Selected culturable enteric bacterial populations are modified by diet acidification and the growth promotant Tylosin.

AIMS: To determine the effect of diet acidification and an in-feed antibiotic growth promotant (Tylosin, Ty) on selected culturable bacterial populations in the gastrointestinal tract (GIT) of mice. METHODS AND RESULTS: Female C57Bl mice were given a standard diet supplemented with Acid Pak (AP) or Ty in the drinking water. After 21 days, lumen and adherent populations of Enterobacteriaceae, enterococci/streptococci, and lactic acid bacteria (LAB) from the ileum, caecum, colon and faeces were enumerated. General intestinal health was assessed by the frequency of haemolytic bacteria in the different intestinal compartments. Contrary to expectations, AP and Ty significantly increased haemolytic bacteria in the lumen of the caecum and colon (P<0.05). The small but significant growth-enhancing effect of Ty (P<0.05) was associated with decreases in enterococci/streptococci and surprisingly, LAB, as well as increases in coliforms. AP, which failed to improve growth rates, reduced coliforms, had limited effects on enterococci/streptococci, and specifically failed to promote the growth of LAB populations in all intestinal compartments. Ty supplementation was also associated with a significant increase in macrolide-resistant enterococci throughout the GIT. CONCLUSIONS: Dietary acidification is less effective than Ty in modulating the population dynamics of selected culturable populations of enteric bacteria. SIGNIFICANCE AND IMPACT OF THE STUDY: The mouse can provide a useful experimental model to examine the effects of new dietary supplements, formulations or regimes on changes in microbial population dynamics, including monitoring for antibiotic resistance.

Use of a generalized linear mixed model to reduce excessive heterogeneity in petroleum spray oil bioassay data.

High heterogeneity (variance) is a consistent and significant problem in petroleum spray oil derived bioassay data. It can mask small statistical differences sought by researchers in relative toxicity or potency analysis. To compensate for excessive heterogeneity, researchers often use very large sample sizes to improve statistical accuracy. We present a statistical method of modeling heterogeneity extending the conventional probit model by adding random effects to it. We illustrate this by reanalyzing 26 of our own published experiments. Twelve of these had excessive heterogeneity that was significantly reduced in ten cases by including random replicate effects with or without random slopes. Five were further improved by allowing a nonlinear (spline) response. The result was tighter confidence intervals for the estimates of lethal dose.