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IntroductionStudies demonstrate that time-restricted feeding (TRF) can regulate gut microbiota composition. However, it is unclear whether TRF could affect the gut microbial rhythmicity in growing pigs. Therefore, the present study aimed to explore the effects of TRF on the dynamic fluctuation of the gut microbiota.MethodsA total of 10 healthy growing pigs equipped with T cannula were employed. Pigs were randomly allotted to the free access (FA) and the TRF groups with 5 replicates (1 pig/replicates). Pigs in the FA group were fed free access during the whole experimental period, whereas pigs in the TRF group were fed free access three times per day within limited times (7:00–8:00, 12:00–13:00, 17:00–18:00). The experiment lasted for 15 days, at 06:00 a.m. of the day 16, colonic digesta were collected at a 6-h interval for consecutive 24 h marked as T06 (06:00), T12 (12:00), T18 (18:00), T24 (24:00), T30 (06:00), respectively.ResultsResults showed that TRF altered the distribution of feed intake without changing the total feed intake within a day (p = 0.870). TRF decreased the overall concentration of colonic cellulose and altered their oscillating patterns. All alpha-diversity indexes of different time points showed significant differences regardless of feeding pattern with a trough at T18 or T24. TRF shifted the trough of the alpha-diversity index Simpson and Invsimpson. TRF lost the rhythmicity of Prevotellaceae, Ruminococcaceae, Bacteroidales_S24-7_group, and Peptococcaceae and gained the rhythmicity of Pasteurellaceae, Clostridiaceae_1, Veillonellaceae, and Peptostreptococcaceae. Also, TRF altered the interaction pattern by increasing the microbes involved in the co-occurrence network and their crosstalk, especially at T24. Interestingly, the microbial variation at T24 could largely explained by colonic substrates starch (R2 = 0.369; p = 0.001), cellulose (R2 = 0.235; p = 0.009) and NH4-N (R2 = 0.489; p = 0.001).ConclusionIn conclusion, TRF has changed the concentrates of cellulose and the relative abundance of specific microbes and certain microbial metabolites. In addition, TRF has more powerful effects on the fluctuation modes of these nutrient substrates, microbes, and metabolites by shifting their peaks or troughs. This knowledge facilitates the development of precision regulation targeting gut microbial rhythmicity.

Additional file 2: Fig. S1. Pairwise comparison of (a) alpha diversity (Richness index) and (b) beta diversity (Bray-Curtis) among the GIT regions at the species levels of gene catalog in the forage-based (F) or grain-based (G) diets, respectively. Significance based on the relative index of each cohort according to the Wilcoxon rank-sum test. *p < 0.05, **p < 0.01, ***p < 0.001. FS, four-chambered stomach; SI, small intestine; LI, large intestine. Fig. S2. The dominant phyla in the GIT of the bacterial, archaeal, and eukaryotic communities in dairy cattle. Firm, Firmicutes; Bact, Bacteroidetes; Prot, Proteobacteria; Fibr, Fibrobacteres; Spir, Spirochaetes; Eury, Euryarchaeota; Nema, Nematoda; Chyt, Chytridiomycota. RUM, rumen; RET, reticulum; OMA, omasum; ABO, abomasum; DUO, duodenum; JEJ, jejunum; ILE, ileum; CEC, cecum; COL, colon; REC, rectum. Bacterial phyla are colored in blue, archaeal phyla are colored in green, and eukaryotic phyla are colored in red. Fig. S3. Pairwise comparison of the fermentation parameters among the FS, SI, and LI cohorts. Significance based on the relative index of each cohort according to the Wilcoxon rank-sum test. *p < 0.05, **p < 0.01, ***p < 0.001. FS, four-chambered stomach; SI, small intestine; LI, large intestine. Fig. S4. (a) Frequencies of phyla, classes, orders, families, and genera among the 1904 SGBs. The five most frequently observed taxa of each rank are shown in the legend, with the remainder grouped as ‘others’ and ‘unclassified’. (b) PCoA plot of 1904 SGBs among 10 GIT regions, with the color of circles indicating regions. RUM, rumen; RET, reticulum; OMA, omasum; ABO, abomasum; DUO, duodenum; JEJ, jejunum; ILE, ileum; CEC, cecum; COL, colon; REC, rectum. Fig. S5. Schematic representation of predicted PULs in targeted Vibrio cholerae RC9 spp. (SGB200, SGB9, SGB627, and SGB357). Fig. S6. Comparison of the fermentation parameters between the forage-based (F) and grain-based (G) diets from the proximal to distal GIT. Significance based on the relative index of each cohort according to the Wilcoxon rank-sum test. *p < 0.05, **p < 0.01, ***p < 0.001. RUM, rumen; RET, reticulum; OMA, omasum; ABO, abomasum; DUO, duodenum; JEJ, jejunum; ILE, ileum; CEC, cecum; COL, colon; REC, rectum. Fig. S7. The numbers of increased and decreased abundance of KOs between the forage-based (F) and grain-based (G) diets in each GIT region. RUM, rumen; RET, reticulum; OMA, omasum; ABO, abomasum; DUO, duodenum; JEJ, jejunum; ILE, ileum; CEC, cecum; COL, colon; REC, rectum. Table S26. Ingredients and nutritional compositions of the forage-based (F) and grain-based (G) diets.

The study investigated the use of dried goat rumen contents (DGRC) on growth performance of broiler chickens. Rumen contents were obtained from goats immediately after slaughter during the wet season, sundried, milled and incorporated in experimental diets at levels of 0, 5 and 10%. The 0% DGRC diet was the control. The experimental diets were formulated on iso-caloric and iso-nitrogenous principles in line with the nutritional requirements for growing broiler birds. Experimental birds were first fed on a common starter broiler diet comprising of 21% CP and 3100 Kcal/kg feed from 0 to 21 days of age; thereafter the birds (21-42 days) were allotted to the experimental treatments in a completely randomized design (CRD) with three replications. A cage with 10 birds was the experimental unit. Experimental diets were offered in the morning and evening, water was provided ad lib. Feed offered and leftovers were weighed daily, and body weight changes were recorded on a weekly basis. The results showed that birds on the 5% diet had significantly (Linear, Quadratic P

Additional file 1: Table S1. Dietary composition and nutrient levels. Table S2. Primer sequences are used for real-time PCR analysis. Table S3. KEGG pathway enrichment of DEGs.

Additional file 1: Table S1. Primer sequences for quantitative real-time PCR analysis

Additional file 1.

Additional file 1: Fig. S1. The biosynthesis pathway of thiamine. Fig. S2. The biosynthesis pathway of riboflavin. Fig. S3. The biosynthesis pathway of niacin. Fig. S4. The biosynthesis pathway of pantothenate. Fig. S5. The biosynthesis pathway of pyridoxine. Fig. S6. The biosynthesis pathway of biotin. Fig. S7. The biosynthesis pathway of folate. Fig. S8. The biosynthesis pathway of cobalamin. Fig. S9. The biosynthesis pathway of menaquinone. Fig. S10. Variability in differences of vitamin biosynthesis explained by regions and species. Fig. S11. Chord plot of the distribution of vitamin biosynthetic genes among different phyla. Fig. S12. Phylogenetic distribution of vitamin biosynthetic genes at the genus level throughout the GIT regions. Fig. S13. The important indicators for regional heterogeneity identified by Random Forest model. Fig. S14. The detection of causal interactions between structure of GIT microbiota and vitamin biosynthesis pathway. Fig. S15. Vitamin synthesis capabilities of 31 genomes assigned to the genera Salmonella and Escherichia. Fig. S16. Variability in differences of vitamin biosynthesis explained by diets. Fig. S17. The detection of causal interactions between Bacteroides and Fibrobacter and cobalamin biosynthesis. Fig. S18. The important indicators for dietary shifts identified by Random Forest model.

Additional file 1. The changed phyla in the dietary fiber groups compared to the CON group. CON, a control diet; RPS, a raw potato starch enriched diet; INU, an inulin enriched diet; PEC, a pectin enriched diet.

Additional file 1: Fig. S1. Computational pipeline for gene catalog and assembly of MAGs. Fig. S2. DNA contamination statistics. Fig. S3. Assembly statistics. Fig. S4. Comparison of the RGMGC to the public datasets. Fig. S5. Coverage of the RGMGC. Fig. S6. Comparison of the dominant microbial taxa at the genus level among GIT regions. Fig. S7. Functional structure of the GIT microbiome. Fig. S8. Gene diversity in microbial communities across the ruminant GIT. Fig. S9. CheckM quality assessment. Fig. S10. Distribution of 10,373 genomes across the ruminant GIT. Fig. S11. Variations in enrichment of MAGs among GIT regions. Fig. S12. Comparative analysis of genomes of the CAG-110 genus. Fig. S13. Species-level clustering of reference genomes and MAGs. Fig. S14. Distribution of the 8,745 USGs across the ruminant GIT. Fig. S15. Taxonomic composition of the 8,745 USGs. Fig. S16. Biosynthetic gene clusters found in the human gut species. Fig. S17. Differences in GP profiles between the USGs and RCGs. Fig. S18. Comparison of the USGs and RCGs in the prevalent phyla. Fig. S19. Phylogenetic tree of the 194 proteobacteria genomes. Fig. S20. Phylogenetic tree of mutualistic archaea. Fig. S21. Distributions of hydrogenases and associated terminal reductases in the 10,373 MAGs. Fig. S22. Associations of GIT microbial species with cattle feed efficiency (FE).

Additional file 2. The CAZymes changes in the dietary fiber groups compared to the CON group. CON, a control diet; RPS, a raw potato starch enriched diet; INU, an inulin enriched diet; PEC, a pectin enriched diet.

Additional file 4. Composition and analyzed nutrient contents of experimental diets (as-fed basis).

Additional file 3. The network of correlations between bacteria and CAZyme classes changed significantly by the dietary fiber. Green lines represent positive correlations and red lines represent negative correlations

Additional file 1: Table S1. Primer sequences for quantitative real-time PCR analysis.

The study aimed to investigate the effects of gynosaponin on in vitro methanogenesis under different forage-concentrate ratios (F:C ratios). Experiment was conducted with two kinds of F:C ratios (F:C = 7:3 and F:C = 3:7) and gynosaponin addition (0 mg and 16 mg) in a 2×2 double factorial design. In the presence of gynosaponin, methane production and acetate concentration were significantly decreased, whereas concentration of propionate tended to be increased resulting in a significant reduction (p

A reciprocal cross-fostering model with an obese typical Chinese piglet breed and a lean Western breed was used to identify genetic and maternal effects on the acquisition and development gut bacteria from birth until after weaning. Pyrosequencing of 16S rRNA genes results revealed an age- and diet-dependent bacterial succession process in piglets. During the first 3 days after birth, the bacterial community was relatively simple and dominated by Firmicutes with 79% and 65% relative abundance for Meishan and Yorkshire piglets, respectively. During the suckling period until day 14, the piglet breed and the nursing mother lead to increasing differentiation of the fecal bacterial community, with specific bacteria taxa associated with breed, and others with the nursing sow most likely due to its milk composition. Although the effect of nursing mother and the breed were evident through the suckling period, the introduction of solid feed and subsequent weaning were the major events occurring that dominated succession of the gut microbiota in the early life of piglets. This piglet cross-fostering model is a useful tool for studying the effects of diet, host genetics and the environment on the development and acquisition of the gut microbiota and over longer studies the subsequent impact on growth, health and performance of pigs.