Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-05-22T15:39:28.230Z Has data issue: false hasContentIssue false

The improvement of parturition duration by high intake of dietary fibre in late gestation is associated with gut microbiota and metabolome in sows

Published online by Cambridge University Press:  14 February 2022

Yang Liu
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
Pingping Jiang
Affiliation:
School of Public Health, Sun Yat-sen University, Guangzhou, People’s Republic of China
Nan Chen
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
Yannan Jiang
Affiliation:
School of Public Health, Sun Yat-sen University, Guangzhou, People’s Republic of China
Ruinan Zhang
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
Zhengfeng Fang
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
Yan Lin
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
Shengyu Xu
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
Bin Feng
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
Yong Zhuo
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
De Wu
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
Peter Kappel Theil
Affiliation:
Department of Animal Science, Aarhus University, Tjele, Denmark
Lianqiang Che*
Affiliation:
Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, People’s Republic of China
*
*Corresponding author: Lianqiang Che, email che.lianqiang@sicau.edu.cn
Rights & Permissions [Opens in a new window]

Abstract

Prolonged parturition duration has been widely demonstrated to be a risk factor for incidence of stillbirth. This study evaluated the supply of dietary fibre on the parturition duration, gut microbiota and metabolome using sows as a model. A total of 40 Yorkshire sows were randomly given diet containing normal level of dietary fibre (NDF, 17·5 % dietary fibre) or high level of dietary fibre (HDF, 33·5 % dietary fibre). Faecal microbiota profiled with 16S rRNA amplicon sequencing, SCFA and metabolome in the faeces and plasma around parturition were compared between the dietary groups. Correlation analysis was conducted to further explore the potential associations between specific bacterial taxa and metabolites. Results showed that HDF diet significantly improved the parturition process as presented by the shorter parturition duration. HDF diet increased the abundance of the phyla Bacteroidetes and Synergistetes and multiple genera. Except for butyrate, SCFA levels in the faeces and plasma of sows at parturition were elevated in HDF group. The abundances of fifteen and twelve metabolites in the faeces and plasma, respectively, markedly differ between HDF and NDF sows. These metabolites are involved in energy metabolism and bacterial metabolism. Correlation analysis also showed associations between specific bacteria taxa and metabolites. Collectively, our study indicates that the improvement of parturition duration by high fibre intake in late gestation is associated with gut microbiota, production of SCFA and other metabolites, potentially serving for energy metabolism.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of The Nutrition Society

Intensive genetic selection resulted in gradually increased litter size, thereby prolonged parturition(Reference Oliviero, Heinonen and Valros1). It has been shown that stillbirth rate increases along with longer farrowing duration(Reference Dijk, Rens and Lende2,Reference Gourley, Swanson and Royall3) . Of note, about 75 % of the stillbirths occurred during parturition process due to asphyxia(Reference Leenhouwers, Lende and Knol4). For litter-bearing animals like sows, parturition duration can be rather long. Our previous study has reported that diet enriched with inulin, a fermentable fibre from roots, shortened parturition duration in a sow model(Reference Wang, Zhou and Liu5). Although several hypotheses have been proposed, such as reducing constipation or body fat deposition by high fibre intake(Reference Guillemet, Hamard and Quesnel6), the underlying mechanisms of high fibre intake during gestation on improving parturition duration remain obscure.

The microbial residents of the gut is associated with various physiological aspects of host and largely affected by diet(Reference Koh, Vadder and Kovatcheva7). The intake of dietary fibre had been largely related to the change of composition and function of gut microbiota(Reference Hussain, Dong and Agopian8,Reference Mathilde, Etienne and Olivier9) . The symbiotic metabolites produced by gut microbiota have been proposed to play roles in gut microbiota affecting host physiology(Reference Kumar, Rani and Datt10). Dietary fibres, including NSP, oligosaccharides and resistant starches, are microbiota-accessible carbohydrates and fermented by gut microbiota to produce various symbiotic metabolites(Reference Kumar, Rani and Datt10,Reference Tan, Beltranena and Zijlstra11) . SCFA are one major type of symbiotic metabolites, which decrease the pH in the gut lumen to suppress the pathogenic bacterial, and serving as an energy substrate of the host(Reference Schönfeld and Wojtczak12,Reference Serena, Jørgensen and Knudsen13) . To our knowledge, however, it is unknown that the gut microbiota and symbiotic metabolites, such as SCFA, are involved in the effect of dietary fibre on parturition duration.

In this study, therefore, we aimed to assess whether the improved parturition duration by high intake of dietary fibre is associated with the gut microbiota and related symbiotic metabolites, using sows as a model. The gut microbiota of sows given different level of dietary fibre was profiled by 16S rRNA amplicon sequencing. Levels of SCFA in both faeces and plasma were quantified, and metabolome was profiled by untargeted metabolomics. Abundance of these metabolites and bacterial taxa were correlated to explore potential association.

Materials and methods

All institutional and national guidelines for the care and use of laboratory animals were followed. The animal experiment was approved by the Animal Care and Use Committee of the Sichuan Agricultural University (DKY-B20121602) and was performed in accordance with the National Research Council’s Guidelines for Care and Use of Laboratory Animals.

Animals and dietary intervention

A total of forty Yorkshire sows with similar parity and body weight were randomly allocated into two dietary treatments (twenty sows per treatment) from day 90 of gestation (G90) to parturition. The diets were formulated based on maize–soyabean meal to meet or exceed the nutrient requirements recommended by NRC (2012), as shown in Table S1: normal dietary fibre diet (NDF, 17·5 % total dietary fibre) and high dietary fibre diet (HDF, 33·5% total dietary fibre). The daily gross energy and crude protein intake were comparable between dietary treatments by adjusting daily feed intake (3·0 kg/d in NDF group and 3·2 kg/d in HDF group). Sows in the NDF group consumed dietary fibre at 485 g/d, while sows in the HDF group consumed 964 g/d. All sows were housed in gestation stalls until moved to farrowing room on G110. The body weight and backfat thickness of sows were recorded on G110. The temperature in the farrowing room was kept between 20°C and 22°C. Three sows from NDF group and four sows from HDF group were eliminated due to illness and abnormal litter size (total piglets born is less than 4) and were excluded from analysis. Therefore the final count for sows was seventeen in NDF group and sixteen in HDF group. The litter size was recorded at parturition and did not markedly differ between two groups (data not shown). Parturition duration was defined as the time elapsed from birth of the first-born to the last-born piglet in the litter(Reference Feyera, Pedersen and Theil14) and was recorded accurately during parturition process by timer.

Sample collection

Blood samples (n 9) of sows were collected from the ear vein into Na-heparinised tubes at 1200 h on G107 and at the onset of parturition. Blood samples were centrifuged at 3000 rpm for 15 min, and plasma was harvested and stored immediately at −20°C for later analysis. Fresh faecal samples of sows were collected into sterile cryopreservation tube on G110 and stored immediately in liquid N2 for later analysis.

Faecal microbiota

Genomic DNA was extracted from faecal samples using Mo Bio Power Faecal DNA Isolation Kit (Mo BIO). The v4 hypervariable regions of 16S rRNA was amplified using primers 515F and 806R, and the amplicon pyrosequencing was carried out on an Illumina HiSeq PE250 platform (Illumina). Raw data were processed with RRR pipeline-based UPARSE (version 7.0.1001). Ribosomal Database Project classifier (version 2.2) was used to assign taxonomic rank. Operational taxonomic units (OTUs) were clustered at 97 % sequence identity (sequences with ≥ 97 % similarity were assigned to the same OTU). OTU and sample information were imported into R software interfaced with R Studio for further analysis using the Phyloseq and VEGAN packages. Bray–Curtis and unifrac dissimilarities were calculated to present the β-diversity and displayed by Non-metric Multi-dimensional Scaling (NMDS), and difference between the dietary groups was tested by Permutational Multivariate Analysis of Variance (PERMANOVA) test with permutation 1000 times.

Quantification of SCFA

Concentrations of SCFA (acetate, propionate, butyrate, isobutyrate, valerate and isovalerate) and sum of them (total SCFA) in faecal and plasma samples were determined as previously described(Reference Che, Hu and Zhou15), using GC (Varian CP-3800, manual injection, flame ionisation detector, 10-µl micro-injector). Briefly, supernatant obtained from faecal suspension (0·7 g of faecal matter in 1·5 ml of water) or plasma was mixed with 25 % metaphosphoric acid and crotonic acid solution (210 mmol/l for faecal samples and 21 mmol/l for plasma samples) and centrifuged at 12 000 rpm for 10 min. Then, the supernatant was mixed with methanol (1:3 dilution), centrifuged at 12 000 rpm for 15 min and filtered by 0·22-µm filter (Millipore) before being manually applied onto a gas chromatographer with flame ionisation detector for quantification.

Faecal and plasma metabolomics

Faecal and plasma samples were thawed and mixed with cold methanol/acetonitrile (1:1, v/v) to remove the protein. The supernatant was dried and re-dissolved in 50 % acetonitrile before being applied onto a ultra high performance liquid chromatography (Infinity 1290, Agilent) coupled to a quadrupole time-of-flight mass spectrometer (qTOF MS, 6550, Agilent) for analysis in random order within the same type of samples. ACQUITY BEH amide column (2·1 mm × 100 mm, 1·7 µm, Waters) was used as the separation column and the mobile phase was mixture of solvent A (25 mmol/l CH.3COONH4 and 25 mmol/l NH4OH) and B (acetonitrile). The 12-min elution gradient was starting with 95 % solvent B (acetonitrile) for 0·5 min, decreasing to 60 % of B in 6·5 min in linear fashion, further to 40 % of B in 1 min, holding for 1 min, changing back to 95 % of B in 0·1 min, then holding for 2·9 min for re-equilibration. To assist the chemical assignment, pooled sample was analysed on a TripleTOF MS (AB Sciex 6600) to obtain MS and MS/MS data in an information-dependent acquisition mode. Obtained data were used for compound assignment against an in-house database established with available authentic standards.

Statistical analysis

The variance homogeneity and normality of data for body weight, backfat thickness, parity, gestation length, parturition duration and SCFA concentrations were evaluated by the Shapiro–Wilk test and Levene’s test procedures of SAS 9.4 (SAS Institute, Inc.), respectively. Then, these data were analysed using linear model against the dietary groups. Difference with P < 0·05 was regarded as significant.

For gut microbiota, difference in α-diversity was presented by Shannon index, and differential abundance between the dietary groups of specific genus or phylum was tested by the non-parametric Wilcoxon rank-sum test. P-values indicating significance of difference at genus level were further adjusted by a two-stage Benjamini and Hochberg (TSBH) step-up false discovery rate (FDR)-controlling procedure with type I error rate (α) set to 0·20 using the mt.rawp2adjp function in multcomp package. P-values of phyla were not adjusted as there were less than twenty tests conducted. Effect size was also calculated using equation, effect size = ${Z \over {\sqrt N }}$ , in which Z is the Wilcoxon Z and N is the number of samples.

Raw LC-MS data for untargeted metabolomics were converted into MzXML format using MSconvert (version 3.0.6458, ProteoWizard, Palo Alto), then imported into XCMS (version, Scripps Center) for data pre-processing. Processed data were annotated with chemical compound ID as aforementioned, then imported into R software integrated with R studio for abundance analysis. All the identified ions in the faeces or plasma were combined together and applied to principal component analysis. Each MS feature was fitted to a linear model using lm function with treatment (NDF v. HDF) as the predictor. P-value for the significance of diet was generated by comparing these two models with anova function. P-values were further adjusted within each data set (faeces or plasma) by a two-staged TSBH FDR procedure as described above. Adjusted P-value less than 0·05 was the threshold of significance.

To explore the associations between composition of gut microbiota and abundance of metabolites in faeces or plasma, Spearman’s correlation was applied to the microbiota data and individual SCFA data, whilst unsupervised regularised canonical correlation analysis with the shrinkage method was performed on the microbiota and metabolomic data across the dietary groups using the mixOmics package. Correlations were shown as heatmap with either Spearman’s r or the regularised canonical correlation analysis similarity scores. Pearson’s correlation analyses were applied to further explore potential associations between SCFA concentration and plasma glucose concentration and parturition duration of sows. The data for plasma glucose concentration have been published in our recent paper(Reference Liu, Chen and Che16) and were used for this correlation analysis.

Results

Body condition, parity and gestation length

As shown in Table 1, the body weight and backfat thickness on G110, parity and gestation length did not markedly differ between NDF and HDF sows (P > 0·05).

Table 1. Body condition, parity and gestation length between NDF and HDF sows

(Mean values with the standard errors of the mean)

NDF, normal dietary fibre diet; HDF, high dietary fibre diet.

n 17 for NDF group and n 16 for HDF group.

Parturition duration and mean birth interval

As shown in Fig. 1(a), sows fed NDF diet had longer parturition duration than sows fed HDF diet (303 min v. 229 min in average, P < 0·05). Additionally, the newborn piglets from HDF sows had significantly lower mean birth interval (P < 0·05).

Fig. 1. Parturition duration (a) and mean birth interval (b) between NDF and HDF sows. *P < 0·05. n 17 for NDF group and n 16 for HDF group. NDF, normal dietary fibre diet; HDF, high dietary fibre diet.

Faecal microbiota

The microbiota data cover 16 phyla and were further divided into 114 genera. No significant difference was found between HDF and NDF sows in α-diversity shown as Shannon index (Fig. 2(a)) or in β-diversity shown by Bray–Curtis and unifrac dissimilarity at genus level (Fig. 2(b) and (c)). Firmicutes was the most abundant phylum, followed by Bacteroidetes, Proteobacteria, Spirochaetes and Euryarchaeota. Relative abundance of phylum Firmicutes (P < 0·05) and Actinobacteria (P = 0·09) decreased in HDF sows, relative to NDF sows, while that of Bacteroidetes, Synergistetes (both Ps < 0·05) and Elusimicrobia (P = 0·07) increased (online Supplementary Table S2). Lactobacillus was the most abundant genus with a known ID, followed by Streptococcus, Methanobrevibacter, Parabacteroides and Oscillospira. Multiple genera were found with higher abundance in HDF sows (12/18) (Fig. 2(d)). Information of these genera, including genus name, P-value, adjusted P and effect size, is listed in Table S3. However, none of these significance retained after the FDR correction, but most of them had relatively large effect size (all > 0·5, online Supplementary Table S3).

Fig. 2. α-diversity (a), β-diversity using Bray–Curtis distance (b) and unifrac distance (c) of the gut microbiota, and taxa with differential abundance between HDF and NDF sows (d). NDF, normal dietary fibre diet; HDF, high dietary fibre diet.

Faecal and plasma SCFA concentration

In both faecal samples of sows on G110 and plasma samples of sows at parturition, levels of total SCFA, acetate, propionate, isobutyrate (P = 0·06 in plasma), v.alerate and isovalerate (P = 0·07 in faeces) were higher in HDF sows than those in NDF ones (all Ps < 0·05 unless otherwise stated). No significant difference in level of butyrate was found between HDF and NDF sows in either faecal or plasma samples (P > 0·05). In plasma samples of sows on G107, levels of isobutyric acid (P = 0·09) and valeric acid (P < 0·05) were higher in HDF sows than those in NDF ones, whereas no significant difference in level of total SCFA, acetic acid, propionic acid, butyric acid and isovaleric acid were found between dietary treatments (Table 2).

Table 2. Faecal and plasma SCFA concentrations between NDF and HDF sows

(Mean values with the standard errors of the mean, n 9 for each group)

NDF, normal dietary fibre diet; HDF, high dietary fibre diet.

Faecal data in μmol/g, plasma data in μmol/l. Total SCFA = the sum of acetate, propionate, butyrate, isobutyrate, valerate and isovalerate.

Faecal and plasma metabolomics

In total, 186 metabolites in positive mode and 170 in negative mode were annotated in faeces, while 217 (positive) and 179 (negative) metabolites were found in plasma, respectively. Among these metabolites, fifteen metabolites in faeces and twelve in plasma were found with significant difference (adjusted P < 0·05) between NDF and HDF sows (Table 3). Principal component analysis score plots of faecal and plasma metabolome are shown in Fig. 3. Tendency of separation in both score plots indicates difference between the two groups in both faecal and plasma metabolome. While only nearly half of the faecal metabolites (7/15) showed higher abundance in HDF sows, levels of all identified plasma metabolites were higher in the HDF sows.

Table 3. Faecal and plasma metabolites with differential abundance between NDF and HDF sows

(Mean values with the standard errors of the mean)

NDF, normal dietary fibre diet; HDF, high dietary fibre diet; RT, retention time; M/Z, mass-to-charge ratio.

Results of abundance are shown as mean ± sem.

Fold change refers to abundance (HDF)/abundance (NDF).

Fig. 3. PCA score plots of faecal (a) and plasma (b) metabolome. PCA, principal component analysis; NDF, normal dietary fibre diet; HDF, high dietary fibre diet.

Correlation

Associations between abundance of specific taxa and SCFA in faeces and plasma by Spearman’s correlation analysis, and that metabolites in faeces and plasma by regularised canonical correlation analysis are shown as heatmaps in Figs. 4, 5 and 6, respectively. Across the diet groups, multiple taxa were correlated with SCFA in abundance in faeces and plasma. Specifically, Cellulosilyticum, Angelakisella, Elusimicrobium and Lachnoclostridium were positively correlated with faecal acetate, Alphaproteobacteria(c)_unas and Phascolarctobacterium were positively correlated with faecal propionate, and Blautia, Muribaculaceae(f)_unas and Butyrivibrio were positively correlated with faecal butyrate (Fig. 4(a)). Moreover, Anaerovibrio was positively correlated with plasma acetate, while Sphaerochaeta, Sutterella and Bacteroides were positively correlated with plasma propionate (Fig. 4(b)). Indole metabolites, such as norharmane in faeces (Fig. 5) and imidazoleacetic acid in plasma (Fig. 6), were also correlated with bacterial taxa, including Anaerovibrio, which was also correlated with abundance of betaine. The gut bacterial taxa, such as Cellulosilyticum and Lachnoclostridium, were found associated with intermediate metabolites related to energy metabolism, such as malic acid and 2-keto-gluconic acid as well as acetylcarnitine in plasma (Fig. 6). As shown in Fig. 7, plasma acetate (r = 0·59, P < 0·05) and total SCFA (r = 0·59, P < 0·05) concentrations on G107 were positively correlated with plasma glucose concentration on G107. Moreover, plasma propionate concentration on G107 was positively correlated with parturition duration (r = 0·55, P < 0·05).

Fig. 4. Heatmap of correlation of the gut bacterial taxa and SCFA in faeces (left) and plasma (right). Correlation coefficients were coloured according to the scale listed on the right.

Fig. 5. rCCA of the gut bacterial taxa and metabolites in faeces. rCCA Similarity Scores are coloured according to the scale listed on the right. rCCA, regularised canonical correlation analysis.

Fig. 6. rCCA of the gut bacterial taxa and metabolites in plasma. rCCA Similarity Scores are coloured according to the scale listed on the right. rCCA, regularised canonical correlation analysis.

Fig. 7. Correlation between SCFA and plasma glucose (a) and (b) and parturition duration (c) of sows.

Discussion

During birth, the blood and oxygen supply to the brain of piglets are affected by parturition duration(Reference Langendijk and Plush17), and piglets with a long parturition duration have higher risk of death. Consistent with previous report(Reference Li, Liu and Lyu18), in this study, sows fed HDF diet had a substantial reduction (24 %) in the parturition duration and mean birth interval as compared with NDF ones.

For mammals, energy is of importance for the maternal uterine contractility during parturition. In humans, ATP production of myometrium increases two- to threefold during uterine contraction(Reference Jrg, Matthews and Gibb19). Likewise, a recent retrospective study emphasised the difference of uterine energy expenditure in sows with short or long farrowing duration(Reference Liu, Zhou and Theil20). The increasing energy intake of sows before the onset of parturition is an effective strategy to improve parturition process(Reference Feyera, Skovmose and Theil21,Reference Oliveira, Neves and Castro22) . However, it should be noted that the feed intake is decreased when sows approach farrowing, and the intense physical activity due to nest-building behaviour increases the energy expenditure of sows prior to parturition(Reference Nielsen, Feyera and Theil23). Previous study had pointed out that sows fed high-fibre diet have a longer postprandial energy uptake from the gastrointestinal tract(Reference Feyera, Højgaard and Theil24) and a more stable level of blood glucose(Reference Serena, Jørgensen and Knudsen13), indicating a more sustainable energy supply during parturition.

In addition to glucose, SCFA can also be utilised as energy source contributing to nearly 30 % of the energy requirements of pigs(Reference Serena, Jørgensen and Knudsen13,Reference Inoue, Tsujimoto and Kimura25) , whereas 10 % in humans(Reference Gijs, Karen and Groen26). In this study, we found that concentrations of plasma acetate and total SCFA on G107 were positively correlated with plasma glucose on G107, indicating the role of SCFA as energy substrates. Consistent with recent study(Reference Feyera, Pedersen and Theil14), moreover, the plasma concentrations of acetate, butyrate and total SCFA at parturition were lower than that on G107, further suggesting the expenditure of SCFA during parturition.

In contrast to acetate, butyrate and total SCFA, the level of plasma propionate increased at parturition as compared with G107. The propionate acts as a precursor for gluconeogenesis in the liver(Reference Gijs, Karen and Groen26), and about 50 % of propionate were utilised as the substrate of hepatic gluconeogenesis in humans(Reference Gijs, Karen and Groen26), and 69 % of total glucose were synthesised from propionate in a mice model(Reference Besten, Lange and Havinga27). Parturition is a highly energy-demanding process(Reference Tokach, Menegat and Gourley28); thus, the elevated plasma propionate may largely support the high energy (glucose) requirement during parturition process. In the present study, the level of plasma propionate was comparable between NDF and HDF sows on G107. When it comes to parturition, however, the HDF sows had significantly higher level of plasma propionate as compared with NDF ones. The higher concentration of plasma propionate in HDF sows (91 % increase) indicated the gluconeogenesis process. Intriguingly, the plasma propionate concentration on G107 was positively correlated with parturition duration, which is also in line with the recent retrospective study(Reference Liu, Zhou and Theil20).

The molar ratio of acetate, propionate and butyrate in faeces of sows is 66:23:11 in this study, a little different with that in human faeces, 60:20:20(Reference Kumar, Rani and Datt10). Of note, most of SCFA, except for butyrate, showed higher levels in both faeces and plasma of HDF sows at parturition. In fact, findings concerning the effect of dietary fibre intake on butyrate level are inconsistent across different studies. The intake of inulin-type fructans by patients with type 2 diabetes showed unchanged butyrate level(Reference Birkeland, Gharagozlian and Birkeland29), whereas inulin intake by mice showed higher level of butyrate in faeces, but not in plasma(Reference Igarashi, Morimoto and Suto30). Alfalfa-containing diet also increased butyrate level in the caecal digesta of pigs(Reference Wang, Qin and He31) and caecal levels of mucosal genes involved in SCFA sensing and absorption(Reference Wang, Qin and He31). In our previous study, sows supplemented with guar gum and cellulose during the whole gestation had increased faecal level of butyrate(Reference Zhuo, Feng and Xuan32). Therefore, it is speculated that this inconsistency could be due to the heterogeneity in fibre types, species and pathophysiological status of the subjects. A previous study pointed out that butyrate and valerate have competitive metabolic pathways(Reference Ramos, Terry and Holman33), which may explain the reasons that valerate increased but butyrate did not increase in this study.

In addition to SCFA, we observed some metabolites were markedly altered by dietary treatment, using non-targeted metabolomics analysis. Particularly, some metabolites are related to energy metabolism, such as betaine and dimethylglycine (DMG). Previous study has reported that betaine improves energy utilisation, especially when energy intake is insufficient(Reference Schrama, Heetkamp and Simmins34). It has been reported that betaine can be catalysed by betaine homocysteine methyltransferase to produce DMG(Reference Cools, Maes and Buyse35). In this study, the increased DMG in the plasma of sows fed HDF diet coincided with the higher betaine. In parallel, the result of plasma metabolomics showed a higher concentration of propionic acid in HDF sows, which is in consistent with the SCFA results in this study. Furthermore, dietary fibre can serve as the metabolism substrate for specific bacteria producing secondary bile acids(Reference Makki, Deehan and Walter36). Our untargeted metabolomic analysis showed there was decreased faecal level of 3b-hydroxy-5-cholenoic acid, which is a monohydroxy bile acid, an intermediate of synthesis of lithocholate, chenodeoxycholate and cholate involving in gut microbiota(Reference Javitt, Kok and Carubbi37). However, there were inconsistent reports that wheat bran fibre decreased the faecal levels of lithocholate and deoxycholate in humans(Reference Alberts, Einspahr and Earnest38,Reference Reddy, Engle and Simi39) , whilst soluble β-glucans increased faecal levels of primary and secondary bile acids(Reference Ghaffarzadegan, Zhong and Nyman40).

Given the crucial role of gut microbiota on fermentation of dietary fibre and production of SCFA and other metabolites, the 16S rRNA sequencing was applied to determine the composition of gut microbiota. We found HDF intake in late gestation altered gut microbiota, with lower abundance of Firmicutes, but higher abundance of Bacteroidetes, which are in accordance with previous study(Reference Ferrario, Statello and Carnevali41). The phylum Synergistetes, digesting fibre to produce acetic acid(Reference Ramos, Terry and Holman33), showed increased abundance in HDF sows. Genera Turicibacter and Terrisporobacter showed decreased abundance in HDF sows, as reported in pigs and children consuming inulin-enriched diet(Reference Wang, Qin and He31,Reference Jae-Young, Min and In-Sung42,Reference Josephine, Nicolucci and Heidi43) . The changes of multiple genera involving in fibre degradation are positively correlated with the levels of specific SCFA in faeces and plasma, suggesting their roles in the production of SCFA. The family Rikenellaceae and genus Cellulosilyticum are reported to degrade carbohydrates(Reference Pitta, Pinchak and Dowd44,Reference Hee, Min-Sung and Tae45) . Accordingly, our results showed that Cellulosilyticum was positively correlated with acetate and isobutyrate in faeces. Lachnoclostridium degrades complex polysaccharides to produce SCFA(Reference Peng, Wang and Hu46), and its positive correlation with acetate and isobutyrate was found in the current study. Alloprevotella and Pyramidobacter from the Synergistetes phylum have been reported to produce acetate(Reference Peng, Wang and Hu46,Reference Pan, Xue and Nan47) , but only a positive correlation between Pyramidobacter and faecal valerate was found in this study. Anaerovibrio was reported with lipolytic activity in producing glycerol for propionate synthesis(Reference Ramos, Terry and Holman33), but instead a strong correlation between the Anaerovibrio abundance and plasma acetate was observed. Nevertheless, these newly observed correlations suggest potential roles of these bacterial taxa in the production of specific SCFA.

The correlations analyses showed the positive correlations between phyla Succinivibrio, Butyrivibrio and isobutyrate, valuerate, genus butyrivibrio and butyrate in the faeces. Also we found the positive correlations between Succinivibrio, Cellulosilyticum and pseudouridine, which is a metabolite reflecting cell turn-over of the host. The family Prevotellaceae, genus Anaerovibrio and imidazoleacetic acid involved in bacterial metabolism of tryptophan. Positive correlation between Lachnoclostridium and 2-keto-gluconic acid, an intermediate of energy metabolism, was also observed by regularised canonical correlation analysis. Moreover, the strong positive correlation found between Anaerovibrio and betaine suggested the role of gut microbiota in betaine metabolism. These correlations suggest new associations between bacterial taxa and host metabolites, revealing the potential mechanism of dietary fibre intake on host physiology.

Conclusion

Our study showed that high dietary fibre intake in late gestation improved parturition duration, which could be associated with altered gut microbiota, production of SCFA and other metabolites involving in energy metabolism. However, the further investigations are needed whether other mechanism remains about the improvement of parturition duration by dietary fibre, also fibre types and gut microbiota differences across species need to be concerned.

Acknowledgement

None.

This work was supported by the National Key R&D Program of China (2018YFD0501000), Overseas Expertise Introduction Project for Discipline Innovation (111 Project), National Natural Science Foundation of China (31872372) and International Cooperation Project of National Natural Science Foundation of China (3191101579) and Scientific and Technological Development Program for Overseas Returnees of Sichuan Province.

In this work, L. C. and Y. Liu. designed the study. Y. Liu. and N. C. carried out the animal and laboratory experiments. Y. J., R. Z., Z. F., Y. Lin., S. X., B. F., Y. Z., D. W., P. K. T. and L. C. analysed the data. Y. Liu and P. J. wrote the manuscript and L. C. helped to revise the manuscript.

The authors declare that they have no conflicts of interest.

Supplementary material

For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S0007114522000502

Footnotes

These authors contributed equally to this work.

References

Oliviero, C, Heinonen, M, Valros, A, et al. (2010) Environmental and sow-related factors affecting the duration of farrowing. Anim Reprod Sci 119, 8591.CrossRefGoogle ScholarPubMed
Dijk, AJV, Rens, BTTM, Lende, TVD, et al. (2005) Factors affecting duration of the expulsive stage of parturition and piglet birth intervals in sows with uncomplicated, spontaneous farrowings. Theriogenology 64, 15731590.CrossRefGoogle ScholarPubMed
Gourley, KM, Swanson, AJ, Royall, RQ, et al. (2020) Effects of timing and size of meals prior to farrowing on sow and litter performance. Transl Anim Sci 4, 66.CrossRefGoogle ScholarPubMed
Leenhouwers, JI, Lende, TVD & Knol, EF (1999) Analysis of stillbirth in different lines of pig. Livest Prod Sci 57, 243253.CrossRefGoogle Scholar
Wang, YS, Zhou, P, Liu, H, et al. (2016) Effects of inulin supplementation in low- or high-fat diets on reproductive performance of sows and antioxidant defence capacity in sows and offspring. Reprod Domest Anim 51, 492500.CrossRefGoogle ScholarPubMed
Guillemet, R, Hamard, A, Quesnel, H, et al. (2007) Dietary fibre for gestating sows: effects on parturition progress, behaviour, litter and sow performance. Animal 1, 872880.CrossRefGoogle ScholarPubMed
Koh, A, Vadder, FD, Kovatcheva, P, et al. (2016) From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 13321345.CrossRefGoogle ScholarPubMed
Hussain, SK, Dong, TS, Agopian, V, et al. (2020) Dietary protein, fiber and coffee are associated with small intestine microbiome composition and diversity in patients with liver cirrhosis. Nutrients 12, 1395.CrossRefGoogle ScholarPubMed
Mathilde, LS, Etienne, L, Olivier, Z, et al. (2018) Effect of dietary fiber content on nutrient digestibility and fecal microbiota composition in growing-finishing pigs. PLOS ONE 13, e206159.Google Scholar
Kumar, J, Rani, K & Datt, C (2020) Molecular link between dietary fibre, gut microbiota and health. Mol Biol Rep 47, 62296237.CrossRefGoogle ScholarPubMed
Tan, FPY, Beltranena, E & Zijlstra, RT (2021) Resistant starch: implications of dietary inclusion on gut health and growth in pigs: a review. J Anim Sci Biotechnol 12, 124.CrossRefGoogle ScholarPubMed
Schönfeld, P & Wojtczak, L (2016) Short- and medium-chain fatty acids in energy metabolism: the cellular perspective. J Lipid Res 57, 943954.CrossRefGoogle Scholar
Serena, A, Jørgensen, H & Knudsen, KEB (2009) Absorption of carbohydrate-derived nutrients in sows as influenced by types and contents of dietary fiber. J Anim Sci 87, 136147.CrossRefGoogle ScholarPubMed
Feyera, T, Pedersen, TF, Theil, PK, et al. (2018) Impact of sow energy status during farrowing on farrowing kinetics, frequency of stillborn piglets and farrowing assistance. J Anim Sci 96, 23202331.CrossRefGoogle ScholarPubMed
Che, L, Hu, L, Zhou, Q, et al. (2019) Microbial insight into dietary protein source affects intestinal function of pigs with intrauterine growth retardation. Eur J Nutr 59, 327344.CrossRefGoogle ScholarPubMed
Liu, Y, Chen, N, Che, L, et al. (2020) Effects of dietary soluble or insoluble fiber intake in late gestation on litter performance, milk composition, immune function, and redox status of sows around parturition. J Anim Sci 98, 17.CrossRefGoogle ScholarPubMed
Langendijk, P & Plush, K (2019) Parturition and its relationship with stillbirths and asphyxiated piglets. Animal 9, 885.CrossRefGoogle ScholarPubMed
Li, H, Liu, Z, Lyu, H, et al. (2020) Effects of dietary inulin during late gestation on sow physiology, farrowing duration and piglet performance. Anim Reprod Sci 219, 106531.CrossRefGoogle ScholarPubMed
Jrg, C, Matthews, SG, Gibb, W, et al. (2000) Endocrine and paracrine regulation of birth at term and preterm. Endocr Rev 21, 514.Google Scholar
Liu, Y, Zhou, Q, Theil, PK, et al. (2021) The differences in energy metabolism and redox status between sows with short and long farrowing duration. Animal 15, 100355.CrossRefGoogle Scholar
Feyera, T, Skovmose, SJW, Theil, PK, et al. (2021) Optimal feed level during the transition period to achieve faster farrowing and high colostrum yield in sows. J Anim Sci 2, 111.Google Scholar
Oliveira, RA, Neves, JS, Castro, DS, et al. (2020) Supplying sows energy on the expected day of farrowing improves farrowing kinetics and newborn piglet performance in the first 24 h after birth. Animal 14, 22712276.CrossRefGoogle ScholarPubMed
Nielsen, SE, Feyera, T, Theil, PK, et al. (2021) Intravenous infusion of glucose improved farrowing performance of hyperprolific crossbred sows. J Anim Sci 5, 111.Google Scholar
Feyera, T, Højgaard, CK, Theil, PK, et al. (2017) Dietary supplement rich in fiber fed to late gestating sows during transition reduces rate of stillborn piglets. J Anim Sci 95, 54305438.CrossRefGoogle Scholar
Inoue, D, Tsujimoto, G & Kimura, I (2014) Regulation of energy homeostasis by GPR41. Front Endocrinol 5, 81.CrossRefGoogle ScholarPubMed
Gijs, DB, Karen, VE, Groen, AK, et al. (2013) The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res 54, 23252340.Google Scholar
Besten, G, Lange, K, Havinga, R, et al. (2013) Gut-derived short-chain fatty acids are vividly assimilated into host carbohydrates and lipids. Am J Physiol-Gastr L 305, G900G910.Google Scholar
Tokach, MD, Menegat, MB, Gourley, KM, et al. (2019) Review: nutrient requirements of the modern high-producing lactating sow, with an emphasis on amino acid requirements. Animal 13, 29672977.CrossRefGoogle ScholarPubMed
Birkeland, E, Gharagozlian, S, Birkeland, KI, et al. (2020) Prebiotic effect of inulin-type fructans on faecal microbiota and short-chain fatty acids in type 2 diabetes: a randomised controlled trial. Eur J Nutr 59, 33253338.CrossRefGoogle ScholarPubMed
Igarashi, M, Morimoto, M, Suto, A, et al. (2020) Synthetic dietary inulin, Fuji FF, delays development of diet-induced obesity by improving gut microbiota profiles and increasing short-chain fatty acid production. PeerJ 8, e8893.CrossRefGoogle ScholarPubMed
Wang, JW, Qin, CF, He, T, et al. (2018) Alfalfa-containing diets alter luminal microbiota structure and short chain fatty acid sensing in the caecal mucosa of pigs. J Anim Sci Biotechnol 9, 11.CrossRefGoogle ScholarPubMed
Zhuo, Y, Feng, B, Xuan, Y, et al. (2020) Inclusion of purified dietary fiber during gestation improved the reproductive performance of sows. J Anim Sci Biotechnol 11, 47.CrossRefGoogle ScholarPubMed
Ramos, AFO, Terry, SA, Holman, DB, et al. (2018) Tucumã oil shifted ruminal fermentation, reducing methane production and altering the microbiome but decreased substrate digestibility within a RUSITEC fed a mixed hay – concentrate diet. Front Microbiol 9, 1647.CrossRefGoogle ScholarPubMed
Schrama, JW, Heetkamp, MJ, Simmins, PH, et al. (2003) Dietary betaine supplementation affects energy metabolism of pigs. J Anim Sci 81, 12021209.CrossRefGoogle ScholarPubMed
Cools, A, Maes, D, Buyse, J, et al. (2010) Effect of N,N-dimethylglycine supplementation in parturition feed for sows on metabolism, nutrient digestibility and reproductive performance. Int J Animal Biosci 4, 20042011.CrossRefGoogle Scholar
Makki, K, Deehan, EC, Walter, J, et al. (2018) The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705715.CrossRefGoogle ScholarPubMed
Javitt, NB, Kok, E, Carubbi, F, et al. (1986) Bile acid synthesis. Metabolism of 3 beta-hydroxy-5-cholenoic acid in the hamster. J Biol Chem 261, 12486.CrossRefGoogle Scholar
Alberts, DS, Einspahr, JG, Earnest, DL, et al. (2003) Fecal bile acid concentrations in a subpopulation of the wheat bran fiber colon polyp trial. Cancer Epidemiol Biomarkers Prev 12, 197.Google Scholar
Reddy, BS, Engle, A, Simi, B, et al. (1992) Effect of dietary fiber on colonic bacterial enzymes and bile acids in relation to colon cancer. Gastroenterology 102, 14751482.CrossRefGoogle ScholarPubMed
Ghaffarzadegan, T, Zhong, Y, Nyman, M, et al. (2017) Effects of barley variety, dietary fiber and β-glucan content on bile acid composition in cecum of rats fed low- and high-fat diets. J Nutr Biochem 53, 104110.CrossRefGoogle ScholarPubMed
Ferrario, C, Statello, R, Carnevali, L, et al. (2017) How to feed the mammalian gut microbiota: bacterial and metabolic modulation by dietary fibers. Front Microbiol 8, 1749.CrossRefGoogle ScholarPubMed
Jae-Young, K, Min, KY, In-Sung, K, et al. (2018) Effects of the brown seaweed laminaria japonica supplementation on serum concentrations of IgG, triglycerides, and cholesterol, and intestinal microbiota composition in rats. Front Nutr 5, 23.Google Scholar
Josephine, H, Nicolucci, AC, Heidi, V, et al. (2019) Effect of prebiotic on microbiota, intestinal permeability, and glycemic control in children with type 1 diabetes. J Clin Endocr Metab 10, 44274440.Google Scholar
Pitta, DW, Pinchak, WE, Dowd, SE, et al. (2010) Rumen bacterial diversity dynamics associated with changing from Bermudagrass Hay to grazed winter wheat diets. Microb Ecol 59, 511522.CrossRefGoogle ScholarPubMed
Hee, EJ, Min-Sung, K, Tae, WW, et al. (2021) Alteration of gut microbiota after antibiotic exposure in finishing swine. Front Microbiol 12, 596002.Google Scholar
Peng, X, Wang, R, Hu, L, et al. (2019) Enterococcus faecium NCIMB 10415 administration improves the intestinal health and immunity in neonatal piglets infected by enterotoxigenic Escherichia coli K88. J Anim Sci Biotechnol 10, 72.CrossRefGoogle Scholar
Pan, X, Xue, F, Nan, X, et al. (2017) Illumina sequencing approach to characterize thiamine metabolism related bacteria and the impacts of thiamine supplementation on ruminal microbiota in dairy cows fed high-grain diets. Front Microbiol 8, 1818.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Body condition, parity and gestation length between NDF and HDF sows(Mean values with the standard errors of the mean)

Figure 1

Fig. 1. Parturition duration (a) and mean birth interval (b) between NDF and HDF sows. *P < 0·05. n 17 for NDF group and n 16 for HDF group. NDF, normal dietary fibre diet; HDF, high dietary fibre diet.

Figure 2

Fig. 2. α-diversity (a), β-diversity using Bray–Curtis distance (b) and unifrac distance (c) of the gut microbiota, and taxa with differential abundance between HDF and NDF sows (d). NDF, normal dietary fibre diet; HDF, high dietary fibre diet.

Figure 3

Table 2. Faecal and plasma SCFA concentrations between NDF and HDF sows(Mean values with the standard errors of the mean, n 9 for each group)

Figure 4

Table 3. Faecal and plasma metabolites with differential abundance between NDF and HDF sows(Mean values with the standard errors of the mean)

Figure 5

Fig. 3. PCA score plots of faecal (a) and plasma (b) metabolome. PCA, principal component analysis; NDF, normal dietary fibre diet; HDF, high dietary fibre diet.

Figure 6

Fig. 4. Heatmap of correlation of the gut bacterial taxa and SCFA in faeces (left) and plasma (right). Correlation coefficients were coloured according to the scale listed on the right.

Figure 7

Fig. 5. rCCA of the gut bacterial taxa and metabolites in faeces. rCCA Similarity Scores are coloured according to the scale listed on the right. rCCA, regularised canonical correlation analysis.

Figure 8

Fig. 6. rCCA of the gut bacterial taxa and metabolites in plasma. rCCA Similarity Scores are coloured according to the scale listed on the right. rCCA, regularised canonical correlation analysis.

Figure 9

Fig. 7. Correlation between SCFA and plasma glucose (a) and (b) and parturition duration (c) of sows.

Supplementary material: File

Liu et al. supplementary material

Tables S1-S3

Download Liu et al. supplementary material(File)
File 23.8 KB