collected for biochemical and molecular microbiological analyses at baseline, week 4 and
|
|
|
- Melinda Day
- 5 years ago
- Views:
Transcription
1 SUPPLEMENTARY FIGURE LEGENDS Figure S1 Study design. Figure was adapted from Paramsothy et al. 5 Blood and stool samples were collected for biochemical and molecular microbiological analyses at baseline, week 4 and week 8 of therapy (blinded and open label), as well as 8 weeks following conclusion of therapy. Mucosal samples were collected at baseline and at week8 conclusion of therapy (blinded and open label). Figure S2 Microbial signatures associated with therapy primary outcome. A: Cluster analysis of taxonomic profiles arising from shotgun sequencing. Raw data was analysed using MetaPhlan2, relative abundances square-root transformed and a Bray-Curtis resemblance matrix was generated. B: Box plot of number of OTUs arising from 16S rrna gene profiling of mucosal biopsy DNA extracts. Blinded and open label patients are combined but stratified according to the primary endpoint. Comparisons across groups did not reach significance. Tx0: Baseline sample (week 0) of FMT patients; Tx8: Week 8 sample of FMT patients; P8: Week 8 sample of open label patients on placebo; PTx8: Week 8 sample of open label patients on FMT; _Y: Patients positive outcomes according to the primary endpoint; _N: Patients with negative outcomes according to the primary endpoint. Figure S3 Microbial signatures associated with therapy primary outcome. A: Microbial taxa that discriminated positive and negative therapy outcomes in blinded patients on FMT. B: Microbial taxa that discriminated positive and negative therapy outcomes in open label patients on FMT. Results are from 16S rrna gene profiling of faecal samples. Differences 1
2 were calculated using LEfSE with outcome as a predictor. Only taxa that showed a Linear Discriminant Analysis (LDA) score >2 and P<0.05 were plotted. Tx8R: Week 8 FMT in blinded patients with positive outcome; Tx8: Week 8 FMT in blinded patients with negative outcome; PTx8R: Week 8 FMT in open label patients with positive outcome; PTx8: Week 8 FMT in open label patients with negative outcome. Figure S4 Microbial signatures associated with therapy primary outcome in the 16S amplicon sequencing data sets. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa abundances were created using DESeq2. Black dots indicate significant results with unadjusted P-values. Tx0: Baseline sample (week 0) of FMT patients; Tx8: Week 8 sample of FMT patients; P8: Week 8 sample of open label patients on placebo; PTx8: Week 8 sample of open label patients on FMT; _Y: Patients positive outcomes according to the primary endpoint; _N: Patients with negative outcomes according to the primary endpoint. 2
3 Figure S5 Microbial signatures associated with endoscopic remission. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. E: Taxa that discriminated positive and negative therapy outcomes in shotgun metagenomic profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. Blinded and open label were combined across all analysis. All negative binomial models for taxa abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S6 Microbial signatures associated with endoscopic response. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. E: Taxa that discriminated positive and negative therapy outcomes in shotgun metagenomic profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. Blinded and open label were combined across all analysis. All negative binomial models for taxa abundances were 3
4 created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S7 Microbial signatures associated with clinical remission. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. E: Taxa that discriminated positive and negative therapy outcomes in shotgun metagenomic profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. Blinded and open label were combined across all analysis. All negative binomial models for taxa abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S8 Pathway counts of carbohydrate digestion and absorption for PICRUSt predictions from 16S rrna gene profiling of mucosal microbiome. Figure S9 Microbial functional changes associated with other therapeutic outcomes. A: KEGG pathways that discriminated positive and negative outcomes for endoscopic remission. B: MetaCyc pathways that discriminated positive and negative outcomes for endoscopic 4
5 remission. C: KEGG pathways that discriminated positive and negative outcomes for endoscopic response. D: MetaCyc pathways that discriminated positive and negative outcomes for endoscopic response. E: KEGG pathways that discriminated positive and negative outcomes for clinical remission. F: MetaCyc pathways that discriminated positive and negative outcomes for clinical remission. Pathway abundances from shotgun metagenomic profiling of faecal samples were calculated using HUMANn2 and modelled using negative binomial GLMs with outcome as a predictor. Blinded and open label were combined across all analysis. All negative binomial models for pathway abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S10 Microbial signatures associated with therapy primary outcome when blinded and open label were stratified. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa abundances were created using DESeq2. Black dots indicate significant results with unadjusted P-values. Tx8_Y: Week 8 FMT in blinded patients with positive outcome; Tx8_N: Week 8 FMT in blinded patients with negative outcome; PTx8_Y: Week 8 FMT in open label patients with positive outcome; PTx8_N: Week 8 FMT in open label patients with negative outcome. 5
6 Figure S11 Relative abundance of Prevotella copri in shotgun metagenomic profiling of faecal samples following stratification of patients according to primary outcome. Figure S12 Metabolites altered by FMT therapy. Metabolites found to be increased post-fmt as compared to baseline, with consistent fold-change observed across mean-pair and group-mean analyses. These five metabolites were selected as they were not altered by placebo (i.e. no change between baseline and post-placebo) and they were found to be different between post- FMT and post-placebo. For a complete list of metabolites altered by FMT, see Table S2. Metabolites with a p<0.05 and q<0.2 across the comparisons involving post-fmt were reported. Statistical test was performed using a paired t-test. q-value was calculated after FDR adjustment of p-value. Figure S13 Donor profiles associated with primary outcome. A: Shannon s diversity index of donor batches according to shotgun metagenomic sequencing and MetaPhlan2. Red: >50% positive outcomes; black: >50% negative outcomes. B: Non-metric multidimensional scaling plot of Bray-Curtis matrix of square-root transformed species relative abundances in donor batches. Data is from shotgun metagenomic sequencing and MetaPhlan2. Red: >50% positive outcomes; black: >50% negative outcomes. C: OTUs that discriminated donor batches with positive and negative outcomes in 16S rrna gene sequencing. Relative abundances of taxa were modelled using negative binomial GLMs with outcome as a predictor. D: OTUs that discriminated donor batches with positive and negative outcomes in 16S rrna transcript 6
7 sequencing. Relative abundances of taxa were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P- values. E: Non-metric multidimensional scaling plot of Bray-Curtis matrix of transformed KEGG pathway abundances in donor batches. Data is from shotgun metagenomic sequencing and HUMANn2. Red: >50% positive outcomes; black: >50% negative outcomes. F: Nonmetric multidimensional scaling plot of Bray-Curtis matrix of transformed MetaCyc pathway abundances in donor batches. Data is from shotgun metagenomic sequencing and HUMANn2. Red: >50% positive outcomes; black: >50% negative outcomes. Figure S14 Donor profiles associated with endoscopic remission. A: Mosaic plot of donor batches classified according to number of positive endoscopic remissions. If a high number of the patients receiving a particular donor batch showed positive outcome, the donor batch was allocated to the DonorRemission = Yes group (Red), while all other donors were allocated to the DonorRemission = No group (Black). B: Shannon s diversity index of donor batches according to 16S rrna gene profiling. Red: >50% positive outcomes; black: >50% negative outcomes. C: Shannon s diversity index of donor batches according to 16S rrna transcript profiling. Red: >50% positive outcomes; black: >50% negative outcomes. D: Non-metric multidimensional scaling plot of Bray-Curtis matrix of square-root transformed OTU relative abundances in donor batches. Circle corresponds to 16S rrna gene profiling; Triangle corresponds to 16S rrna transcript profiling. Red: >50% positive outcomes; black: >50% negative outcomes. E: OTUs that discriminated donor batches with positive and negative outcomes in 16S rrna gene sequencing. F: OTUs that discriminated donor batches with positive and negative outcomes in 16S rrna transcript sequencing. G: Taxa that 7
8 discriminated donor batches with positive and negative outcomes in shotgun metagenomic profiling. Relative abundances of taxa were modelled using negative binomial GLMs with outcome as a predictor. H: KEGG pathways that discriminated donor batches with positive and negative outcomes in shotgun metagenomic profiling. I: MetaCyc pathways that discriminated donor batches with positive and negative outcomes in shotgun metagenomic profiling. Pathway abundances were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa and pathway abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S15 Donor profiles associated with endoscopic response and clinical remission. A: Mosaic plot of donor batches classified according to number of positive endoscopic response outcomes. B: Mosaic plot of donor batches classified according to number of positive clinical remission outcomes. If more than 50% of the patients receiving a particular donor batch showed positive outcome, the donor batch was allocated to the DonorRemission = Yes group (Red), while all other donors were allocated to the DonorRemission = No group (Black). C: Shannon s diversity index of donor batches according to shotgun metagenomic sequencing and MetaPhlan2. Red: >50% positive endoscopic response outcomes; black: >50% negative endoscopic response outcomes. D: Shannon s diversity index of donor batches according to shotgun metagenomic sequencing and MetaPhlan2. Red: >50% positive clinical remission outcomes; black: >50% negative clinical remission outcomes. E: Taxa that discriminated donor batches with positive and negative clinical remission outcomes in shotgun metagenomic profiling. Relative abundances of taxa were modelled using negative binomial GLMs with outcome as a predictor. F: KEGG pathways that discriminated donor batches 8
9 with positive and negative clinical remission outcomes in shotgun metagenomic profiling. G: MetaCyc pathways that discriminated donor batches with positive and negative clinical remission outcomes in shotgun metagenomic profiling. Pathway abundances were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa and pathway abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S16 Changes in microbial diversity and composition with faecal microbiota transplantation. A: Diagnostic plot of mixed model for number of OTUs. B: Diagnostic plot of mixed model for Shannon s diversity index. Models: Diversity ~ Time * TreatmentGroup + random(patient.id). X-axis shows fitted values (expected values from model) while Y-axis shows the residuals (difference between observed and expected values). C: Changes in number of OTUs in open label patients across sample groups following data fitting into the model. D: Changes in Shannon s diversity index in open label patients across sample groups following data fitting into the model. E: Non-metric multidimensional scaling plot of Bray- Curtis matrix of square-root transformed OTU relative abundances. Blinded and open-label time points were combined. Samples were stratified according to type of therapy (P: placebo; Tx: FMT). F: Changes in z-score abundances of OTUs across type of treatment. OTUs correspond to those shown in heatmap in Figure 6E. Tx0: Baseline sample (week 0) of blinded FMT patients; Tx4: Week 4 sample of blinded FMT patients; Tx8: Week 8 sample of blinded FMT patients; PTx0: Baseline sample (week 0) of open label patients; P4: Week 4 sample of open label patients on placebo; P8: Week 8 sample of open label patients on placebo; PTx4: Week 4 sample of open label patients on 9
10 FMT; PTx8: Week 8 sample of open label patients on FMT; PTxF: Follow-up sample 8 weeks post-fmt of open label patients. 10
Are microbes the missing piece of the gut comfort puzzle?
Priority Research Programme Foods for improving gut function and comfort Are microbes the missing piece of the gut comfort puzzle? Wayne Young Senior Scientist, AgResearch Host institution Foods for gut
Supplementary Materials
Supplementary Materials Supplementary figure 1. Taxonomic representation summarized at genus level. Fecal microbiota from a separate set of Jackson and Harlan mice prior to irradiation. A taxon was included
THE ROLE OF MICROBIOME IN IBD
Disclosures THE ROLE OF MICROBIOME IN IBD Janssen UCB No relevance to the talk Subra Kugathasan, MD Professor of Pediatrics & Human Genetics Marcus Professor of Pediatric Gastroenterology Emory University
Gut Microbiomes of Malawian Twin Pairs Discordant for Kwashiorkor
Gut Microbiomes of Malawian Twin Pairs Discordant for Kwashiorkor Michelle I. Smith et al. Science 339, 548 (2013) Dept Meeting, 28 May 2013, M. UMEZAKI ABSTRACT. Kwashiorkor, an enigmatic form of severe
Supplementary Figure 1. Metabolic landscape of cancer discovery pipeline. RNAseq raw counts data of cancer and healthy tissue samples were downloaded
Supplementary Figure 1. Metabolic landscape of cancer discovery pipeline. RNAseq raw counts data of cancer and healthy tissue samples were downloaded from TCGA and differentially expressed metabolic genes
The Gut Microbiota: Evidence For Gut Microbes as Contributors to Weight Gain
The Gut Microbiota: Evidence For Gut Microbes as Contributors to Weight Gain Michael T. Bailey, Ph.D. Center for Microbial Pathogenesis The Research Institute, Nationwide Children s Hospital Department
Microbiome in You: Optimizing Gut Bacteria for Better IBD Management
Microbiome in You: Optimizing Gut Bacteria for Better IBD Management KT Park, M.D., M.S. Assistant Professor Co-Director, Stanford Children s Inflammatory Bowel Disease Center Stanford University School
Comparison of the Respiratory Microbiome in Healthy Nonsmokers and Smokers
Comparison of the Respiratory Microbiome in Healthy Nonsmokers and Smokers Weiguang (Wayne) Mao Morris, A., Beck, J. M., Schloss, P. D., Campbell, T. B., Crothers, K., Curtis, J. L.,... & Weinstock, G.
Nature Immunology: doi: /ni Supplementary Figure 1. Transcriptional program of the TE and MP CD8 + T cell subsets.
Supplementary Figure 1 Transcriptional program of the TE and MP CD8 + T cell subsets. (a) Comparison of gene expression of TE and MP CD8 + T cell subsets by microarray. Genes that are 1.5-fold upregulated
Understandable Statistics
Understandable Statistics correlated to the Advanced Placement Program Course Description for Statistics Prepared for Alabama CC2 6/2003 2003 Understandable Statistics 2003 correlated to the Advanced Placement
Supplementary Online Content
Supplementary Online Content Kao D, Roach B, Silva M, et al. Effect of oral capsule vs colonoscopy-delivered fecal microbiota transplantation on recurrent Clostridium difficile infection: a randomized
Microbiome GI Disorders
Microbiome GI Disorders Prof. Ram Dickman Neurogastroenterology Unit Rabin Medical Center Israel 1 Key Points Our gut microbiota Were to find them? Symbiosis or Why do we need them? Dysbiosis or when things
EPIDEMIOLOGICAL INVESTIGATIONS IN UNDERSTANDING HEALTH IMPACTS OF HUMAN BUILT ENVIRONMENT INTERACTIONS
EPIDEMIOLOGICAL INVESTIGATIONS IN UNDERSTANDING HEALTH IMPACTS OF HUMAN BUILT ENVIRONMENT INTERACTIONS JOANNE E. SORDILLO, SCD HARVARD MEDICAL SCHOOL, BRIGHAM AND WOMEN S HOSPITAL CHANNING DIVISION OF
Microbial Adaptations of the Microbiome. Damian R. Plichta, PhD Director of Bioinformatics
Microbial Adaptations of the Microbiome Damian R. Plichta, PhD Director of Bioinformatics Tailored shotgun metagenomics Microbiome model for biomarker discovery microbiome incoming species conditional
Role of the Gut Microbiota in Autoimmunity
Role of the Gut Microbiota in Autoimmunity Pavan Bhargava, MD - Neuroimmunology Fellow Division of Neuroimmunology and Neurological Infections Johns Hopkins University, Baltimore, MD. May, 2015 None Disclosures
Alpha diversity estimate coefficient P value No. of OTUs shannon chao PD_whole_tree
Supplementary Table 1. Paired differences between swab-1 and swab-2 in estimates of alpha diversity. Swab-1 mean Swab-2 paired Student t test P value paired Wilcoxon signed-rank test No. of unique OTUs
Gastric microbial community profiling reveals a dysbiotic cancer-associated microbiota
Gastric microbial community profiling reveals a dysbiotic cancer-associated microbiota Rui M. Ferreira 1,2, Joana Pereira-Marques 1,2,3, Ines Pinto-Ribeiro 1,2,4, Jose L. Costa 1,2,4, Fatima Carneiro 1,2,4,5,
Targeted Therapies and Gut Microbiome in Renal Cell Carcinoma (RCC)
Targeted Therapies and Gut Microbiome in Renal Cell Carcinoma (RCC) Nazli Dizman, MD Postdoctoral Fellow Department of Medical oncology & Experimental Therapeutics City of Hope Comprehensive Center December
Supplementary Figures. Supplementary Figure 1. Treatment schematic of SIV infection and ARV and PP therapies.
Supplementary Figures Supplementary Figure 1. Treatment schematic of SIV infection and ARV and PP therapies. Supplementary Figure 2. SIV replication and CD4 + T cell count. (A) Log SIVmac239 copies/ml
Lecture 21. RNA-seq: Advanced analysis
Lecture 21 RNA-seq: Advanced analysis Experimental design Introduction An experiment is a process or study that results in the collection of data. Statistical experiments are conducted in situations in
SYN-004 (ribaxamase) Prevents Clostridium difficile Infection and Antimicrobial Resistance
SYN-004 (ribaxamase) Prevents Clostridium difficile Infection and Antimicrobial Resistance John F. Kokai-Kun Anaerobe Las Vegas, NV July 12, 2018 Forward-Looking Statements This presentation includes forward-looking
Maternal Secretor Status and Child Microbiota Composition. Paula Smith-Brown Paediatric Dietitian & PhD Scholar
Maternal Secretor Status and Child Microbiota Composition Paula Smith-Brown Paediatric Dietitian & PhD Scholar The First 1000 Days Development of Microbiota Yatsunenko et al. Nature 000, 1-7 (2012) WHO,
Matched Analysis of Differences of Microbiomes in Japanese vs. Non-Japanese Patients from an Ulcerative Colitis Study
Matched Analysis of Differences of Microbiomes in Japanese vs. Non-Japanese Patients from an Ulcerative Colitis Study Davit Sargsyan 1,2, Javier Cabrera 2, Kathy Mutambanengwe 3, Dhammika Amaratunga 2,
SUPPLEMENTARY INFORMATION
doi:10.1038/nature12198 1. Supplementary results 1.1. Associations between gut microbiota, glucose control and medication Women with T2D who used metformin had increased levels of Enterobacteriaceae (i.e.
2/13/2018. What s bugging you? Relevance of the Human Microbiome in Health and Disease Heidi H. Kong, MD, MHSc, FAAD Dermatology Branch
What s bugging you? Relevance of the Human Microbiome in Health and Disease Heidi H. Kong, MD, MHSc, FAAD Dermatology Branch DISCLOSURE OF RELATIONSHIPS WITH INDUSTRY Heidi H Kong, MD, MHSc, FAAD U017:
NIH Public Access Author Manuscript Science. Author manuscript; available in PMC 2012 June 06.
NIH Public Access Author Manuscript Published in final edited form as: Science. 2011 October 7; 334(6052): 105 108. doi:10.1126/science.1208344. Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes
chapter 1 - fig. 2 Mechanism of transcriptional control by ppar agonists.
chapter 1 - fig. 1 The -omics subdisciplines. chapter 1 - fig. 2 Mechanism of transcriptional control by ppar agonists. 201 figures chapter 1 chapter 2 - fig. 1 Schematic overview of the different steps
Corporate Overview. August Leading the Microbiome Revolution
Corporate Overview August 2018 Leading the Microbiome Revolution Forward looking statements Some of the statements in this presentation constitute forward looking statements under the Private Securities
Beyond the Scope: The Microbiome & The Future of Gastroenterology
Beyond the Scope: The Microbiome & The Future of Gastroenterology Robynne Chutkan, MD, FASGE Digestive Center for Wellness, LLC MedStar Georgetown University Hospital Rapid Identification of Microbes
Beyond the Scope: An Integrative Gastroenterologist s Approach to Digestive Disorders
Beyond the Scope: An Integrative Gastroenterologist s Approach to Digestive Disorders Robynne Chutkan, MD, FASGE Digestive Center for Wellness, LLC MedStar Georgetown University Hospital The real voyage
Per the study design and a limitation of this analysis, swabs were not tested for HPV types 6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 SUPPLEMENTARY MATERIAL Progression, regression, and clearance of HPV6/11 infections Per the study design and a limitation of this analysis, swabs
Gut Microbes Are Essential: What to Feed Them? David M. Klurfeld USDA Agricultural Research Service Beltsville, MD
Gut Microbes Are Essential: What to Feed Them? David M. Klurfeld USDA Agricultural Research Service Beltsville, MD The idea of a beneficial microbiome is not new Life would not long remain possible in
What Have We Learned About the Microbiome in Pediatric IBD?
What Have We Learned About the Microbiome in Pediatric IBD? Ted Denson, MD Cincinnati Children s Hospital Medical Center and the University of Cincinnati College of Medicine Disclosures: NIH, CCF, AbbVie
Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes
Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes Gary D. Wu, 1 * Jun Chen, 2,3 Christian Hoffmann, 4,5 Kyle Bittinger, 4 Ying-Yu Chen, 1 Sue A. Keilbaugh, 1 Meenakshi Bewtra, 1,2 Dan Knights,
Influence of maternal breast milk ingestion on acquisition of the intestinal microbiome in preterm infants
Influence of maternal breast milk ingestion on acquisition of the intestinal microbiome in preterm infants The Harvard community has made this article openly available. Please share how this access benefits
Impact of early events and lifestyle on the gut microbiota and metabolic phenotypes in young school-age children
Zhong et al. Microbiome (2019) 7:2 https://doi.org/10.1186/s40168-018-0608-z RESEARCH Impact of early events and lifestyle on the gut microbiota and metabolic phenotypes in young school-age children Open
Helminth Colonization Is Associated with Increased Diversity of the Gut Microbiota
Helminth Colonization Is Associated with Increased Diversity of the Gut Microbiota Soo Ching Lee 1, Mei San Tang 2, Yvonne A. L. Lim 1 *, Seow Huey Choy 1, Zachary D. Kurtz 3,4, Laura M. Cox 3,4, Uma Mahesh
SUPPLEMENTARY APPENDIX
SUPPLEMENTARY APPENDIX 1) Supplemental Figure 1. Histopathologic Characteristics of the Tumors in the Discovery Cohort 2) Supplemental Figure 2. Incorporation of Normal Epidermal Melanocytic Signature
Supplementary Figures
Supplementary Figures Supplementary Figure 1. Heatmap of GO terms for differentially expressed genes. The terms were hierarchically clustered using the GO term enrichment beta. Darker red, higher positive
Structural modulation of the gut microbiota and the relationship with body weight: compared evaluation of liraglutide and saxagliptin treatment
Structural modulation of the gut microbiota and the relationship with body weight: compared evaluation of liraglutide and saxagliptin treatment Lin Wang, Peicheng Li, Zhaosheng Tang, Xinfeng Yan, Bo Feng
LETTER. Diet-induced extinctions in the gut microbiota compound over generations
doi:1.138/nature1654 Diet-induced extinctions in the gut microbiota compound over generations Erica D. Sonnenburg 1 *, Samuel A. Smits 1 *, Mikhail Tikhonov 2,3, Steven K. Higginbottom 1, Ned S. Wingreen
Sub-clinical detection of gut microbial biomarkers of obesity and type 2 diabetes
Yassour et al. Genome Medicine (2016) 8:17 DOI 10.1186/s13073-016-0271-6 RESEARCH Open Access Sub-clinical detection of gut microbial biomarkers of obesity and type 2 diabetes Moran Yassour 1,2, Mi Young
Fecal Microbial Transplant Effect on Clinical Outcomes and Fecal Microbiome in Active Crohn's Disease
Fecal Microbial Transplant Effect on Clinical Outcomes and Fecal Microbiome in Active Crohn's Disease March 2015 - Volume 21 - Issue 3 - p 556 563 doi: 10.1097/MIB.0000000000000307 Original Clinical Articles
Applications. DSC 410/510 Multivariate Statistical Methods. Discriminating Two Groups. What is Discriminant Analysis
DSC 4/5 Multivariate Statistical Methods Applications DSC 4/5 Multivariate Statistical Methods Discriminant Analysis Identify the group to which an object or case (e.g. person, firm, product) belongs:
Going With Your Gut: The Microbiome and You
Going With Your Gut: The Microbiome and You Robert T. Schooley, MD Professor of Medicine University of California San Diego San Diego, California Learning Objectives After attending this presentation,
The gut microbiota in conventional and serrated precursors of colorectal cancer
Peters et al. Microbiome (2016) 4:69 DOI 10.1186/s40168-016-0218-6 RESEARCH The gut microbiota in conventional and serrated precursors of colorectal cancer Open Access Brandilyn A. Peters 1, Christine
Nature Genetics: doi: /ng Supplementary Figure 1. SEER data for male and female cancer incidence from
Supplementary Figure 1 SEER data for male and female cancer incidence from 1975 2013. (a,b) Incidence rates of oral cavity and pharynx cancer (a) and leukemia (b) are plotted, grouped by males (blue),
Using Phylogenetic Structure to Assess the Evolutionary Ecology of Microbiota! TJS! iseem Call! April 2015!
Using Phylogenetic Structure to Assess the Evolutionary Ecology of Microbiota! TJS! iseem Call! April 2015! How are Microbes Distributed In Nature?! A major question in microbial ecology! Used to assess
Award Number: W81XWH TITLE: PRINCIPAL INVESTIGATOR: Rafail Kushak, PhD, Dr. Sc.
AD Award Number: W81XWH-10-1-0477 TITLE: PRINCIPAL INVESTIGATOR: Rafail Kushak, PhD, Dr. Sc. CONTRACTING ORGANIZATION: Massachusetts General Hospital Boston, Massachusetts 02114-2621 REPORT DATE: May 2013
Nature Immunology: doi: /ni Supplementary Figure 1
Supplementary Figure 1 NLRP12 is downregulated in biopsy samples from patients with active ulcerative colitis (UC). (a-g) NLRP12 expression in 7 UC mrna profiling studies deposited in NCBI GEO database.
Stable Isotope Probing of gut bacteria RNA. Wayne Young. Identifying gut bacteria that can use sialic acid using an RNA-SIP approach
Stable Isotope Probing of gut bacteria RNA Identifying gut bacteria that can use sialic acid using an RNA-SIP approach Wayne Young Food Nutrition & Health Team Food & Bio-based Products Group AgResearch
RNA-Seq Preparation Comparision Summary: Lexogen, Standard, NEB
RNA-Seq Preparation Comparision Summary: Lexogen, Standard, NEB CSF-NGS January 22, 214 Contents 1 Introduction 1 2 Experimental Details 1 3 Results And Discussion 1 3.1 ERCC spike ins............................................
Multilevel social structure and diet shape the gut microbiota of the gelada monkey, the only grazing primate
Trosvik et al. Microbiome (2018) 6:84 https://doi.org/10.1186/s40168-018-0468-6 RESEARCH Open Access Multilevel social structure and diet shape the gut microbiota of the gelada monkey, the only grazing
Growth and Morbidity of Gambian Infants are Influenced by Maternal Milk Oligosaccharides and Infant Gut Microbiota
Growth and Morbidity of Gambian Infants are Influenced by Maternal Milk Oligosaccharides and Infant Gut Microbiota Authors: Jasmine C. C. Davis 1,2, Zachery T. Lewis 2,3, Sridevi Krishnan 4, Robin M. Bernstein
Unit 1 Exploring and Understanding Data
Unit 1 Exploring and Understanding Data Area Principle Bar Chart Boxplot Conditional Distribution Dotplot Empirical Rule Five Number Summary Frequency Distribution Frequency Polygon Histogram Interquartile
Consistent and Reproducible Production of a Microbiota-based Drug for Recurrent C. difficile
Consistent and Reproducible Production of a Microbiota-based Drug for Recurrent C. difficile Infection: Application of a Novel Diagnostic for Dysbiosis Courtney Jones, BS Rebiotix Inc. Roseville, MN USA
SUPPLEMENTARY INFORMATION
SUPPLEMENTARY INFORMATION doi:10.1038/nature13421 Supplementary Notes Anthropologic assessment The study population resided in the Mirpur slum of Dhaka, Bangladesh (23.8042 N 90.3667 E) in a catchment
Gene expression profiling predicts clinical outcome of prostate cancer. Gennadi V. Glinsky, Anna B. Glinskii, Andrew J. Stephenson, Robert M.
SUPPLEMENTARY DATA Gene expression profiling predicts clinical outcome of prostate cancer Gennadi V. Glinsky, Anna B. Glinskii, Andrew J. Stephenson, Robert M. Hoffman, William L. Gerald Table of Contents
Model of IBD pathogenesis, 2002
Model of IBD pathogenesis, 2002 Model of IBD pathogenesis, 2012 Applying an ecosystem of microbial community analysis tools Curtis Huttenhower, Rob Knight, Dirk Gevers With Rob Beiko 4 The microbiome
Life in the small intestine: the forgotten microbiome?
Life in the small intestine: the forgotten microbiome? Erin R Shanahan A,B,C, Gerald Holtmann A and Mark Morrison B A Department of Gastroenterology and Hepatology, Princess Alexandra Hospital and Faculty
Aesthetic Response to Color Combinations: Preference, Harmony, and Similarity. Supplementary Material. Karen B. Schloss and Stephen E.
Aesthetic Response to Color Combinations: Preference, Harmony, and Similarity Supplementary Material Karen B. Schloss and Stephen E. Palmer University of California, Berkeley Effects of Cut on Pair Preference,
Supplemental Information. Infant and Adult Gut Microbiome. and Metabolome in Rural Bassa. and Urban Settlers from Nigeria
Cell Reports, Volume 23 Supplemental Information Infant and Adult Gut Microbiome and Metabolome in Rural Bassa and Urban Settlers from Nigeria Funmilola A. Ayeni, Elena Biagi, Simone Rampelli, Jessica
Fungal microbiome. Gwen Falony BVMDM symposium 19 th of November 2015
Fungal microbiome Gwen Falony BVMDM symposium 19 th of November 2015 How human are you? There are 100.000 times more bacteria on your body than there are humans on earth. You have 10 times more bacterial
How to interpret scientific & statistical graphs
How to interpret scientific & statistical graphs Theresa A Scott, MS Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott 1 A brief introduction Graphics:
Nature Structural & Molecular Biology: doi: /nsmb.2419
Supplementary Figure 1 Mapped sequence reads and nucleosome occupancies. (a) Distribution of sequencing reads on the mouse reference genome for chromosome 14 as an example. The number of reads in a 1 Mb
Nature Genetics: doi: /ng Supplementary Figure 1
Supplementary Figure 1 Replicability of blood eqtl effects in ileal biopsies from the RISK study. eqtls detected in the vicinity of SNPs associated with IBD tend to show concordant effect size and direction
Comparing Ecological Communities Part Two: Ordination. Ordination vs. classification. Species responses to gradients. Read: Ch.
Comparing Ecological Communities Part Two: Ordination Read: Ch. 15, GSF 10/19/09 1 Ordination vs. classification The main purpose of both multivariate methods is to interpret patterns in species composition
RNA-seq. Differential analysis
RNA-seq Differential analysis Data transformations Count data transformations In order to test for differential expression, we operate on raw counts and use discrete distributions differential expression.
Multi-view learning and deep learning for heterogeneous biological data to maintain oral health Imangaliyev, Sultan
UvA-DARE (Digital Academic Repository) Multi-view learning and deep learning for heterogeneous biological data to maintain oral health Imangaliyev, Sultan Link to publication Citation for published version
Breast tissue, oral and urinary microbiomes in breast cancer
/, Advance Publications 2017 Breast tissue, oral and urinary microbiomes in breast cancer Hannah Wang 1,2, Jessica Altemus 1, Farshad Niazi 1, Holly Green 4, Benjamin C. Calhoun 5, Charles Sturgis 5, Stephen
Supplementary Figure 1: Features of IGLL5 Mutations in CLL: a) Representative IGV screenshot of first
Supplementary Figure 1: Features of IGLL5 Mutations in CLL: a) Representative IGV screenshot of first intron IGLL5 mutation depicting biallelic mutations. Red arrows highlight the presence of out of phase
Association of household siblings with nasal and fecal microbiota in infants
Association of household siblings with nasal and fecal microbiota in infants Kohei Hasegawa, Harvard Medical School Rachel W. Linnemann, Emory University Jonathan M. Mansbach, Boston Children's Hospital
Microbiome and Asthma
제 12 차천식연구회 COPD 연구회공동심포지엄 Microbiome and Asthma 한양대학교병원호흡기알레르기내과 김상헌 Disclosure 내용 1 Lung Microbiome 2 Lung Microbiome and Asthma 3 Gut Microbiome and Asthma Microbiome and Microbiota human microbiome
Fructo-oligosaccharides in Crohn s disease: a prospective randomised double blind controlled trial
Fructo-oligosaccharides in Crohn s disease: a prospective randomised double blind controlled trial Principal investigator: Dr James Lindsay St Bartholomew's Hospital and The Royal London Hospital Queen
Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study
Kang et al. Microbiome (2017) 5:10 DOI 10.1186/s40168-016-0225-7 RESEARCH Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study Open Access
Systematic reviews and meta-analyses of observational studies (MOOSE): Checklist.
Systematic reviews and meta-analyses of observational studies (MOOSE): Checklist. MOOSE Checklist Infliximab reduces hospitalizations and surgery interventions in patients with inflammatory bowel disease:
CHL 5225 H Advanced Statistical Methods for Clinical Trials. CHL 5225 H The Language of Clinical Trials
CHL 5225 H Advanced Statistical Methods for Clinical Trials Two sources for course material 1. Electronic blackboard required readings 2. www.andywillan.com/chl5225h code of conduct course outline schedule
Microbiome as a marker for CRC screening
WEO CRC Screening Committee Meeting Microbiome as a marker for CRC screening Dr Sunny H Wong MBChB (Hons), DPhil, MRCP, FHKCP, FHKAM (Medicine) Assistant Professor Institute of Digestive Disease Department
Supplementary Figure 1. ALVAC-protein vaccines and macaque immunization. (A) Maximum likelihood
Supplementary Figure 1. ALVAC-protein vaccines and macaque immunization. (A) Maximum likelihood tree illustrating CRF01_AE gp120 protein sequence relationships between 107 Envs sampled in the RV144 trial
Observed # of species (normalized) Chao 1 (richness) Nonsmoker 21 Male 2418 (337) 67 (33)
Additional Files The human laryngeal microbiome: effects of cigarette smoke and reflux Marie E. Jetté, Kimberly A. Dill-McFarland, Alissa S. Hanshew, Garret Suen, Susan L. Thibeault Table S1. Detailed
Supplementary Material. for. A cell-centered meta-analysis reveals baseline predictors of anti-tnf nonresponse. in biopsy and blood of IBD patients
Supplementary Material for A cell-centered meta-analysis reveals baseline predictors of anti-tnf nonresponse in biopsy and blood of IBD patients This document includes Supplementary Methods, Figures and
Differences in gut microbial composition correlate with regional brain volumes in irritable bowel syndrome
Washington University School of Medicine Digital Commons@Becker Open Access Publications 2017 Differences in gut microbial composition correlate with regional brain volumes in irritable bowel syndrome
Chapter 1: Exploring Data
Chapter 1: Exploring Data Key Vocabulary:! individual! variable! frequency table! relative frequency table! distribution! pie chart! bar graph! two-way table! marginal distributions! conditional distributions!
Supplemental Figure S1. Expression of Cirbp mrna in mouse tissues and NIH3T3 cells.
SUPPLEMENTAL FIGURE AND TABLE LEGENDS Supplemental Figure S1. Expression of Cirbp mrna in mouse tissues and NIH3T3 cells. A) Cirbp mrna expression levels in various mouse tissues collected around the clock
SUPPLEMENTARY INFORMATION
doi:10.1038/nature10866 a b 1 2 3 4 5 6 7 Match No Match 1 2 3 4 5 6 7 Turcan et al. Supplementary Fig.1 Concepts mapping H3K27 targets in EF CBX8 targets in EF H3K27 targets in ES SUZ12 targets in ES
Diet-microbiome-health interactions in older people
Diet-microbiome-health interactions in older people Paul W. O Toole Prof. Microbial Genomics School of Microbiology, Univ. College Cork, Ireland APC Microbiome Institute, Univ. College Cork, Ireland http://apc.ucc.ie
fl/+ KRas;Atg5 fl/+ KRas;Atg5 fl/fl KRas;Atg5 fl/fl KRas;Atg5 Supplementary Figure 1. Gene set enrichment analyses. (a) (b)
KRas;At KRas;At KRas;At KRas;At a b Supplementary Figure 1. Gene set enrichment analyses. (a) GO gene sets (MSigDB v3. c5) enriched in KRas;Atg5 fl/+ as compared to KRas;Atg5 fl/fl tumors using gene set
SUPPLEMENTARY INFORMATION
Supplementary text Collectively, we were able to detect ~14,000 expressed genes with RPKM (reads per kilobase per million) > 1 or ~16,000 with RPKM > 0.1 in at least one cell type from oocyte to the morula
The human milk microbiome healthy breasts, mothers, and babies. James A. Foster 22 September 2010
The human milk microbiome healthy breasts, mothers, and babies James A. Foster 22 September 2010 The human milk microbiome healthy breasts, mothers, and babies James A. Foster 22 September 2010 Who cares
Influence of maternal breast milk ingestion on acquisition of the intestinal microbiome in preterm infants
Gregory et al. Microbiome (2016) 4:68 DOI 10.1186/s40168-016-0214-x RESEARCH Open Access Influence of maternal breast milk ingestion on acquisition of the intestinal microbiome in preterm infants Katherine
Early microbial and metabolomic signatures predict later onset of necrotizing enterocolitis in preterm infants
Microbiome This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. Early microbial and metabolomic
Extensive Modulation of the Fecal Metagenome in Children With Crohn s Disease During Exclusive Enteral Nutrition
nature publishing group ORIGINAL CONTRIBUTIONS 1 see related editorial on page x Extensive Modulation of the Fecal Metagenome in Children With Crohn s Disease During Exclusive Enteral Nutrition Christopher
Nature Biotechnology: doi: /nbt Supplementary Figure 1. Experimental design and workflow utilized to generate the WMG Protein Atlas.
Supplementary Figure 1 Experimental design and workflow utilized to generate the WMG Protein Atlas. (a) Illustration of the plant organs and nodule infection time points analyzed. (b) Proteomic workflow
Discovering the Role of the Microbiome in Human Health through the Power of Metagenomics. Tanya Yatsunenko Second Genome
Discovering the Role of the Microbiome in Human Health through the Power of Metagenomics Tanya Yatsunenko Second Genome The microbiome is implicated in the biggest public health threats Ridaura et al,
Gut Reaction. Mary ET Boyle, Ph. D. Department of Cognitive Science UCSD
Gut Reaction Mary ET Boyle, Ph. D. Department of Cognitive Science UCSD Ley, R. et al (2005) PNAS vol. 102 no. 31 Bacterial diversity in the distal gut (ceca) of C57BL6 mice. (A) Phylogenetic tree of
The vaginal bacterial meta-transcriptome
The vaginal bacterial meta-transcriptome Advisors: Dr. Greg Gloor (ggloor@uwo.ca) Dr. Gregor Reid (gregor@uwo.ca) Jean Macklaim mmacklai@uwo.ca The University of Western Ontario The vaginal microbiota
Discriminant Analysis with Categorical Data
- AW)a Discriminant Analysis with Categorical Data John E. Overall and J. Arthur Woodward The University of Texas Medical Branch, Galveston A method for studying relationships among groups in terms of
Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum
Crusell et al. Microbiome (2018) 6:89 https://doi.org/10.1186/s40168-018-0472-x RESEARCH Open Access Gestational diabetes is associated with change in the gut microbiota composition in third trimester
Metabolomic Data Analysis with MetaboAnalyst
Metabolomic Data Analysis with MetaboAnalyst User ID: guest6501 April 16, 2009 1 Data Processing and Normalization 1.1 Reading and Processing the Raw Data MetaboAnalyst accepts a variety of data types
Microbiome and Metabolome Studies:
Microbiome and Metabolome Studies: Overall WHI, Buffalo Clinical Center, and Future Directions Mike LaMonte University at Buffalo WHI Investigators Meeting May 4-5, 2017 Columbus, Ohio Betsy: The Microbiome: