phylogenetic similarity of E. coli pulsotypes from macaques within and across adjacent sampling events. Macaque E. coli pulsotypes were more phylogenetically similar to each other than to environmental isolates, which established a premise for socially mediated transmission. Dyadic grooming and huddling frequencies strongly influenced the likelihood of E. coli transmission during the second data collection phase, but not the first. Macaques that were more central/well connected in both their grooming and huddling networks were also more central in the E. coli transmission networks. Our results confirmed that affiliative grooming and huddling behaviours mediate the transmission of gut microbes among rhesus macaques, particularly among females and high-ranking individuals. The detectability of socially mediated E. coli transmission maybe partially masked by environmental acquisition in males, or by high frequencies of interactions in captivity. Predicting the potential transmission pathways of gastrointestinal parasites and pathogens, our findings add to current knowledge of the coevolutionary relationships between affiliative behaviour and health and may be used to identify ‘superspreader’ individuals as potential targets for disease control strategies.
... Up to six E. coli colonies (range, 2 to 6; median, 4) were randomly selected from each incubated sample, subcultured, and frozen for transport to the United States. To determine the genetic profile of each isolate, densitometric curves were generated using the banding patterns from BOX-PCR and gel electrophoresis (97)(98)(99). Genetic similarity of each isolate to all others was subsequently calculated through pairwise comparisons of densitometric curves using the Pearson product-moment correlation coefficient. Two isolates were interpreted as the same E. coli subtype if they were at least 90% similar (21). ...
There is growing evidence that anthropogenic sources of antibiotics and antimicrobial-resistant bacteria can spill over into natural ecosystems, raising questions about the role wild animals play in the emergence, maintenance, and dispersal of antibiotic resistance genes. In particular, we lack an understanding of how resistance genes circulate within wild animal populations, including whether specific host characteristics, such as social associations, promote interhost transmission of these genes. In this study, we used social network analysis to explore the forces shaping population-level patterns of resistant Escherichia coli in wild giraffe (Giraffa camelopardalis) and assess the relative importance of social contact for the dissemination of resistant E. coli between giraffe. Of 195 giraffe sampled, only 5.1% harbored E. coli isolates resistant to one or more tested antibiotics. Whole-genome sequencing on a subset of resistant isolates revealed a number of acquired resistance genes with linkages to mobile genetic elements. However, we found no evidence that the spread of resistance genes among giraffe was facilitated by interhost associations. Giraffe with lower social degree were more likely to harbor resistant E. coli, but this relationship was likely driven by a correlation between an individual's social connectedness and age. Indeed, resistant E. coli was most frequently detected in socially isolated neonates, indicating that resistant E. coli may have a selective advantage in the gastrointestinal tracts of neonates compared to other age classes. Taken together, these results suggest that the maintenance of antimicrobial-resistant bacteria in wild populations may, in part, be determined by host traits and microbial competition dynamics within the host.
... Fourth, differences in genotyping methods may also explain why our results differed from some prior studies. In particular, two prior studies that found effects of social structure on E. coli transmission used the BOX-PCR method of Cesaris et al. [95] to define genetically similar isolates of E. coli in giraffes. Studies on E. coli infecting gorillas and humans used a similar approach called repetitive-element PCR [10,96]. ...
Social structure is proposed to influence the transmission of both directly and environmentally transmitted infectious agents. However in natural populations, many other factors also influence transmission, including variation in individual susceptibility and aspects of the environment that promote or inhibit exposure to infection. We used a population genetic approach to investigate the effects of social structure, environment, and host traits on the transmission of Escherichia coli infecting two populations of wild elephants: one in Amboseli National Park and another in Samburu National Reserve, Kenya. If E. coli transmission is strongly influenced by elephant social structure, E. coli infecting elephants from the same social group should be genetically more similar than E. coli sampled from members of different social groups. However, we found no support for this prediction. Instead, E. coli was panmictic across social groups, and transmission patterns were largely dominated by habitat and host traits. For instance, habitat overlap between elephant social groups predicted E. coli genetic similarity, but only in the relatively drier habitat of Samburu, and not in Amboseli, where the habitat contains large, permanent swamps. In terms of host traits, adult males were infected with more diverse haplotypes, and males were slightly more likely to harbor strains with higher pathogenic potential, as compared to adult females. In addition, elephants from similar birth cohorts were infected with genetically more similar E. coli than elephants more disparate in age. This age-structured transmission may be driven by temporal shifts in genetic structure of E. coli in the environment and the effects of age on bacterial colonization. Together, our results support the idea that, in elephants, social structure often will not exhibit strong effects on the transmission of generalist, fecal-oral transmitted bacteria. We discuss our results in the context of social, environmental, and host-related factors that influence transmission patterns.
... Samples were shipped on dry ice to the UC Davis School of Veterinary Medicine. DNA was extracted from cultured cells using QIAGEN DNeasy Blood and Tissue kits (QIAGEN, Valencia, CA) and genetic subtypes were determined using BOX-PCR and gel electrophoresis, which is a well-established method for discriminating between genetically similar E. coli subtypes (Cesaris et al., 2007;Goldberg et al., 2006;Mohapatra and Mazumder, 2008). Similarity of each isolate to all others was determined through pairwise comparisons of the densitometric curves generated for each isolate by gel electrophoresis. ...
Multi-host wildlife pathogens are an increasing concern for both wildlife conservation and livestock husbandry. Here, we combined social network theory with microbial genetics to assess patterns of interspecific pathogen transmission among ten species of wild and domestic ungulates in Kenya. If two individuals shared the same genetic subtype of a genetically diverse microbe, Escherichia coli, then we inferred that these individuals were part of the same transmission chain. Individuals in the same transmission chain were interlinked to create a transmission network. Given interspecific variation in physiology and behavior, some species may function as “super-spreaders” if individuals of that species are consistently central in the transmission network. Pathogen management strategies targeted at key super-spreader species are theoretically more effective at limiting pathogen spread than conventional strategies, and our approach provides a means to identify candidate super-spreaders in wild populations. We found that Grant’s gazelle (Gazella granti) typically occupied central network positions and were connected to a large number of other individuals in the network. Zebra (Equus burchelli), in contrast, seemed to function as bridges between regions of the network that would otherwise be poorly connected, and interventions targeted at zebra significantly increased the level of fragmentation in the network. Although not usually pathogenic, E. coli transmission pathways provide insight into transmission dynamics by demonstrating where contact between species is sufficient for transmission to occur and identifying species that are potential super-spreaders.
... Using data from captive giraffe, which hosted approximately two subtypes of E. coli per individual (unpublished data), we calculated that there was a <10% probability of failing to capture subtype diversity in the gut if four isolates were taken (Singer et al. 2000). Genetic subtypes were determined using BOX-PCR and gel electrophoresis, which is a well-established method for discriminating between genetically similar E. coli subtypes (Cesaris et al. 2007;Mohapatra & Mazumder 2008). See Appendix S1 in the online supporting information for detailed laboratory procedures. ...
Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks.
Using a novel approach, we utilize the genetics of a diverse microbe, E scherichia coli , to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population ( G iraffe camelopardalis ). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E . coli .
By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home‐range overlap among individuals.
We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied ‘bottleneck’ positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super‐spreaders or transmission bottlenecks in the population.
These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli . This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis.
... However, in Cuba, where diarrheic diseases are responsible for 31% and 37% of the total piglet's mortality during the pre-and post-weaning periods, respectively(Cabrera and García, 2009), the identification of enteropathogens is limited, and the antibiotic resistance of porcine enterobacteria has not been surveyed(Barrera et al., 2005;Lazo et al., 2005;Blanco et al., 2006).Many DNA-based tests have been applied to reveal genetic relatedness amongEnterobacteriaceae (e.g. PFGE, REP-PCR, BOX-PCR, and ERIC-PCR;Versalovic et al., 1991;Cesaris et al., 2007;Duan et al., 2009). Enterobacterial repetitive intergenic consensus (ERIC) sequences are highly conserved at the nucleotide sequence level, and they constitute an effective marker when identifying clonal variability of isolates because their chromosomal locations and numbers differ among Enterobacteriaceae clones(Versalovic et al., 1991). ...
Porcine pre- and post-weaning diarrhea are multi-factorial diseases that negatively impact the efficiency of swine production worldwide. Often, swine veterinarians complain about the scarce information on the epidemiology of diarrhea and about limitations for the specific identification of enteropathogens in Cuba. Therefore, insufficient control measures are undertaken which results in an increased incidence of swine diarrhea.
This thesis updates epidemiological knowledge concerning the infectious etiology of diarrhea in young pigs in Cuba through the differential identification of enteropathogens in neonatal, in suckling and in newly-weaned piglets. This epidemiological study identified Escherichia coli as a major enteropathogen. Therefore, a major focus was directed on E. coli infections in the rest of the thesis.
First, the antibiotic resistance and the genetic relatedness of Cuban pathogenic E. coli isolates were determined. Then, the seroprevalence of antibodies against F4 and F18 fimbriae of E. coli was assessed. Finally, the genetic susceptibility of Cuban pigs for enteric colonization by F4+ and F18+ E. coli was analyzed. The first Chapter is a review of literature focused on the swine production in the Cuban context and on the epidemiology of the main enteropathogens causing diarrhea in young pigs worldwide. The identification of different enteropathogens is also discussed and the epidemiological term ¨mixed types¨ which refers to the combined infectious etiology of diarrhea is introduced.
Chapter 2 was undertaken to gain insights into the infectious etiology of porcine pre- and post-weaning diarrhea in Villa Clara province, Cuba. Intestinal contents of diarrheic pigs were tested by culture, microscopy, and immunological or DNA-based techniques. At least one enteropathogen was detected in 64.4% and in 42.2% of suckling and weaned pigs, respectively. Enterotoxigenic E. coli (ETEC) was significantly the most frequent pathogen, and most virotypes were either STa+/STb+ or F4+/STa+/STb+. The overall occurrence of the rest of the pathogens was 10% for transmissible gastroenteritis virus and Cryptosporidium parvum, 6.7% for rotavirus A and Isospora suis, 5.6% for -toxigenic Clostridium perfringens, 3.3% for verocytotoxigenic E. coli (VTEC), and 2.2% for Salmonella enterica subspecies enterica serovar Newport. Porcine epidemic diarrhea virus, β-toxigenic C. perfringens, Eimeria spp., and helminths were not identified. Twelve out of 48 enteropathogen positive piglets (25%) were infected with more than one pathogen and ETEC was present in 10 out of 12 mixed infections. These results demonstrate that several enteropathogens, either alone or as part of a mixed infection, are associated with porcine pre- and post-weaning diarrhea in the Villa Clara province, Cuba.
Since E. coli is the most prevalent enteropathogen in pigs suffering from diarrhea in Cuba, we studied this pathogen in more detail in the next Chapters.In Chapter 3 we determined the antibiotic resistance profile and the genetic relatedness of pathogenic E. coli isolated from piglets suffering from diarrhea. The highest resistance rates were seen to antibiotics traditionally administered in Cuban piggeries: tetracycline (69%), ampicillin (54%), sulphonamide compounds (50%), and kanamycin (50%); 65% of isolates were multi-drug resistant. The ERIC-PCR revealed a high degree of polymorphism in the E. coli DNA sequences and relatedness among F4+/STa+/STb+ or F18+/LT+/STb+ isolates from different piggeries, and among the STb+, STa+/STb+, F4+/STa+/STb+ or F18+/STx2e+ ones from the same piggery. Genetically diverse as well as genetically related pathogenic E. coli highly susceptible to nalidixic acid, ciprofloxacin, gentamicin, amikacin, chloramphenicol, cephalosporins, amoxicilline-clavulanic acid, and trimethoprim are associated with piglet’s diarrhea in the Villa Clara province, Cuba. The analysis on genetic diversity by ERIC-PCR demonstrated a clonal relationship among
pathogenic E. coli carrying the same virulence factors and similar antibiotic resistance. The implementation of these epidemiological research data throughout Cuba can contribute to the surveillance, prevention, and control of swine colibacillosis.
Considering that F4- or F18-specific serum antibodies in Cuban pigs will reflect the presence and spread of F4+ ETEC or F18+ ETEC/VTEC, we determined in Chapter 4 the prevalence of F4- and F18-specific antibodies in sera of 1044 gilts. For the data analysis random-effects models and a mixture model in R (package “mixAK”; Komárek, 2009) were fitted. Low, moderate, and high levels of F4-specific antibodies were found in 67.6%, 26.8%, and 5.6% of gilts, while 66.4% and 33.6% of them showed low and high levels of F18-specific antibodies, respectively. Hereby, we show that F4+ and F18+ E. coli are highly prevalent as potential enteropathogens in Cuban piggeries.
Chapter 5 was carried out to firstly know the frequency of genotypes and alleles determining susceptibility or resistance to F18+ E coli infections. To investigate the susceptibility to F4+ E. coli infections, the XbaI polymorphism in the porcine mucin 4 gene was determined in pigs raised in Villa Clara province, Cuba. The XbaI-resistant genotype was frequently detected (0.66) and the susceptible heterozygote or homozygote genotypes were less frequent (0.31 and 0.03, respectively). Since we found that there is a high seroprevalence of F4-specific antibodies in sera of young sows in Cuba, we conclude that the XbaI polymorphism in mucin 4 cannot be used as a reliable marker to determine genetic resistance to F4+ E. coli infections.
Pigs carrying the F18-resistant genotype were not frequently found (0.13), while the heterozygous or homozygous susceptible genotypes occurred most frequently (0.27 and 0.60, respectively), coinciding with previous reports of a high prevalence of F18+ E. coli and F18-specific antibodies in the Cuban swine herd. At least for F18+ E. coli control, a genetic improvement of pigs considering the resistant genotype could be taken as a way to control swine diarrhea or edematous disease in Cuba. This strategy is not recommended for F4 since the XbaI polymorphism in the mucin 4 gene does not appear to be a good marker for resistance to F4+ E. coli infections. Finally, the general discussion and future perspectives are presented in Chapter 6. A better production efficiency of the Cuban Company for pork production might be achieved by the improvement of surveillance, prevention, and control programs of infectious diarrhea in young pigs, particularly colibacillosis. Cuban veterinary authorities should acknowledge the importance of identification of enteropathogens and of surveillance of antibiotic resistance by the Veterinary Diagnostic Laboratories. In the short-term, vaccination against E. coli, the introduction of efficient antibiotics and improvement of management practices appear advantageous to control porcine pre- and post-weaning diarrhea in Cuba. In the long-term, breeding of F4- and F18-resistant pigs as well as investments in facilities will be necessary.
... A genomic fingerprinting method was commonly used to discriminate fecal E. coli strains from cows, chickens and humans by their BOX-PCR profiles [15]. Regarding bacterial source tracking, Fourier transform infrared (FTIR) micro-spectroscopy was demonstrated by Carlos et al. [16] to be a suitable tool for fecal E. coli discrimination. ...
... In the present study, PCR enabled identification of ETEC K99 and Salmonella spp. in neonatal calves. Although there are available many diagnostic tests, PCR was found specific and convenient for large-scale screening of ETEC K99 (Franck et al., 1998;Cesaris et al., 2007). To the best of our knowledge, this is the first report for molecular screening of ETEC K99 and Salmonella spp. ...
The aim of the present study was to carry out molecular epidemiological investigation on enterotoxigenic Escherichia coli (ETEC) K99 and Salmonella spp. in diarrheic neonatal calves. Fecal samples were obtained from 220 diarrheic calves at 9 farms related to four governorates in central and northern Egypt. E. coli and Salmonella spp. isolates were examined for E. coli K99 and Salmonella spp. using PCR. ETEC K99 was recovered from 20 (10.36 %) out of 193 isolates, whereas Salmonella spp. was recovered from nine calves (4.09%). Multivariable logistic regression was used to evaluate the risk factors associated with both infections. ETEC K99 was significantly affected by age (P<0.01; OR: 1.812; CI 95%: 0.566-1.769), colostrum feeding practice (P<0.01; OR: 5.525; CI 95%: 2.025-15.076), rotavirus infection (P<0.001; OR: 2.220; CI 95%: 0.273-1.251), vaccination of pregnant dams with combined vaccine against rotavirus, coronavirus and E. coli (K99) (P<0.001; OR: 4.753; CI 95%: 2.124-10.641), and vitamin E and selenium administration to the pregnant dam (P<0.01; OR: 3.933; CI 95%: 0.703-1.248). Infection with Salmonella spp. was found to be significantly affected by the animal age (P<0.05; OR: 0.376; CI 95%: 0.511-1.369), Hygiene (P<0.05; OR: 0.628; CI 95%: 1.729-5.612), and region (P<0. 01; OR: 0.970; CI 95%: 0.841-1.624). The results of the present study indicate the importance of PCR as rapid, effective and reliable tool for screening of ETEC and Salmonella spp. when confronted with cases of undifferentiated calf diarrhea. Moreover, identification of the risk factors associated with the spreading of bacteria causing diarrhea may be helpful for construction of suitable methods for prevention and control.
