Colitis

Both the forward and reverse ends of the same read were truncated at the first base where the Q value was no more than 2. If the pair of reads had a minimum overlap of 50 bp, they were then merged into a complete read. These reads were not kept unless longer than 399 bp with an expected error of no more than 0.561 (link). Two batches of sequencing data from healthy and DSS-induced colitis mice were pooled before OTU picking. Quality-filtered reads were dereplicated into unique sequences and then sorted by decreasing abundance, and singletons were discarded. Representative non-chimeric OTU sequences were next picked by Uparse’s default62 (link). Further reference-based chimera detection was performed using UCHIME63 (link) against the RDP classifier training database (v9)64 (link). The OTU table was finalized by mapping quality-filtered reads to the remaining OTUs with the Usearch61 (link) global alignment algorithm at a 97% cutoff.
The number of high-quality reads of 2 sample were less than 9000, which was removed from further analysis. Then, the sequences of all the samples were downsized to 9000 (1000 permutations) to equal the difference in sequencing depth. All subsequent analysis was performed based on the QIIME platform (version 1.8)65 (link). The alpha diversity of each sample was calculated with observed OTUs and the Shannon index. Representative sequences for each OTU were built into a phylogenetic tree by FastTree and subjected to the RDP classifier to determine the phylogeny with a bootstrap cutoff of 80% (RDP database version 2.10). The preliminary results of sequencing on 16S rRNA gene V3–V4 region were presented in the Supplementary Results.
Random forest models66 were introduced to identify specific bacterial phylotypes that contributed to the segregation of gut microbiota induced by DSS and/or BPB5. Group pairs with a significant difference (P < 0.05, PERMANOVA based on Bray-Curtis distance) were included for random forest discrimination. Models with class error = 0 were considered successful. The importance of an OTU was determined based on the mean decrease in accuracy of discrimination, and OTUs with a value greater than 0.003 were considered key OTUs.
The correlation among 83 key OTUs was calculated by the SparCC algorithm67 (link) with a bootstrap procedure repeated 100 times and then visualized into a network diagram. The Ward clustering algorithm and PERMANOVA (9999 permutations, P < 0.005) based on SparCC correlation coefficients were used to cluster the 83 key OTUs into 11 co-abundance groups (CAGs) using the R program.

Single nucleotide polymorphisms (SNPs) significantly (P < 5×10^-8) related to VEGF levels were selected as instrumental variables (IVs). Since only 3 SNPs were retained after a harmonizing step at r² < 0.001, all independent variants (r² < 0.01) were retained based on European ancestry reference data from the 1000 Genomes Project. In addition, the Phenoscanner (22 (link), 23 (link)) (http://www.phenoscanner.medschl.cam.ac.uk/) search was used to check or detect whether any of these selected SNPs were strongly related to other diseases or phenotypes other than VEGF, so as to prevent a possible effect of the genetic variants on the outcome through confounding factors, known as horizontal pleiotropy. We looked up each SNP and their proxies (r² > 0.80) to check any previous associations (P <5×10^-8) with 3 potential confounders selected based on previously published studies: ulcerative colitis (24 (link)–26 (link)), interleukin (IL) levels (27 (link)–29 (link)) and hemoglobin concentration (30 (link)). Three SNPs were detected and eliminated for being associated with potential confounders (rs6920532: colitis ulcerative, rs6921438: interleukin (IL) (IL-12p70, IL-10, IL-13, IL-7 and IL-5) levels, and rs34881325: hemoglobin concentration).

The potential targets from ten types of herbs (Coptis 3 g, parched white peony root 10 g, parched angelica 10 g, simmered radix aucklandiae 10 g, sanguisorba officinalis 15 g, lithospermum15 g, agrimonia pilosa ledeb 15 g, parched atractylodes macrocephala koidz 10 g, poria cocos 15 g, radix glycyrrhizae preparata 5 g) of the QRXY recipe were identified using the SymMap database. The UC-related microarray dataset GSE53835 was obtained through the gene expression omnibus (GEO) database, the microarray contained platform annotation file GPL1261, and the sample grouping information is presented in Supplementary Table 2. The DEGs were analyzed using the “limma” package of R language with the threshold set as |log2 fold change (FC) | > 1, p < 0.05. An intersection between the potential targets and the DEGs in the UC-related microarray dataset GSE53835 was identified using the jvenn online tool to predict the potential regulators of the QRXY recipe to subsequently attenuate colitis. The expression heat map of candidate genes in the UC-related microarray dataset GSE53835 was plotted using the “pheatmap” package of R language. KEGG pathway enrichment analysis of upregulated genes and downregulated genes was performed by the means of NetworkAnalyst tool. A combination of the coexpedia database and BioGRID database were used to predict the interaction genes among the factors for subsequent prediction of the downstream regulatory factors of candidate genes. The GeneMANIA database was used to analyze the function and the coexpression relationship of the candidate genes.