Designing a functional rice muffin formulated with prebiotic oligosaccharides and sugar reduction

The present study aims to evaluate the effects of an infant formula supplemented with a mixture of prebiotic short and long chain inulin-type oligosaccharides on health outcomes, safety and tolerance, as well as on fecal microbiota composition during the first year of life. In a prospective, multicenter, randomized, double-blind study, n = 160 healthy term infants under 4 months of age were randomized to receive either an infant formula enriched with 0.8 g/dL of Orafti®Synergy1 or an unsupplemented control formula until the age of 12 months. Growth, fever (>38 °C) and infections were regularly followed up by a pediatrician. Digestive symptoms, stool consistency as well as crying and sleeping patterns were recorded during one week each study month. Fecal microbiota and immunological biomarkers were determined from a subgroup of infants after 2, 6 and 12 months of life. The intention to treat (ITT) population consisted of n = 149 infants. Both formulae were well tolerated. Mean duration of infections was significantly lower in the prebiotic fed infants (p < 0.05). The prebiotic group showed higher Bifidobacterium counts at month 6 (p = 0.006), and higher proportions of Bifidobacterium in relation to total bacteria at month 2 and 6 (p = 0.042 and p = 0.013, respectively). Stools of infants receiving the prebiotic formula were softer (p < 0.05). Orafti®Synergy1 tended to beneficially impact total daily amount of crying (p = 0.0594). Supplementation with inulin-type prebiotic oligosaccharides during the first year of life beneficially modulates the infant gut microbiota towards higher Bifidobacterium levels at the first 6 months of life, and is associated with reduced duration of infections.

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A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

Microorganisms ◽

10.3390/microorganisms9051034 ◽

2021 ◽

Vol 9 (5) ◽

pp. 1034

Author(s):

Carlos Sabater ◽

Lorena Ruiz ◽

Abelardo Margolles

Keyword(s):

Machine Learning ◽

Supervised Classification ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Human Milk Oligosaccharides ◽

Future Studies ◽

High Fiber ◽

Machine Learning Approach ◽

Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

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