A Minimal Book Example
1
Microbial Bile Acid Metabolism Shapes T Cell Responses During Inflammation
2
Introduction: load the datasets
2.1
Load packages
2.2
Load datasets
3
Analyze the effect of UDCA administration on the bile acid pool (supplementary figure 5)
3.1
Plot ursodiol exposure and secondary BAs concentrations
3.2
Plot correlation of ursodiol with other bile acid pools: plot conjugated UDCA (tauroursodeoxycholic_acid+glycoursodeoxycholic_acid), TBAs (total_BAs), PBAs (primary_pool), SBAs (secondary_pool), nonUDCA total BAs (total_nonUDCA_pool), nonUDCA SBAs (secondary_nonUDCA), secondary/primary ratio (SP_ratio)
3.3
Create correlation plots to evaluate association of UDCA with all individual BAs
3.3.1
Visualization of significant correlations of UDCA with individual BAs (R>0.4)
4
Create the bile acid pools (figure 4, supplementary figure 6,8)
4.1
Create BA pools first for peri-GVHD-onset timepoint
4.1.1
Plot: TBAs (total_BAs), PBAs (primary_pool), SBAs (secondary_pool), nonUDCA SBAs (secondary_nonUDCA), conjugated (conjugated_pool), unconjugated (unconjugated_pool), sulfated (sulfated_pool)
4.1.2
Create pies
4.2
Create BA pools for peri-engraftment timepoint
4.2.1
Plot BA pools and GVHD; can plot total BAs (total_BAs), PBAs (primary_pool), SBAs (secondary_pool), nonUDCA SBAs (secondary_nonUDCA), conjugated (conjugated_pool), unconjugated (unconjugated_pool), sulfated_pool, secondary/primary ratio and secondary*/primary ratio
4.2.2
Create pies
5
Evaluate T cell modulatory BAs in patients with GVHD vs controls (figure 4)
5.1
3oxoLCA
5.2
isoLCA
5.3
isoDCA
5.4
OMCA
6
Shotgun metagenomic sequencing: Evaluate genes of interest (figure 5, supplement figure 10)
6.1
BSH
6.1.1
Evaluate BSH abundance at peri-GVHD onset
6.1.2
Evaluate BSH abundance at peri-engraftment time point
6.2
Bai operon gene
6.2.1
Evaluate correlation of bai operon gene sum and nonUDCA secondary BAs
6.2.2
Bai operon gene sum in peri-GVHD onset
6.2.3
Bai operon individual gene abundance
6.3
Bile acid related bacteria
6.3.1
Eggerthella lenta
6.3.2
Ruminococcus gnavus
7
Create landscape of all peri-GVHD-onset samples (figure 5)
7.1
Define sample order from higher nonUDCA secondary BAs to lower
7.2
GI GVHD plot
7.3
SBA plot
7.4
A-diversity plot
7.5
Bile acid related genes
7.6
Microbiome composition
7.7
Add all plots together
8
Diversity, bai operon and domination
8.1
Evaluate correlation of a-diversity and bai operon sum
8.2
Identify patients with monodomination by 16S
8.3
Domination and a-diversity
8.4
Domination and bai operon
8.5
SBAs and domination
9
Evaluation of UDCA exposure and clinical outcomes: Teng Fei
9.1
Prepare the patient outcome table
9.2
Evaluate ursodiol exposure and overall survival
9.2.1
Univariable analysis
9.2.2
Multivariable analysis
9.3
Evaluation of cumulative incidences
9.3.1
Cumulative incidence of GVHD-related mortality
9.3.2
Cumulative incidence of Relapse/progression of disease
9.3.3
Cumulative incidence of mortality non-related to GVHD or relapse/progression of disease
9.3.4
Multivariable analysis of GVHD-related mortality
10
scRNA seq Data: Anastasia Kousa
10.1
GSEA RESULTS
10.2
FOLD CHANGE vs FOLD CHANGE
11
RNA sequncing in mice (Figure 2): Anastasia Kousa
11.1
read in file
11.2
link ensembl ids to gene names
11.3
Read in the count file: - D7
11.4
plot GSEA results facet by organ - Hallmarks - D7
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Microbial Bile Acid Metabolism Shapes T Cell Responses During Inflammation
Microbial Bile Acid Metabolism Shapes T Cell Responses During Inflammation
Oriana Miltiadous
2023-08-18
Chapter 1
Microbial Bile Acid Metabolism Shapes T Cell Responses During Inflammation