Post‐natal development of the porcine microbiota composition and activities

Abstract

The current study describes the development of the porcine microbiota and its metabolic activities during the neonatal and weaning period. Using 16S rRNA-based approaches, we first analysed the ileal and colonic microbiota of neonatal piglets at days 2, 5 and 12 after birth. To further investigate the effect of weaning at 3 weeks of age, 19-day-old piglets (n = 64) were randomly allocated into two groups. Half of the piglets remained with their sows throughout the study, while the remaining piglets were weaned. As revealed by sequence analysis of 16S rRNA gene amplicons, the samples of 2-day-old piglets harboured a consortium of bacteria related to Escherichia coli, Shigella flexneri, Lactobacillus sobrius, Lactobacillus reuteri and Lactobacillus acidophilus. Moreover, species-specific real-time polymerase chain reaction assays unveiled that L. sobrius and L. reuteri predominated in the ileal samples of the neonatal and unweaned piglets with population levels up to 7 x 10(8) cells per gram of lumen content. Following weaning, however, these two lactobacilli were detected at significantly lower levels (< 10(3)) in the ileal samples. Furthermore, a shift in composition and metabolic activities of the predominant microbiota, and emergence of clostridia and E. coli, were encountered in the intestinal samples of the piglets after the early post-weaning period.

The immediate environment during postnatal development has long-term impact on gut community structure in pigs

“…In mammals, diet (intake of breast milk followed by weaning) and its impact on the microbial community structure is known to change with time. For example, large differences in the microbiota of breastfed human infants compared to formula-fed infants have been reported (Harmsen et al, 2000) and differences in the porcine gut community associated with weaning (Konstantinov et al, 2006). However, later in life, changes to the diet produce only temporary or no apparent change to community structure in adults (Mai et al, 2004;Tannock et al, 2004).…”

“…It is, therefore, important that colonization processes are better understood to determine which factors have long-term consequences for gut community structure. Some studies have looked at the composition of microbiota at distinct ages (Konstantinov et al, 2006) but very few studies have looked in any detail at community dynamics during the establishment of an individual's intestinal microbiota.…”

Hindgut fermentation in pigs induced by diets with different sources of starch

“…The L. sobrius/amylovorus is a bacterial species capable to degrade starch, that has been observed in abundant concentration in the intestinal tract or faeces of piglets and adult pigs, in a range between 5 and 9 log 10 cells per g (Konstantinov et al, 2006;Marti et al, 2010), constituting up to 80% of total lactobacilli. A typical feature of this species is its ability to utilize starch, producing lactic acid (Du Toit et al, 2001), and therefore it was chosen as a target amylolytic species to compare changes in the caecal ecosystem induced by the experimental diets.…”

“…(Bird et al, 2007;Regmi et al, 2011). Recently, Lactobacillus sobrius/amylovorus has been described as a major amylolytic species for the pig digestive microbiota (Konstantinov et al, 2006;Marti et al, 2010). It has been hypothesised that diets with starch sources differing in starch composition may promote different nutrient levels reaching the hindgut of growing pigs, thus affecting environment and microbial fermentation.…”

Molecular characterization of mesophilic and thermophilic sulfate reducing microbial communities in expanded granular sludge bed (EGSB) reactors

“…DGGE analysis DGGE was performed using gradients of 35-55% and 30-50% for bacterial and archaeal amplicons, respectively (Ben-Amor et al 2005;Roest et al 2005b). Gels were stained using silver nitrate, scanned at 400 dpi and analyzed with BioNumerics, v. 4.0 software package (Applied Maths, SintMartens-Latem, Belgium) as previously described to calculate similarity indices and moving window correlations using the Pearson product-movement correlation (Konstantinov et al 2006). …”