Additional file 1: of Alignment behaviors of short peptides provide a roadmap for functional profiling of metagenomic data

An additional file is available along with the online version of this paper. Additional file 1 not only contains the Figures S1–45 but also contains a detailed description of the nature of files and data shared on ( http://cage.unl.edu/DataPeptide ). (DOCX 14611 kb

Supplemental Data Legends from A Novel MAPK–microRNA Signature Is Predictive of Hormone-Therapy Resistance and Poor Outcome in ER-Positive Breast Cancer

Supplemental Data Legends. Figure and table legends for supplemental data</p

Supplemental Figure S1-S8, Tables S1-S7 from A Novel MAPK–microRNA Signature Is Predictive of Hormone-Therapy Resistance and Poor Outcome in ER-Positive Breast Cancer

Figure S1. Flowchart of study overview Figure S2. (A) gene set enrichment analysis for gene targets of microRNAs overexpressed in the hMAPK- microRNA signature. (B) gene set enrichment analysis for gene targets of microRNAs underexpressed in the hMAPK microRNA signature. The DIANA Mirpath online tool was used for gene set enrichment analyses. Figure S3. (A) MCF-7 cells stably transfected with constitutively active RAF kinase were treated with U0126 for 24 hours. miRNA expression was determined by qRT-PCR; expression given as fold change relative to DMSO treated sample. (B) MCF-7 cells stably transfected to overexpress EGFR were stimulated with EGF and harvested 8 and 24 hours later. Figure S4. (A) Expression of genes that are targets of microRNAs underexpressed in the hMAPKmicroRNA signature in ER+ cancers from TCGA dataset. (B) Expression of genes that are targets of microRNAs overexpresed in the hMAPK-microRNA signature in in ER+ cancers from TCGA dataset. (C) Expression of genes that are targets of microRNAs underexpressed in the hMAPKmicroRNA signature in all breast cancers from METABRIC dataset. (D) Expression of genes that are targets of microRNAs overexpressed in the hMAPK-microRNA signature in all breast cancers from METABRIC dataset. (E) Expression of genes that are targets of microRNAs underexpressed in the hMAPK-microRNA signature in ER+ cancers from METABRIC dataset. (F) Expression of genes that are targets of microRNAs overexpressed in the hMAPK-microRNA signature in ER+ cancers from METABRIC dataset. p-values and t-scores for student's t-test between highhMAPK-microRNA and low-hMAPK-microRNA groups are indicated. Figure S5. Comparison of protein expression of target genes of hMAPK-microRNAs between breast cancers classified as high-hMAPK and low-hMAPK by the hMAPK-microRNA signature. Figure S6. Breast cancers classified as "high hMAPK" by the hMAPK-microRNA signature are enriched for cancers that are (A) ER-negative, (B) and high tumor grade in training datasets. pvalues for chi squared test of independence and for Fisher's exact test are reported. Figure S7. Breast cancers classified as "high hMAPK" by the hMAPK-microRNA signature are enriched for cancers that are (A) ER-negative, (B) high tumor grade, (C) described by a highproliferation metric, and (D) ERBB2 positive in validation datasets. p-values for chi squared test of independence and for Fisher's exact test are reported. Figure S8. Kaplan-Meier survival analysis of patients from the Buffa dataset classified as highhMAPK or low-hMAPK by the hMAPK-microRNA recurrence signature. (left) All patients,(middle) patients with ER+ disease (right)patients with ER- disease. Kaplan-Meier curves: dashed= low-hMAPK-microRNA, solid= high-hMAPK-microRNA; logrank test p-values are indicated Table S1A. Clinical characteristics of Buffa training dataset Table S1B. Clinical characteristics of TCGA training dataset Table S1C. Clinical characteristics of Enerly training dataset Table S1D. Clinical characteristics of METABRIC training dataset Table S2. The hMAPK-microRNA signature: microRNAs that are commonly differentially expressed in tumors classified as hMAPK-mRNA vs those classified as not-hMAPK-RNA in the TCGA and Buffa training datasets. Table S3. RPPA protein expression for proteins with significant differential expression between cancers from TCGA dataset classified as hMAPK-miRNA or not-hMAPK-miRNA. T-statistic, pvalue, and permutation adjusted p-value given. Yellow= upregulated in hMAPK-miRNA; blue: downregulated in hMAPK-miRNA Table S4. The hMAPK-microRNA recurrence signature. Table S5. Multivariate analysis of hMAPK-microRNA recurrence signature in METABRIC dataset and Lyng datasets.Table S6. Multivariate analysis of hMAPK-microRNA recurrence signature in METABRIC dataset, patients stratified according to treatment status. Table S7. hMAPK-microRNA survival signature.</p

Supplemental Materials and Methods from A Novel MAPK–microRNA Signature Is Predictive of Hormone-Therapy Resistance and Poor Outcome in ER-Positive Breast Cancer

Supplemental Materials and Methods. Additional description of common materials and methods not included in main body of manuscript</p

Additional file 1: of Probiotic Bifidobacterium strains and galactooligosaccharides improve intestinal barrier function in obese adults but show no synergism when used together as synbiotics

Table S1. Baseline demographic and metabolic characteristics of study subjects by treatment group. Table S2. Differences in gastrointestinal symptoms by treatment group. Table S3. Percent change in intestinal permeability in subjects by treatment group. Table S4. Differences in markers of endotoxemia by treatment group. Table S5. Percent change in anthropometrics and metabolic markers in subjects by treatment group. (DOCX 49 kb

Additional file 2: of Probiotic Bifidobacterium strains and galactooligosaccharides improve intestinal barrier function in obese adults but show no synergism when used together as synbiotics

Figure S1. Phylogenetic analysis of OTU_1 and six closely related Bifidobacterium OTUs that might have competed with OTU_1 for the niche in the GI tract using Maximum Likelihood method. OTU_2281 Lactobacillus animalis was selected as the Outgroup. Numbers indicate branch lengths. (PPTX 41 kb

Correlation networks identified that intersect epigenetic pathways/signaling pathways with patient specific DE genes.

<p>Connections were calculated for gene-gene pairs emanating from epigenetic pathways or genes in the Notch, SHH, or WNT pathways and genes that were Differentially Expressed in each patient.</p

Additional file 1: of Role of whole grains versus fruits and vegetables in reducing subclinical inflammation and promoting gastrointestinal health in individuals affected by overweight and obesity: a randomized controlled trial

Treatment foods supplied to subjects. (XLSX 10 kb

Microbiome composition of DFU samples.

Pie charts showing the relative abundance and composition of the most dominant phyla according to sampling location and time point.</p

Additional file 2: of Role of whole grains versus fruits and vegetables in reducing subclinical inflammation and promoting gastrointestinal health in individuals affected by overweight and obesity: a randomized controlled trial

Gastrointestinal symptom questionnaire results. (XLSX 10 kb