Characterisation of Early-Life Fecal Microbiota in Susceptible and Healthy Pigs to Post-Weaning Diarrhoea.

Early-life microbial exposure is of particular importance to growth, immune system development and long-lasting health. Hence, early microbiota composition is a promising predictive biomarker for health and disease but still remains poorly characterized in regards to susceptibility to diarrhoea. In the present study, we aimed to assess if gut bacterial community diversity and composition during the suckling period were associated with differences in susceptibility of pigs to post-weaning diarrhoea. Twenty piglets from 5 sows (4 piglets / litter) were weaned in poor housing conditions to challenge their susceptibility to post-weaning diarrhoea. Two weeks after weaning, 13 pigs exhibited liquid faeces during 2 or 3 days and were defined as diarrhoeic (D) pigs. The other 7 pigs did not have diarrhea during the whole post-weaning experimental periodand were defined as healthy (H) pigs. Using a molecular characterisation of fecal microbiota with CE-SSCP fingerprint, Next Generation Sequencing and qPCR, we show that D and H pigs were mainly discriminated as early as postnatal day (PND) 7, i.e. 4 weeks before post-weaning diarrhoea occurence. At PND 7 H pigs displayed a lower evenness and a higher abundance of Prevotellaceae, Lachnospiraceae, Ruminocacaceae and Lactobacillaceae compared to D pigs. The sPLS regression method indicates that these bacterial families were strongly correlated to a higher Bacteroidetes abundance observed in PND 30 H pigs one week before diarrhoea. These results emphasize the potential of early microbiota diversity and composition as being an indicator of susceptibility to post-weaning diarrhoea. Furthermore, they support the health promoting strategies of pig herds through gut microbiota engineering.

Resolution of fluorophore mixtures in biological media using fluorescence spectroscopy and Monte Carlo simulation.

In excitation-emission fluorescence spectroscopy, the simultaneous quantitative prediction and qualitative resolution of mixtures of fluorophores using chemometrics is a major challenge because of the scattering and reabsorption effects (turbidity) presented mainly in biomaterials. The measured fluorescence spectra are distorted by multiple scattering and reabsorption events in the surrounding medium, thereby diminishing the performance of the commonly used three-way resolution methods such as parallel factor (PARAFAC) analysis or multivariate curve resolution-alternating least squares (MCR-ALS). In this work we show that spectral loadings and concentration profiles from model mixtures provided using PARAFAC and MCR-ALS are severely distorted by reabsorption and scattering phenomena, although both models fit rather well the experimental data in terms of percentage of the explained variance. The method to correct the fluorescence excitation-emission matrix (EEM) consisted in measuring the optical properties (absorption parameter µa , scattering parameter µs, and anisotropy factor g) of samples and calculating the corresponding transfer function by means of the Monte Carlo simulation method. By applying this transfer function to the measured EEM, it was possible to compensate for reabsorption and scattering effects and to restore the ideal EEM, i.e., the EEM that is due only to fluorophores, without distortions from the absorbers and scatterers that are present. The PARAFAC and MCR-ALS decomposition of the resulting ideal EEMs provided spectral loadings and concentration profiles that matched the true profiles.