Linking perturbations to temporal changes in diversity, stability, and compositions of neonatal calf gut microbiota: prediction of diarrhea

Abstract

Perturbations in early life gut microbiota can have long-term impacts on host health. In this study, we investigated antimicrobial-induced temporal changes in diversity, stability, and compositions of gut microbiota in neonatal veal calves, with the objective of identifying microbial markers that predict diarrhea. A total of 220 samples from 63 calves in first 8 weeks of life were used in this study. The results suggest that increase in diversity and stability of gut microbiota over time was a feature of "healthy" (non-diarrheic) calves during early life. Therapeutic antimicrobials delayed the temporal development of diversity and taxa-function robustness (a measure of microbial stability). In addition, predicted genes associated with beta lactam and cationic antimicrobial peptide resistance were more abundant in gut microbiota of calves treated with therapeutic antimicrobials. Random forest machine learning algorithm revealed that Trueperella, Streptococcus, Dorea, uncultured Lachnospiraceae, Ruminococcus 2, and Erysipelatoclostridium may be key microbial markers that can differentiate "healthy" and "unhealthy" (diarrheic) gut microbiota, as they predicted early life diarrhea with an accuracy of 84.3%. Our findings suggest that diarrhea in veal calves may be predicted by the shift in early life gut microbiota, which may provide an opportunity for early intervention (e.g., prebiotics or probiotics) to improve calf health with reduced usage of antimicrobials.

Fig. 1. Temporal changes of Shannon index in gut microbiota.

a Calves that never exhibited diarrhea (H2H). b Calves that exhibited diarrhea but not treated with Trimidox or Excenel (R2H). c Calves that exhibited diarrhea and treated with Trimidox or Excenel (S2H). Statistical analysis was done by Friedman test and multiple comparisons were performed based on Wilcoxon signed-rank test and subjected to “BH” P value adjustment (*P ≤ 0.05).

Fig. 2. Temporal changes of Bray–Curtis distance between two successive days of age at sampling in gut microbiota.

a Calves that never exhibited diarrhea (H2H); b calves that exhibited diarrhea but not treated with Trimidox or Excenel (R2H); c calves that exhibited diarrhea and treated with Trimidox or Excenel (S2H). Statistical analysis was done by Friedman test and multiple comparisons were performed based on Wilcoxon signed-rank test and subjected to “BH” P value adjustment (*P ≤ 0.05).

Fig. 3. Temporal changes of attenuation and buffering values in gut microbiota.

a Attenuation value of calves that never exhibited diarrhea (H2H). b Attenuation value of calves that exhibited diarrhea but not treated with Trimidox or Excenel (R2H). c Attenuation value of calves that exhibited diarrhea and treated with Trimidox or Excenel (S2H). d Buffering value of calves that never exhibited diarrhea (H2H). e Buffering value of calves that exhibited diarrhea but not treated with Trimidox or Excenel (R2H). f Buffering value of calves that exhibited diarrhea and treated with Trimidox or Excenel (S2H). Statistical analysis was done by Friedman test and multiple comparisons were performed based on Wilcoxon signed-rank test and subjected to “BH” P value adjustment (*P ≤ 0.05).

Fig. 4. Prediction of health status based on fecal microbial markers.

a The relationship between the contributions of the six most discriminant bacterial genera to classification of microbiota as well as their relative abundance. b Accuracy of the random forest model established based on the six most discriminant bacterial genera for discriminating “healthy” (n = 39) and “unhealthy” (n = 59) microbiota. High indicates a probability above 0.50, and low indicates a probability below 0.50.

Fig. 5. The ranks of bacterial genera between “healthy” and “unhealthy” gut microbiota estimated using multinomial regression.

The y-axis represents the log-fold change that is known up to some bias constant K, and the x-axis numerically orders the ranks of each genus in the analysis. Truperella is postively associated with 'unhealthy' microbiota while Erysipelatoclostridium, Ruminococcus 2, Streptococcus, Dorea, and uncultured Lachnospiraceae are positively associated with 'healthy' microbiota.