The aims of this study were to determine the ability of AFLP to differentiate Salmonella
isolates from different units of swine production and to demonstrate the relatedness of Salmonella
between farms and abattoirs by AFLP. Many methods have been developed to differentiate isolates of a particular bacterial species, and many publications have catalogued the ability of these methods to differentiate these organisms. However, molecular epidemiology requires not just the ability to differentiate but also the ability to associate “different isolates” with meaningful epidemiological outcomes. Such knowledge is critical to our understanding of the ecology of microorganisms and the epidemiology of disease. An example of this idea is the use of PFGE to identify food source contamination in salmonellosis outbreaks. The ability of PFGE to identify pathogens in contaminated food sources with those from human cases within the same outbreak and to separate distinct outbreaks is the basis of the usefulness of this method. Interestingly, the employment of PFGE in outbreak investigations is currently being scrutinized. In the multistate Salmonella
Enteritidis outbreak related to shell eggs in 2010, PFGE was not sufficiently discriminatory for the outbreak because Salmonella
isolates that appeared to have no epidemiological link to the outbreak had similar PFGE patterns (5
). To determine the preharvest source of Salmonella
contamination and assess the impact of interventions in the context of swine production, the application of an inappropriate genotyping method could produce a similar issue. Previous studies have shown that some methods of differentiating Salmonella
are unable to associate Salmonella
isolates with the epidemiological units of concern in swine production (8
). For example, serotyping and MLST were not useful in attributing S
. Typhimurium or a sequence type found at the abattoir to a particular farm, because the serotype or sequence type could be found in most of the farms (8
). Therefore, it is necessary to attribute Salmonella
contamination at the abattoir to the farm origin by adopting an appropriate genotyping method that is able to associate the unit of differentiation with the unit of production.
The AMOVA for isolates from farm feces used in this study indicated that AFLP was able to differentiate Salmonella
populations originating from multiple swine farms. The ability to differentiate between farms might make it possible to attribute Salmonella
contamination at the abattoir back to a particular farm, therefore pinpointing such a farm(s) as a candidate(s) for intervention programs to control preharvest Salmonella
. An important prerequisite of the food attribution model from human salmonellosis to Salmonella
-contaminated food origins is that Salmonella
isolates from different contaminated food sources can be distinguished by the subtyping method (14
). Similarly, the assumption of the farm attribution model from contaminated abattoirs to farm origins with Salmonella
infection requires all farms to be distinguished. AFLP was demonstrated by AMOVA in this study as a useful tool to distinguish Salmonella
isolates from different farms, which paves the way for building a Salmonella
abattoir-to-farm attribution model.
However, the AMOVA for isolates from mesenteric lymph nodes indicated that AFLP was not able to distinguish Salmonella
populations at the abattoir that originated from pigs from different swine farms. This observation is consistent with our expectation and is likely explained by the commingling effect in lairage pens. Among pigs at the end of the finishing period, 5 to 30% might still excrete Salmonella
, and this percentage might increase due to transport stress (3
). Due to the cross-contamination in lairage pens between pigs from different farms, the Salmonella
population in lairage pens potentially represented a sampling of all farms for that day/week. In addition, after holding in lairage pens for 2 to 4 h, mesenteric lymph nodes could have been transiently infected by the Salmonella
). Therefore, although the pigs were from different farms, the Salmonella
isolates of mesenteric lymph nodes from those pigs may represent the Salmonella
population in lairage pens, not their original farm. The commingling effect likely masked the level of between-farm heterogeneity; therefore, AFLP could not differentiate between mesenteric lymph node isolates from different farms.
Our data suggested that the harvest cohort is not a significant factor in explaining genetic variations. This is perhaps not surprising, as pigs from the same farm have the same management factors, such as diet, herd health status, and stocking density. These farm-related factors are likely to be more strongly associated with Salmonella than cohort-level experiences, such as the season in which an animal is raised and concurrent disease status of the cohort.
This study suggests that AFLP is able to differentiate isolates of Salmonella at the pig level; i.e., AFLP identified 220 types (based on a 100% similarity threshold level) among 220 Salmonella isolates. Compared to serotyping and MLST, the issue is “too much” differentiation rather than too little. The AMOVA partitioned the overall diversity of Salmonella isolates into multiple epidemiological units of concern. The variance attributable to “among pigs within harvest cohorts” was three times greater than “among farms.” Both the variance “among pigs within harvest cohorts” and “among farms” was a significant factor in explaining genetic diversity, while the variance “among harvest cohorts within farms” did not attain the significance level of 0.05. Using AFLP as a differentiation method, these results suggest that the genetic diversity of a Salmonella population on a farm is relatively small across cohorts harvested within a relatively short period, such as within the time period of the present study. AFLP could potentially be used to differentiate between farms, but there would be substantial pig-level noise, and many pig-level samples would be needed for a particular farm to account for between-pig variation. Therefore, a large number of fecal samples would be required to identify enough Salmonella isolates to capture the genotypic distribution of the Salmonella population on a particular swine farm.
The permutation test for epidemiological relatedness indicated that AFLP could be used to identify the flowthrough contamination from farms to abattoirs. The contaminated carcasses at the abattoir can be attributed to infected pigs (flowthrough contamination) as well as healthy but later cross-infected pigs prior to harvest. Previous studies have documented that transportation via trucks and holding in lairage pens were major sources for Salmonella
contamination after pigs leave swine farms (19
). Therefore, Salmonella
isolated at the abattoir can arise partly from the original pigs on the farms but also from other pigs from different cohorts or farms. Following this theory, the observed test statistic should be in the middle part of the permutation test statistic distribution, which suggests no genetic relationship between the on-farm and at-abattoir isolates originating from pigs of the same farm. However, the permutation test results indicated that Salmonella
isolates from farms and abattoirs originating from the same farm were genetically correlated. The genetic relatedness implies that the introduction of external source isolates from truck or lairage transit infection was not sufficient to block the genetic link from farms to abattoirs, which makes it possible to trace the postharvest Salmonella
contamination back to a particular farm by using AFLP.
MLST and serotyping methods were not capable of discriminating between epidemiological units of concern. Compared to serotyping and PFGE, MLST has the advantage of being reproducible and easily exchanged between laboratories, but MLST is not clearly associated with an epidemiological unit of concern. Others have reported the use of MLST in Salmonella
from swine, for example, a total of 110 S. enterica
isolates were typed using the seven-gene MLST scheme (as used in this study) and 43 STs were identified (22
). However, the epidemiological origins of the human and veterinary isolates were not presented, and the level of epidemiological unit that MLST could differentiate was not explored. For example, it was not reported whether the MLST method could differentiate among human and veterinary isolates from different outbreaks (22
). Similarly, Fakhr et al. (8
) conducted MLST (4-gene scheme: manB
, and spaM
) for the genetic discrimination of 85 S
. Typhimurium isolates and found no genetic diversity among the isolates tested, with 100% identity to the sequence reported in GenBank for the S
. Typhimurium LT 2 strain, and obviously no link to an epidemiological unit was reported. The limited discrimination of MLST between closely related isolates may be due to a relatively small part of the genome being used in an MLST investigation, as well as to a moderate-to-slow mutation rate within the targeted housekeeping genes (15
In this study, one of our interests was the apportionment of total variation to different sources. However, a negative estimate of the variance component of the farm level was observed from the R output of AMOVA when applied to the isolates from mesenteric lymph nodes. One possible reason for the negative variance component of the farm level might be that the variability among cohorts was too large. The expected variation among farms was calculated as the sum of the expected variation among cohorts and the product of the variance component of farm level and appropriate degrees of freedom. Therefore, a negative variance component of farm level could occur when the observed variation of “among harvest cohorts within farms” is greater than “among farms.” One explanation for the greater “among harvest cohorts within farms” variation when applied to the mesenteric lymph node isolates is that Salmonella
contamination of a harvest cohort at the abattoir has influences from both farms and abattoir lairage pens. As discussed previously, the lairage pens potentially represent a sampling of all farms for that day/week. Salmonella
can be isolated from pig mesenteric lymph nodes 2 h after the animals become exposed to a Salmonella
-contaminated environment (18
). Therefore, Salmonella
isolates from the pigs of a harvest cohort at the abattoir can potentially originate from different farms. In addition, the lack of enough numbers of cohorts per farm might be the reason for the large variance estimation for “among cohorts.” We conducted an ad hoc
solution to fix the variance component of farm level as zero. The restriction was reasonable, because the P
value (0.82) showed the farm-level variance component was not significantly different from zero.
In this study, the error of the AMOVA model was variance between pigs. We identified one Salmonella isolate per pig, which ignored the variation among isolates within pigs. It is possible that different genotypes from multiple Salmonella isolates occur in a single pig. However, because our aim was to find a tool useful for Salmonella preharvest intervention and such an intervention at the farm or harvest cohort level is more relevant, it might not be necessary to be concerned about the isolate-isolate variation below the pig level.
Overall, the findings of this study have important implications. First, AFLP could differentiate Salmonella isolates between the epidemiological unit farms, but not the harvest cohorts within farms. Second, AFLP could link the abattoir contamination to the farm origin. Finally, there is substantial pig-to-pig genotypic variation. Although using AFLP to subtype Salmonella isolates on a swine farm might be able to capture the genetic character of the Salmonella population on that swine farm, practical application of AFLP for molecular epidemiology in the market chain requires large sample sizes per source farm.