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 > 0.05) which was accompanied by improved serum T3, IgG and IgA (p < 0.05) but lower serum TP, ALB, TC and TG during the overall period (p < 0.05). Similarly, FSMS significantly up-regulated (p < 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 < 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.

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).