Characterization of General E. coli and Salmonella in Pre- and Post-Anaerobically Digested Diary Manure


Anaerobic digestion (AD) speeds up natural degradation of manure during storage, reduces odor, and produces energy by capturing methane. After AD, wastewater can be utilized on farms as a crop fertilizer and irrigation, and solids can be used for animal bedding.

Manure can be environmentally problematic and a reservoir of infectious agents (Guan et al., 2003). Previous studies have shown that anaerobic digestion of dairy manure decreases concentrations of viable fecal bacteria known to cause zoonotic diseases, notably E. coli and Salmonella (Aitken et al., 2007; Frear et al., 2011; Pandey and Soupir, 2011; Manyi-Loh et al., 2014; Chiapetta et al., 2019)

This study’s objective was to characterize and compare genetic changes in pathogens pre- and post-AD as evaluated by metabolic changes (sugar fermentation) or antimicrobial resistance to antibiotics. Generic E. coli (GEC) and Salmonella were selected for evaluation in this study as both are known to cause food borne and zoonotic disease. While a limited number of specific bacteria have been studied, AD has shown efficacy in pathogen reduction for both GEC and Salmonella. Characterizing these bacteria in AD influent and effluent can more firmly establish the efficacy of AD for reducing potential risks to human and animal health posed by these pathogens. We hypothesized that GEC and Salmonella would meet the 75% threshold of genetic similarity (post-AD vs pre-AD), suggesting limited mutation and lowered risk of AD creating resistant strain.

What Did We Do

An anaerobic digester (AD) in Monroe, WA was utilized from December 2008 through March 2010 to assess its effects on the survival and adaptation of pathogens in dairy manure (Chiapetta et al., 2019). The AD was a plug-flow design with a capacity of approximately 6.1million liters that was operated at ~38°C for a 17-day retention time. Inputs to the AD were comprised of 70% dairy cow manure and 30% pre-consumer food wastes from the dairy farm where the AD was located and from local food processors, respectively. Salmonella and general E. coli (GEC) were isolated from samples collected before and after AD. GEC isolates were characterized by sugar fermentation profiles (adonitol, dulcitol, melibiose, raffinose, rhamnose, salicin, sorbose, sucrose and the indicator medias MAC and MUG) and genetically compared using repetitive extragenic palindromic chain reaction (REP-PCR) followed by Ward’s cluster analysis. Salmonella were separated into serogroups. The Kirby Bauer disk diffusion method was used to identify antibiotic resistance (AMR). Antibiotics used were: ampicillin, chloramphenicol, gentamycin, amikacin, kanamycin, sulfamethaxazole/triemthroprim, streptomycin, tetracycline, amoxicillin/clavulanic acid, nalidixic, sulfisoxazole, and ceftazidime.

What Have We Learned

Antibiotic resistant GEC isolates were isolated from 22.3% and 19.1% of pre- and post-AD samples, respectively, and were observed to be genetically similar after clustering for sugar fermentation. Analysis of genetic similarity using the Pearson’s chi square method (e.g. likelihood–ratio) revealed that AD status (pre- vs. post AD) antibiotic resistance was not statistically significantly associated with AD (Figure 1, Table). Any effect of AD on AMR was dependent on grouping based on % genetic similarity.

Genetic analysis (REPPCR for GEC) yielded similar results, following a Pearson’s Chi Square test of log likelihood it was determined that AD status (pre- vs. post AD) and AMR were not significantly associated (Figure 1). Any effect of AD on AMR was dependent on grouping (Table 1).

Salmonella predominant serogroups (Table 2) (B, C1, and E1) remained at 23%, 9%, and 2% AMR pre- and post-AD. Analyses showed a significant interaction between Salmonella serogroup vs. source (p=0.0004) and serogroup vs. AMR (p<0.0001). No interaction was observed between source (pre- or post-AD) and AMR for Salmonella, p=0.12. There was no uniform effect for Salmonella as a group based on AD.

In summary, GEC sampled pre- and post-AD showed no difference in sugar fermentation, nor significant genetic dissimilarity, nor antibiotic resistance. Salmonella serotypes were observed to be equally or inconsistently effected by AD. Overall, the evidence suggests that anaerobic digestion does not create antibiotic resistant GEC and Salmonella.

Figure 1. Dendrogram of the sugar fermentation cluster analysis of generic E. coli. G= group based on sugar fermentation similarity, and n= number of isolates within each group.

Running a Chi Square on that: AD status (pre- vs. post AD) antibiotic resistance was not statistically significantly associated with this set of fermentation cluster memberships.

Pearson chi2(19) = 25.5411 Pr = 0.143

Table 1 – Data distribution of REPPCR GEC data
Pre-AD Post-AD
Grouping Susceptible Resistant Susceptible Resistant
1 2 2 3 (Am*)
2 2 5 (2 – Am, Cf, S, G, Te) (Am, S, Te) (Te)
(Amc, Am, Cf)
1 3 (Cf)
(2 – C, S, G, Te)
3 6
4 5 3 (2 – G, Te)
(Cf, C, S, G, Te)
5 1 2 1 (Amc, Am, Cf,  S, G, Te)

*Am = Ampicillin, C= Chloramphenicol, CF = Ceftiofur, S = Streptomycin, G = Sulfasoxizole, Te = Tetracycline, Amc = Amoxycillin clavulanic acid

(fisher.test(tbl, simulate.p.value = TRUE, B = 1e5)

Fisher’s Exact Test for Count Data with simulated p-value (based on 1e+05 replicates)

p-value = 0.104

If no selection is occurring, output equals input, so at P < 0.1 is a trend for a selective process.

Table 2 – Salmonella – Number of susceptible or resistant bacteria
Serogroup Pre-AD Susceptible Pre-AD Resistant Post-AD Susceptible Post-AD Resistant
B 6 1 1 10
C1 12 4 14 0
C2 1 8 0 0
E1 34 0 50 0
K 4 2 2 2
Total 57 65 29 12
% 47 53 71 29

Configuration 1 SeroGrp*ABResist = best fits – association (interaction) of serogroup and resistance

Configuration 2 SeroGrp*PrePost = best fits – association (interaction) of serogroup and pre- post AD, but is conditioned by whether it is resistant

Goodness-of-fit Summary Statistics

Statistic Chi-Sq DF P
Pearson 6.91 5 0.2276
Likelihood 8.67 5 0.1230
Freeman-Turkey 8.28 5 0.1416

Number of Near Zero Expected Cells     4

Three observations were made:

      • a serotype may become more resistant as it goes through the AD
      • a serotype may become less resistant, or
      • a serotype may not survive.


J. H. Harrison – Livestock Nutrient Management Specialist, Department of Animal Sciences, Washington State University Puyallup Research and Extension Center

Additional Authors

J. Gay – Department of Veterinary Clinical Medicine, Washington State University, Pullman, WA
R. McClannahan – Facility Manager – Integrated Research and Innovation Center – University of Idaho, Moscow, ID
E. Whitefield – Research and Outreach Specialist Department of Animal Sciences, Washington State University Puyallup Research and Extension Center


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