Konjac Flour Inclusion in Gestation Diet Changes Gut Microbiota, Alleviates Oxidative Stress, and

35 Pages·2016·0.91 MB·English

AEM Accepted Manuscript Posted Online 29 July 2016
Appl. Environ. Microbiol. doi:10.1128/AEM.01374-16
Copyright © 2016, American Society for Microbiology. All Rights Reserved.

1 Konjac Flour Inclusion in Gestation Diet Changes Gut Microbiota, Alleviates
2 Oxidative Stress, and Improves Insulin Sensitivity in Sows
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5 Chengquan Tana, Hongkui Weia, Jiangtao Aoa, Guang Longa, Jian Penga,b# w
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8 Department of Animal Nutrition and Feed Science, College of Animal Science and
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9 Technology, Huazhong Agricultural University, Wuhan 430070, P. R. Chinaa; The :/
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10 Cooperative Innovation Center for Sustainable Pig Production, Wuhan 430070, P. R. m
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11 Chinab m
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12 rg
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13 Running Head: Effect of konjac flour on gut microbiota of sows
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16 #Address correspondence to Jian Peng, [email protected]. 3,

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23 Abstract Although dietary fibers contribute to health and physiology primarily via the
24 fermentative actions of the gut microbiota of the hosts, few studies have focused on
25 how these interactions influence the metabolic status of sows. Here, the effects of
26 konjac flour (KF) inclusion in gestation diet on oxidative stress status, insulin
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27 sensitivity and gut microbiota were investigated to elucidate the correlation between
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28 the microbiota and metabolic changes in sows. Sows were assigned to either control d
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29 or 2.2% KF dietary treatment during gestation. The gut microbiota population in sows f
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30 during gestation and lactation was assessed by 16S rDNA gene sequencing. The
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31 oxidative stress parameters, homeostasis model assessment (HOMA) values, and fatty :/
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32 acids in the blood of sows were also assessed. Compared to the control diet group, KF m
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33 significantly reduced the serum levels of reactive oxygen species (ROS) and m
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34 8-hydroxy-deoxyguanosine (8-OHdG), but increased the serum concentrations of g
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35 glutathione peroxidase (GSH-Px) in sows on day 1 in lactation. Additionally, sows in
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36 the KF group had a lower value of HOMA-insulin resistance, but a higher value of e
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37 HOMA-insulin sensitivity (HOMA-IS). KF induced changes in the gut microbial e
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38 composition at the phylum and genus levels. The increased relative abundance of ,

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39 Akkerermansia and Roseburia in the KF group was positively correlated with the 1
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40 HOMA-IS. Overall, dietary KF alleviated oxidative stress and improved insulin y
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41 sensitivity of sows, and the changes of gut microbiota in response to KF may be s
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42 correlated with the host metabolism response.
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45 IMPORTANCE
46 To date, the effect of dietary fiber on metabolism responses and gut microbiota in
47 sows has not been investigated. Here, KF supplementation to gestation diet in sows
48 was found to alleviate oxidative stress, and improve insulin sensitivity.
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49 Pyrosequencing analysis revealed that KF treatment induces changes in the gut
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50 microbiota composition at the phylum and genus levels. Moreover, the changes of gut d
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51 microbiota in response to KF may be correlated with the host metabolism response. f
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52 INTRODUCTION
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53 The performance of breeding sows directly affects the overall productivity of pig :/
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54 operation. Increased metabolic burdens on sows during late gestation and lactation m
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55 cause elevated systemic oxidative stress during these important periods (1, 2). m
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56 Elevated oxidative stress is reported to be associated with pregnancy complications in g
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57 highly prolific sows (3, 4). Moreover, the transition from pregnancy to lactation is
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58 characterized by physiological and metabolic changes, such as a progressive decrease e
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59 in insulin sensitivity during late gestation and lactation (5, 6), which may e
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60 unfortunately result in decreased lactation feed intake of sows (7, 8). Our previous ,

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61 studies showed that sows provided with konjac flour (KF) during gestation 1
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62 significantly increased the voluntary feed intake, and improved their piglet y
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63 performance during lactation (9-11), probably as a result of insulin sensitivity s
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64 improvement. However, the mechanism by which dietary KF exerts the benefit is
65 poorly understood.
66 KF is produced abundantly in the konjac tuber (Amorphophallus Konjac), mainly
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67 containing konjac glucomannan (KGM) (12), and has been consumed as rubbery jelly,
68 noodles and other food products by humans in Asia for centuries. KGM is a complex
69 carbohydrate consisting of D-glucose and D-mannose units joined by β-1,4 glycosidic
70 bond linkages (13). KGM has great viscosity and swelling capacity (10), and
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71 withstands digestion in the small bowel and has been shown to alter the colonic /fecal
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72 microbiota composition in rats (14), adult humans (12), and sows (9). Growing d
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73 evidence suggests that gut microbiota plays a vital role in driving metabolic disease f
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74 development, including inflammation, and reduced insulin sensitivity in nonpregnant
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75 (15, 16) or pregnant hosts (17). Dietary interventions are thus a potential tool to :/
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76 modulate gut microbiota and further impact host health (18, 19). Previous studies m
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77 have shown that resistant starch (rich in non-starch polysaccharides) improves glucose m
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78 tolerance in healthy subjects with a normal body mass index (20, 21) and provided the g
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79 evidence of a link between colonic fermentation and glucose metabolism (20).
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80 However, to date, litter information is available about the impact of KF feed e
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81 consumption on the gut microbiota of pregnant females and the correlation between e
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82 gut microbiota and metabolic parameters after dietary KF supplementation. ,

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83 The aim of this study was to determine the effects of KF inclusion in gestation diet 1
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84 on oxidative stress status and insulin sensitivity of sows. The influence of KF y
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85 treatment on the taxonomic profile of the gut microbiota of sows was also investigated s
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86 by high-throughput sequencing analysis. Our hypothesis was that the inclusion of KF
87 in a gestation diet would modify the intestinal microbiota, which would be associated
88 with changes in oxidative stress status, homeostasis model assessment (HOMA)
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89 values, plasma concentrations of short-chain fatty acids (SCFA), free fatty acids (FFA)
90 and inflammatory cytokine of sows.
91 MATERIALS AND METHODS
92 The experiment was approved by the Huazhong Agricultural University Animal
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93 Care and Use Committee guidelines.
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94 Animals, diets and housing d
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95 Fifty large white sows with an average parity of 4.95 ± 1.02 (2-6) were allocated f
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96 across two dietary treatments groups based on parity and body weight (BW). During
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97 gestation the sows were fed with two different diets consisting of a control gestation :/
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98 diet (n=24) and the same basal diet supplemented with 2.2% konjac flour (KF diet, m
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99 n=26). Sows were restrict-fed with their respective diets during gestation twice a day m
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100 (07:00 and 14:30). All sows were allowed to consume the same lactation diets ad g
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101 libitum (see Table S1 in the supplemental material). All diets had the same levels of
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102 net energy, crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber e
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103 (NDF) and insoluble fiber (ISF). KF diet had higher soluble fiber (SF) level than the e
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104 control diet. Pregnant sows were housed individually in gestation stalls (2.2 m × 0.7 ,

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105 m × 1.1 m). Sows were moved from the gestation stalls to the farrowing rooms on day 1
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106 107 ± 2 of gestation and then kept in individual farrowing crates with stalls (2.2 m × y
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107 0.7 m) in pens and space on both sides of the stall (2.2 m × 0.5 m) for the pigs after s
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108 birth. Both sows and piglets had free access to water.
109 Sample collection
110 Blood samples (5 mL) from ear vein were collected from the fasted sows (5 sows
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111 per dietary treatment with a similar parity and BW) before feeding on day 10 and 109
112 of gestation and on day 3 and 7 of lactation for HOMA values analysis. Blood
113 samples were collected from the fasted sows (5 sows per dietary treatment with a
114 similar parity and BW) before feeding on day 109 of gestation and on day 3 of
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115 lactation for inflammatory cytokine analysis. Blood samples were collected from sows
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116 (5 sows per dietary treatment with a similar parity and BW) before feeding and at 4 h d
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117 postprandial on day 109 of gestation for SCFA and FFA analyses. Blood samples (5 f
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118 mL) were collected from sows (5 sows per dietary treatment with a similar parity and
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119 BW) before feeding on day 10, 60, 90 and 109 of gestation, and on day 1, 3, 7, and 21 :/
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120 of lactation for oxidative stress parameters analysis. Fasting sows were selected for m
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121 blood sampling after an overnight fasting period of 16 h during gestation and 12 h m
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122 during lactation. g
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123 Blood samples for HOMA, SCFA, FFA and inflammatory cytokine analyses were
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124 collected in heparinized tubes (5 mL), while those for oxidative stress parameter e
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125 analyses were collected in tubes without anticoagulant (5 mL). Samples for plasma e
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126 assays (heparinized tubes) were kept on ice and centrifuged for 5 min at 8500× g at ,

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127 4 °C. Samples for serum assays (tubes containing no anticoagulant) were stored at 1
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128 room temperature for 4 h, and then centrifuged for 5 min at 5000× g at 4 °C. Serum y
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129 and plasma samples were stored at -80 °C until analysis. s
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130 Fresh fecal samples were individually collected using sterile 50 mL centrifuge
131 tubes (without any treatment) from the sows (10 sows per dietary treatment with a
132 similar parity and BW) on day 109 of gestation as well as day 21 of lactation. Samples
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133 were transported (the tubes frozen on dry ice) immediately to the laboratory and then
134 stored at -20 °C before total genomic DNA extraction (within 12 h).
135 Analysis of oxidative stress parameters
136 Serum samples were used to measure levels of thiobarbituric acid reactive
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137 substances (TBARS), 8-hydroxy-deoxyguanosine (8-OHdG), glutathione peroxidase
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138 (GSH-Px) and reactive oxygen species (ROS). TBARS, one of the most frequently d
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139 used indicators of lipid peroxidation, was determined in the current study. The major f
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140 marker for oxidative damage to nucleic acids, 8-OHdG, was chosen to determine the
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141 DNA damage in the current study (22). Serum samples were analyzed for the :/
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142 activities of antioxidant enzymes including GSH-Px and for TBARS using the m
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143 commercial kits provided by Nanjing Jiancheng Bioengineering Institute (Nanjing, m
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144 China) (23). GSH-Px activity was determined by quantifying the rate of oxidation of g
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145 GSH to GSSG by H2O2 catalyzed by GSH-Px. GSH reacts with N
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146 5,5’-dithiobis-p-nitrobenzoic acid (DTNB) to produce yellow colored e
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147 5-thio-2-nitrobenzoic acid (TNB) that can be quantified spectrophotometrically at 412 e
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148 nm. TBARS was analyzed based on the reaction with 2-thiobarbituric acid. The ,

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149 resulting pink product was measured spectrophotometrically at 535 nm. The 1
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150 concentration of 8-OHdG in the serum sample was determined using an anti-8-OHdG y
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151 monoclonal antibody in an ELISA kit (Dobio Biotech Co., LTD, Shanghai, PR China) s
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152 as described by Pialoux et al. (24). Levels of ROS in serum were measured by
153 chemiluminescence assay using luminol (5-amino-2, 3 dihydro-1, 4- phthalazinedione,
154 Sigma) as probe according to the procedure described by Du et al. (25). Briefly, 50μl
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155 serum and 20μl horseradish peroxidase (HRP) (1U of HRP, type VI 310 U/mg;
156 Sigma) were mixed in a tube, and then supplemented with 200μl Krebs-HEPES
157 buffer (99 mM NaCl, 4.7 mM KCl, 1.9 mM CaCl2, 1.2 mM MgCl2, 1.00 mM KH2PO4,
158 25 mM NaHCO3, 20 Mm HEPES, 11.0 mM glucose; pH 7.4). All chemiluminometric D
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159 counts were obtained at an interval of 0.05 s for 3 s using a LB 940 luminometer
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160 (Berthold Technologies, Bad Wildbad, Gremany), and the results were expressed as d
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161 areas under the curve (AUCs) of relative light units. The calculation was based on the f
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162 integration of the curve using the trapezoidal rule (a linear approximation). All
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163 samples were analyzed in duplicate. :/
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164 Laboratory analyses m
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165 Diet samples were ground through a 0.45 mm sieve by a high-speed universal m
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166 disintegrator before analysis. CP was determined according to AOAC (1990). ADF g
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167 and NDF were analyzed as described by Van Soest et al. (26). SF and ISF were
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168 determined by AOAC Method 991.43 (1990). Plasma concentrations of glucose and e
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169 insulin were determined using the glucose dehydrogenase activity colorimetric assay e
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170 kit (BioVision Inc., CA, USA) and the insulin ELISA kit (Biosource Inc., Sunnyvale, ,

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171 CA, USA) according to the manufacturer’s instructions, respectively. The indirect 1
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172 methods were used to evaluate insulin sensitivity by HOMA, HOMA-IR (insulin y
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173 resistance) = [(fasting insulin, mIU/L)] × (fasting glucose, mmol/L)] ÷22.5; s
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174 HOMA-IS (insulin sensitivity)=1÷[(fasting insulin, mIU/L)] ×(fasting glucose,
175 mmol/L)] (27). Plasma SCFA was analyzed using gas chromatography (Varian Inc.,
176 USA) equipped with a capillary column (28). The ratio of the split injection used was
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177 1:10. A flame ionization detector was used to identify the components within the
178 sample. Column temperature was set at 110 °C and increased at a rate of 18 °C/min up
179 to 200 °C with T1=1min and T2=2 min. Helium was used as a carrier gas at a flow
180 rate of 8 mL/min with a 10-min run time for each sample. FFA was extracted from
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181 plasma samples after homogenization in a suitable excess of chloroform–methanol
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182 (2:1, v/v) (29). FFA was prepared for gas chromatography determination using d
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183 KOH/methanol. The injector and detector temperatures were held at 250 and 270°C. A f
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184 total of 40 saturated, monounsaturated and polyunsaturated fatty acid standards
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185 (NU-CHEK, Prep, UK) were used. Peaks were identified by retention time relative to :/
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186 individual fatty acid standard. Plasma concentrations of TNF-α and IL-6 were m
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187 measured using commercially available procine ELISA kits (Quantikine PTA00, R&D m
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188 Systems, Minneapolis, MN) according to the manufacturer’s instructions. All samples g
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189 were analyzed in duplicate.
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190 DNA extraction, PCR amplification of 16S rDNA, amplicon sequence and e
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191 sequence data processing e
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192 Microbial genomic DNA was extracted from 220 mg of each fecal sample using a ,

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193 QIAamp Stool DNA Kit (QIAamp DNA Stool Mini Kit, 51504, QIAGEN) according 1
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194 to the manufacturer’s instructions. Successful DNA isolation was confirmed by y
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195 agarose gel electrophoresis. Based on previous reports (30, 31), bacterial 16S rDNA s
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196 was PCR amplified using primers flanking the V3+V4 hyper variable region with
197 barcoded fusion forward primer 338F 5’-ACTCCTACGGGAGGCAGCA-3’, and the
198 reverse primer was 806R 5'-GGACTACHVGGGTWTCTAAT-3'. The PCR
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199 conditions were as follows: one pre-denaturation cycle at 94 °C for 4 min, 25 cycles
200 of denaturation at 94 °C for 30 s, annealing at 55 °C for 45 s, and elongation at 72 °C
201 for 30 s, and one post-elongation cycle at 72 °C for 5 min. The PCR amplicon
202 products were separated on 0.8 % agarose gels and then extracted from the gels. Only
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203 the PCR products without primer dimers and contaminant bands were used for
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204 sequencing by synthesis. Barcoded V4 amplicons were sequenced using the d
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205 paired-end method by Illumina MiSeq with a 7-cycle index read. Sequences with an f
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206 average phred score lower than 30, ambiguous bases, homopolymer runs exceeding 6
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207 bp, primer mismatches, or sequence lengths shorter than 100 bp were removed. Only :/
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208 the sequences with an overlap longer than 10 bp and without any mismatch were m
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209 assembled according to their overlap sequence. Reads that could not be assembled m
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210 were discarded. Barcode and sequencing primers were trimmed from the assembled g
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211 sequence (30).
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212 16S rDNA gene sequencing and bioinformatics analysis e
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213 Taxon-dependent analysis was conducted using the Ribosomal Database Project e
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214 (RDP) classifier (31). The RDP classifier is a web-based program that assigns 16S ,

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215 rRNA sequences to phylogenetically consistent bacterial species with an identity 1
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216 cutoff of 97%. The abundance of operational taxonomic units (OTUs) for each sample y
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217 was generated at the genus level, log 2 transformed and then normalized as follows: s
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218 from each log-transformed measure, the arithmetic mean of all transformed values
219 was subtracted, and the difference was divided by the standard deviation of all
220 log-transformed values for a given sample.
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