Identification and trends of change for differential metabolites.

a<p>Change trend compared with control group.</p>b<p>Change trend compared with SLE model group.</p><p>The levels of differential metabolites were marked with (↓) down-regulated, (↑) up-regulated and (—) no significant change (*<i>P</i><0.05; **<i>P</i><0.01).</p

The MS and MS/MS parameters of the standards and their quantitative results.

<p>The MS and MS/MS parameters of the standards and their quantitative results.</p

MS (a) and MS/MS (b) information of 14-HDOHE.

<p>MS (a) and MS/MS (b) information of 14-HDOHE.</p

OPLS score plot of the SLE model group, PA-treated group, JP-treated group and control group by SIMCA-P11.0 (n = 10 in each group).

<p>OPLS score plot of the SLE model group, PA-treated group, JP-treated group and control group by SIMCA-P11.0 (n = 10 in each group).</p

(a) Merged EIC of 14-HDOHE based on the SLE model group and control group.

<p>The relative content of 14-HDOHE significantly increased in SLE model mice. (b) Merged EIC of 14-HDOHE based on SLE model group, control group, JP-treated group, and PA-treated group.</p

BPC of standards (a) and JP (b).

<p>BPC of standards (a) and JP (b).</p

(a) OPLS score plot of the SLE model group (▴) and control group (▪). (b) OPLS loading plot of the SLE model group and control group.

<p>The 12 metabolites far from the origin that contributed significantly to differentiating the clustering of the SLE model group from the control group were defined as differential metabolites.</p

The perturbed metabolic network associated with SLE.

<p>The differential metabolite levels of the SLE model group compared to the control group were marked with (⇑) upregulated and (⇓) downregulated. (*Differential metabolites which could be effectively regulated by JP).</p

Herbs, ingredients and targets of SWT and SJZT1.rar

<p><b>Traditional Chinese Medicine (<a></a><a>TCM</a>) is increasingly getting clinical application worldwide. But its theory like QI-Blood is still abstract. Actually, Qi deficiency and blood deficiency, which were treated by Si-Jun-Zi-Tang (SJZT) and Si-Wu-Tang (SWT) respectively, have characteristic clinical manifestations. Here, we analyzed targets of the ingredients in SJZT and SWT to unveil potential biologic mechanisms between Qi deficiency and blood deficiency through biomedical approaches. First, ingredients in SWT and SJZT were retrieved from TCMID database. The genes targeted by these ingredients were chosen from STITCH. After enrichment analysis by Gene Ontology (GO) and DAVID, enriched GO terms with p-value less than 0.01 were collected and interpreted through DAVID and KEGG. Then a visualized network was constructed with ClueGO. Finally, a total of 243 genes targeted by 195 ingredients of SWT formula and 209 genes targeted by 61 ingredients of SJZT were obtained. Six </b></p> <p><b>metabolism pathways and two environmental information processing pathways </b><b>enriched by targets were correlated with two or more herbs in SWT and SJZT formula, respectively. SWT significantly <a></a><a>influence</a> amino acid and carbohydrate metabolism. While SJZT mainly influence the organismal system function, environmental information processing, metabolism and cellular processes.</b></p

Additional file 1: of Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis

Table S1. Phenotype information of AS patient individuals and health controls in discovery stage (156 samples) and validation stage (55 samples). Table S2. Data production and quality control of 156 samples in discovery stage and 55 samples in validation stage. Table S3. The 8743 reference genomes from NCBI and HMP (downloaded on 15 Dec 2013). Table S4. The differentially abundant genus in AS patients (n = 73) and healthy controls (n = 83). Table S5. Assembly result of 156 samples in discovery stage. Table S6. The improvement with the repeatedly assembly. Table S7. Gene prediction of 156 samples in discovery stage. Table S8. Genes with abundance which belong to proteasome modules. All the differentially abundant genes identified in this study only belong to bacterial proteasome. Table S9. The taxonomic annotation of MGSs. Table S10. The phenotypic correlation analysis (p value) of 12 MGSs according to different clinical groups. Table S11. Comparison of the MGS in different diseases. Table S12. The taxonomic annotation of CAGs (Gene number ≥ 100). Table S13. The details of the best markers selected for five monitoring and classification models based on five kinds of bio-markers. Table S14. The 210 differentially abundant sequenced reference genome markers used for classification training. (XLSX 870 kb