This review summarizes the key results of recently published studies on the effects of dietary change and nutritional intervention on the human microbiome from around the world, focusing on the USA, Canada, Europe, Asia, and Africa. It first explores mechanisms that might explain the ability of fiber-rich foods to suppress the incidence and mortality from westernized diseases, notably cancers of the colon, breast, liver, cardiovascular, infectious, and respiratory diseases, diabetes,

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Photoperiod is an important factor in stimulating broiler performance in commercial poultry practice. However, the mechanism by which photoperiod affects the performance of broiler chickens has not been adequately explored. The current study evaluated the effects of three different photoperiod regimes (short day (LD) = 8 h light, control (CTR) = 12.5 h light, and long day (SD) = 16 h light) on the cecal microbiota of broiler roosters by sequencing bacterial 16S rRNA genes. At the phylum level, the dominant bacteria were Firmicutes (CTR: 68%, SD: 69%, LD: 67%) and Bacteroidetes (CTR: 25%, SD: 26%, and LD: 28%). There was a greater abundance of Proteobacteria (p < 0.01) and Cyanobacteria (p < 0.05) in chickens in the LD group than in those in the CTR group. A significantly greater abundance of Actinobacteria was observed in CTR chickens than in SD and LD chickens (p < 0.01). The abundance of Deferribacteres was significantly higher in LD chickens than in SD chickens (p < 0.01). Fusobacteria and Proteobacteria were more abundant in SD chickens than in CTR and LD chickens. The predicted functional properties indicate that cellular processes may be influenced by photoperiod. Conversely, carbohydrate metabolism was enhanced in CTR chickens as compared to that in SD and LD chickens. The current results indicate that different photoperiod regimes may influence the abundance of specific bacterial populations and then contribute to differences in the functional properties of gut microbiota of broiler roosters.

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Characterization of bovine vaginal microbial genetic diversity can lead to better understanding of cattle physiology, which is of economic interest in improving cattle reproduction health. However, there is limited knowledge on the factors affecting the microbial community harbored in the vaginal tract. The aim of the current study is to evaluate and compare the microbial genetic diversity and abundance between the Holstein and Fleckvieh cattle using metagenomic approach. The main bacterial phyla found were firmicutes (51.70-58.80%), proteobacteria (16.70-11.70%) and bacteroidetes (13.20-19.50%) for Holstein and Fleckvieh. Notable significant difference (p < 0.05) was observed for Fusobacteria, which was more abundant in Holstein (9.73-0.81%). At the achaea phyla, Euryarchaeota was significantly different (p < 0.05) and was more abundant in Fleckvieh than in Holstein cattle (0.86-0.22%). At the genus level, Turicibacter, Ruminococcaceae_UCG-010, Clostridium_sensu_stricto_1, Dorea, Methanobrevibacter, Acetitomaculum, Ruminococcaceae_NK4A214_group and Ruminiclostridium_5 were found to be significantly (p < 0.05) different between the two cattle breeds and all of them were more abundant in Fleckvieh than in Holstein cattle. The current results indicate that the breed can influence bovine vaginal microbial abundance and diversity. Our study characterized and provided pivotal scientific knowledge to understand the microbial community that harbors the bovine virginal flora.

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Milk production accounts for about 60% of the variation in weaning weight and is therefore considered an economically important trait in beef production. However, milk production data is not routinely available in beef improvement programmes and therefore weaning weight is used as a proxy for milk production. Despite the importance of milk production in beef cattle, little research has been done to evaluate the milk production potential of South African indigenous beef cattle. The objective of this study was to estimate average lactation curve parameters for the South African Bonsmara and Nguni cattle. Milk yield was estimated using the weigh-suckle-weigh technique. Lactation curves were modelled using a nonlinear form of the incomplete gamma function (Wood function): Y t = at b exp -ct. Estimates of the a, b and c parameters were 4.095 ± 0.808, 0.274 ± 0.063 and 0.005 ± 0.001 for the Bonsmara, respectively. Corresponding estimates for the Nguni were 1.869 ± 1.527, 0.451 ± 0.242 and 0.008 ± 0.003. Peak lactation time was estimated to be 59 days in Bonsmara and 54 days in Nguni. Estimates of peak yields were 10 kg and 7 kg for the Bonsmara and Nguni, respectively. Estimates of daily milk yield obtained in the current study provide useful baseline information for more accurate modelling of South African beef production systems.