DOI: 10.5061/dryad.x3ffbg7gj
title: Cecal and colinic 16s rRNA sequencing OTUs

description: The purpose of this study was to investigate the effects of Fermented
 Spent Mushroom Substrates (FSMS) on growth performance, serum biochemical,
 gut digestive enzyme activity, microbial community, genes expression of
 tight junction proteins and volatile fatty acids (VFA) in hindgut (colon
 and cecum) of weaned piglets. A total of 100 weaned Yihao native pigs
 (Native × Duroc, 50 males and 50 females) were allocated to two groups
 with five replicates and ten pigs per replicate. Pigs in the control group
 were fed a basal diet (BD group) and the others were fed basal diets
 supplemented with 3% FSMS (FSMS group). Relative to the BD Group, it had
 better results for Final weight, average daily gain (ADG) and feed
 conversion ratio (FCR) in FSMS Group but not significant (p &gt; 0.05)
 which was accompanied by improved serum T3, IgG and IgA (p &lt; 0.05)
 but lower serum TP, ALB, TC and TG during the overall period (p &lt;
 0.05). Similarly, FSMS significantly up-regulated (p &lt; 0.05) the
 expression of Duodenal tight junction proteins such as pTJP1, pTJP2 and
 pOCLN. Meanwhile, Isobutyric acid, Valeric acid and Isovaleric acid levels
 were increased while Propanoic acid was decreased (p &lt; 0.05) in the
 FSMS group than the BD group. In addition, the piglets in FSMS group
 changed the microbial diversity in the colon and cecum. 16S rRNA gene
 sequencing-based compositional analysis of the colonic and cecal
 microbiota showed differences in relative abundance of bacterial phyla
 (Firmicutes, Bacteroidetes etc.), genus (Lactobacillus, Streptococcus,
 Roseburia etc.) and species (Lactobacillus gasseri, Clostridium_disporicum
 etc.) between the BD and FSMS fed piglets. In conclusion, dietary
 supplementation with FSMS benefited to the intestinal mucosal barrier,
 immunity, and composition of microbiota.
description: Fecal samples from cecum and colon were collected and transferred
 to a 2ml centrifuge tube and immersed in 1ml RNA later. Microbial DNA
 extraction was performed using a QIAamp DNA Stool Mini Kit (QIAGEN,
 Germany) according to manufacturer’s protocol. DNA concentrations of every
 sample were quantified using a Nanodrop 2000 spectrophotometer (Thermo
 Fisher Scientific, Wilmington, DE, United States). The genes of all
 bacterial 16S rRNA in the region of V3–V4 were amplified by polymerase
 chain reaction (PCR) using a universal forward primer 338F
 (5′-ACTCCTRCGGGAGGCAGCAG-3′) and a reverse primer 806R
 (5′-GGACTACCVGGGTATCTAAT-3′) (Mao et al., 2015). PCR reactions were
 carried out in 30 μL reactions with 15 μL of Phusion High-Fidelity PCR
 Master Mix (New England Biolabs), 0.2 μM of forward and reverse primers,
 and 10 ng template DNA. Thermal cycling consisted of initial denaturation
 at 98℃ for 1 min, followed by 30 cycles of denaturation at 98℃ for 10 s,
 annealing at 50℃ for 30 s, and elongation at 72℃ for 30 s. Finally 72℃ for
 5 min. Mix same volume of 1X loading buffer (contained
 SYB green) with PCR products and operate electrophoresis on 2% agarose gel
 for detection. PCR products were mixed in equidensity ratios. Then,
 mixture PCR products were purified with Qiagen Gel Extraction Kit (Qiagen,
 Germany). Sequencing libraries were generated using Illumina TruSeq DNA
 PCR-Free Library Preparation Kit (Illumina, USA) following manufacturer’s
 recommendations and index codes were added. The sequences were performed
 by Illumina Hiseq platorm (NovoGene Ltd and Biomarker Ltd
 respectively). The QIIME (version 1.9.1,
 http://qiime.org/scripts/split_libraries_fastq.html) software package was
 used to demultiplex and quality-filter raw sequence data generated from
 16S rRNA MiSeq sequencing (Campbell et al., 2010). Gaps in each sequence
 were discarded from all the samples to decrease the noise generated the
 screening, filtering, and pre-clustering processes as described previously
 (Gao et al., 2018). Operational taxonomic units (OTUs) were clustered as a
 similarity cut-off of 97% using UPARSE (version 7.0.1001,
 http://drive5.com/uparse/) and unnormal gene sequences were identified and
 deleted using UCHIME (Edgar, 2010).. With each OTU, the representative
 sequence was analyzed using the Ribosomal Database Project (RDP)
 classifier (RRID: SCR_006633) against the Silva (http://www.arb-silva.de/)
 16S rRNA database employing a confidence level of 90%.
 The bacterial diversity, such as rarefaction analysis, the number
 of observed OTUs, coverage abundance estimator, richness estimator (Chao 1
 and ACE), and diversity indices (Shannon and Simpson) were calculated
 using MOTHUR software (version 1.35.12) according to previous
 instructions (Barbara J. Campbell et al., 2010).