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1

Microbiome Analysis Pipeline Detailed

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The raw reads were put for quality control after base calling, and then were subjected to pipeline QIIME for OTU picking and taxonomy as described (26 (link)). Alpha diversity (Shannon, Simpson, and Chao1) were calculated, and the one-way analysis of variance (ANOVA) with a Turkey's test as post-hoc was used to compare between the groups. The beta diversity (Bray-Curtis Index) was calculated using principal coordinate analysis (PCoA), with further comparison of groups by analysis of similarities (ANOSIM). Dendrograms and colinear relation diagrams were generated using Majorbio Cloud Platform (Shanghai Majorbio Bio-pharm Technology, Shanghai, China). Relative abundance of operational taxonomic units (OTUs) were compared by one-way analysis of variance (ANOVA) using Majorbio Cloud Platform, with a Turkey's test as post-hoc and p-values adjusted by false discovery rate (FDR). Functional potentials of OTUs were evaluated by KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment using PICRUSt (http://huttenhower.sph.harvard.edu/galaxy/), a heatmap was generated using TBtools (https://github.com/CJ-Chen/TBtools), and the comparison was processed by ANOVA using Majorbio Cloud Platform with a Turkey's test as post-hoc. Significance was set at p < 0.05 for all comparisons.
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2

Comprehensive Analysis of Chemical and Microbial Profiles

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Data of chemical characteristics, microbial counts, antioxidant capacity, and fatty acid composition were analyzed using a one-way ANOVA using the Statistical Package for the Social Sciences Statistics 26.0 (IBM Corp., Armonk, NY, United States). Differences among treated groups were analyzed by Tukey's test, and a p < 0.05 was considered a significant difference. All microbial data were analyzed using the Majorbio Cloud Platform (https://www.majorbio.com/web/www/index), and the bacterial sequences (SAR number for bacteria: PRJNA949551) were uploaded to the National Center for Biotechnology Information. Metabolites were analyzed using partial least squares discriminant analysis (PLS_DA) in the Majorbio Cloud Platform (https://www.majorbio.com/web/www/index). All metabolites were compared and identified on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Human Metabolome Database (HMDB). Differential metabolite analysis was carried out using Student's t-test with a significance level of P <0.05. Fold changes <1 were considered significant.
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3

Metabolomic Analysis of Biological Samples

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Metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/), Human Metabolome Database (HMDB) (www.hmdb.ca/), and Lipidmaps database (http://www.lipidmaps.org/). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed using the online Majorbio Cloud Platform (www.majorbio.com). Statistical significance (p-value) was determined using univariate analysis (t-test). Metabolites with VIP_PLS-DA > 1 and p < 0.05 were considered to be differential metabolites. Volcano plots were prepared at BIC (http://www.ehbio.com/Cloud_Platform/front/#/), with Log2 (fold change) on the x-axis and −Log10 (p-value) on the y-axis. Cluster and functional analyses of metabolites were performed using the online Majorbio Cloud Platform (www.majorbio.com). Metabolic pathways at p < 0.05 were considered to be significantly enriched.
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4

Bioinformatic Analysis of Amplicon Sequencing

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Raw fastq files were quality-filtered by Trimmomatic and merged by FLASH using UPARSE (Zhao et al. 2019 (link), Xu et al. 2020 (link), Kong et al. 2021) (link). Based on the Silva (SSU123) 16S rRNA data base, each 16S rRNA gene sequence was ana lyzed by the RDP Classi fier algorithm (http:// rdp. cme.msu.edu/), with a confidence threshold of 70%. Richness and diversity, the community struc ture, and functional analysis were performed using the free online Majorbio Cloud Platform (www.majorbio.com).
KEGG orthology (KO), cluster of orthologous groups (COG), and pathways of the samples from amplicon sequencing results were predicted using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) based on the Greengenes 13.5 database (Langille et al. 2013) . All software packages were freely available on the Majorbio Cloud Platform.
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5

Physicochemical and Microbiome Analysis

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Data processing and statistical analyses of physicochemical properties were performed using GraphPad Prism 7.0.4 software, and all data are expressed as mean ± SD. Assessments between groups were analyzed using one-way ANOVA, followed by Kruskal–Wallis test. 16S sequencing data analysis was performed in the platform of Major Bio Information Cloud Platform. All sequences were clustered into operational taxonomic units (OTUs) based on 97% identity threshold by the SILVA database (Quast et al., 2013 (link)). A result was considered statistically significant when the P-value was less than 0.05.
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6

Metabolomics Analysis of Biological Samples

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For the metabonomics data, the raw data were imported into the Progenesis QI software (Waters Corporation, Milford, MA, USA) for data preprocessing after UHPLC-QE-HFX/MS analyses. In order to detect metabolites, we carried on the principal component analysis (PCA) and orthogonal partial least-squares (OPLS-DA) analysis. To reveal the differences in the metabolic composition among groups, T-test (Student’s test) and the multivariate analysis of OPLS-DA were used to explore the potential marker or differential metabolites (variable importance in the projection (VIP) > 1, p-value < 0.05). For analysis software, the online platform of Majorbio ISanger Cloud platform (https://cloud.majorbio.com/) was used (assessed on 6 February 2020).
Data of this study were analyzed by one-way ANOVA using the SAS 8.2 software package, followed by a Tukey’s studentized range test to explore treatment effects. Results are presented as means ± standard errors (SD). Differences between significant means were viewed to be statistically different at p < 0.05.
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7

Microbiome Analysis Using Majorbio Cloud

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SPSS 22.0 (IBM SPSS, USA) with significant criteria was used to evaluate the data of physiological, biochemical indicators (ANOVA), and Duncan's multiple range tests. At p < 0.05, differences were judged significant, while p < 0.01 was regarded highly significant. Mean ± standard deviation (SD) was used to represent all data.
The Majorbio Cloud Platform's free online platform (www.majorbio.com.) was used for the microbiological analysis. For the operational taxonomic units (OTUs) analysis, sequences having a similarity to sample sequences of at least 97 % were chosen using Usearch (version 7.0, available at https://drive5.com/uparse/). Using UCHIME, chimeric sequences were discovered and removed. The diversity analysis, principal coordinates analysis (PCoA), and the examination of the bacterial taxonomic compositions were then carried out using the acquired OTUs data. By using one-way ANOVA, the microbiota substantially difference analysis was obtained. Linear discriminant analysis (LDA) and non-parametric factorial Kruskal-Wallis sum-rank test was used to find the LEfSe multistage species difference discriminant analysis (LEfSe).
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8

Screening SAGs for Comparative Analysis

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We initially screened 77 SAGs on the Majorbio Cloud Platform. By using the BLAST (Basic Local Alignment Search Tool) program of the NCBI (National Center for Biotechnology Information) and LSD 3.0 (Leaf Senescence Database, https://bigd.big.ac.cn/lsd/index.php) [47] (link), we selected some SAGs with high homology to important SAGs in other species for further analysis.
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9

Gut Microbiome and NLRP3 Inflammasome

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Differences of the transcript data and metabolite concentration between the CON group and TRP group were analyzed using Student t test. These analyses were performed using SPSS Statistics 17.0. Differences with P < 0.05 were considered as “significant.”
Bioinformation analysis of the gut microbiota was carried out using the Majorbio Cloud platform (https://cloud.majorbio.com). The principle coordinate analysis was produced based on Bray-curtis dissimilarity and the significance in microbial community was analyzed using Wilcoxon rank-sum test. The correlation between gut microbiota and NLRP3inflammasome was analyzed based on Spearman correlation.
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10

Microbial Community Analysis for Agricultural Treatments

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The data shown in this study are mean values ± standard error (SE) of three replicated treatments. Significant differences were determined by one-way ANOVA according to the Tukey test in SPSS v26.0 (IBM Corp., Armonk, NY, USA). Correlation analysis was performed using Software R v3.6.3 (RStudio, Vienna, Austria). principal component analysis (PCA) about treatments containing a different microbial community composition on OUT level was performed by Majorbio Cloud Platform (www.majorbio.com (accessed on 4 January 2021)). The platform took the two eigenvalues of principal components (PC1, PC2, respectively) that could best reflect the difference between the samples automatically. In addition, CMC and CA contents were considered as two environment factors and presented by arrows in the PCA analysis diagram.
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