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Supplementary Material
Double-stranded RNA induces molecular and inflammatory signatures that
are directly relevant to COPD
Paul Harris*1, Sriram Sridhar*2, Ruoqi Peng1, Jonathan E. Phillips1, Ronald G.
Cohn1#, Lisa Burns1, John Woods1, Meera Ramanujam1, Martine Loubeau1,
Gaurav Tyagi3,
John Allard2, Michael Burczinski2, Palanikumar Ravindran2,
Donavan Cheng2, Hans Bitter2, Jay S. Fine1, Carla M.T. Bauer1, and Christopher
S. Stevenson1,4§
Hoffmann-La Roche Inc., pRED, Pharma Research & Early Development, 1DTA
Inflammation,
2Translational
Research Sciences,
3Non-Clinical
Safety, 340
Kingsland Street, Nutley, NJ 07110 USA
4Imperial
College London, National Heart and Lung Institute, Centre for
Respiratory Infections, Respiratory Pharmacology Group, Pharmacology and
Toxicology Section, Exhibition Road, London SW2 7AZ
*Authors have contributed equally to this work;
§Correspondence:
Christopher S. Stevenson, PhD.
Inflammation Discovery
F. Hoffmann-La Roche Inc.
340 Kingsland Street
Nutley, NJ
07110-1199
E-mail: Christopher.stevenson@roche.com
FAX: +1-973-235-5005
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Supplementary methods
Microarray processing and data analysis
Total RNA was isolated from the mouse lung tissue of poly I:C and saline treated
mice across 7 time points (2, 6, 24, 48, 72, 96 hours, 7 days, n=6 per group) and
homogenized in QIAzol reagent. Purified total RNA was amplified and labeled
using NuGen Ovation kits (NuGEN Technologies, Inc., San Carlos, CA) and
samples were hybridized to Affymetrix Mouse 430 2.0 arrays. Array washing,
staining and scanning was performed according to standard Affymetrix protocols
(Affymetrix Inc., Santa Clara, CA). Probe level data was curated by first mapping
individual probe sequences to their most current genome sequences. Probes
which were non-uniquely mapped to specific genes or contained outdated
mappings were discarded, and the remaining probes were summarized into
probesets and normalized using Robust Multi-array Average (RMA). Potential
outlier samples were assessed using principal component analysis (PCA) on all
normalized probesets across all samples. Probe level data was subsequently
summarized to unique genes based on a variance filter, yielding one expression
value per unique gene across all samples. Murine genes were then mapped to
their human orthologs for subsequent pathway analysis. This resulted in 14300
unique mouse genes which mapped to human orthologs and were subsequently
used for analysis.
Differentially expressed genes (DEGs) were determined using an ANOVA,
with pairwise comparisons between poly I:C and saline treatment at each time
point. P-values for DEGs in pairwise comparisons were adjusted using a
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Benjamini-Hochberg correction to account for multiple hypothesis testing
1.
Genes that were significantly altered at least 2-fold between poly I:C treatment
and saline controls (false discovery rate, FDR < 0.05) were considered to be
differentially expressed. Unsupervised hierarchical clustering was performed on
the union of DEGs between poly I:C and saline-treated samples across all time
points to determine phases of response to poly I:C treatment. Common and
unique genes between these phases of response were determined for each
phase by taking the union and intersection of DEGs.
Gene ontology and pathway analysis
Gene ontology (GO) functional analysis and pathway enrichment analysis were
performed on DEGs between saline and poly I:C treated mice at each time point.
Enrichment of functional ontologies for all DEGs was determined by a
hypergeometric test, using internally curated GO biological process annotations.
Internal curation of this repository involved generating clusters of GO categories
based on the degree of overlap between sets of genes within a functional group.
Significantly enriched clusters were determined based on a mean FDR cutoff for
all the groups within a cluster. Similarly, pathway enrichment for all DEGs was
determined using internally curated data from the NCI Pathway Interaction
Database (http://pid.nci.nih.gov/index.shtml). This repository includes pathway
data imported from BioCarta and Reactome. Significantly enriched clusters of
pathways were also determined as described above.
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Gene set enrichment analysis of custom inflammatory gene signatures
Additional pathway analyses were conducted for custom defined inflammatory
signatures of interest which have been compiled from literature using three
different text-mining resources: Ingenuity’s Knowledgebase (Ingenuity Systems,
Inc, Redwood City, CA), Ariadne’s ResNet database (Ariadne Genomics,
Rockville, MD), I2E from Linguamatics (Linguamatics Ltd, Cambridge, UK).
These signatures include TLR3 signaling. An additional set of blood-cell specific
signatures were also obtained from a previous study involving expression
profiling of 17 different blood cell subtypes from resting and activated cell
populations 2. Signatures of interest were evaluated in the murine poly I:C
expression dataset using gene set enrichment analysis
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to determine altered
expression of signaling pathways in response to poly I:C treatment at each time
point. Briefly, enrichment of gene sets was calculated against the entire set of
14300 genes from the poly I:C treatment dataset, ranked based on a composite
score of fold-change and FDR differences between saline and poly I:C treatment
at each time point. FDR values were determined for gene set enrichment by
permuting genes within gene sets.
Modular analysis of blood transcriptomic signatures
Enrichment of blood transcriptome modules was performed as previously
described 4. Briefly individual sets of poly I:C DEGs were compiled for each time
point assayed. Enrichment of each set of poly I:C DEGs was determined across
each of 28 blood transcriptome modules by determining the overlap of poly I:C
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DEGs with each module, normalized by the total number of genes within a
module.
Modular enrichment = (number up-regulated genes) – (number downregulated genes) / total number of genes in module
The significance of the enrichment was determined using a hypergeometric test.
Gene set variation analysis
Enrichment of poly I:C genes in clinical datasets was determined using the gene
set variation analysis (GSVA) algorithm 5, as implemented in the R software
environment (http://www.r-project.org/). Briefly, GSVA determines enrichment of
gene sets in an expression dataset on a per sample basis by transforming the
gene-by-sample matrix into a gene set-by-sample matrix. The transformation is
carried out using a non-parametric, unsupervised approach, calculating relative
enrichment of a gene set in each sample across a sample space. This allows for
the sample-wise comparison of gene set enrichment across a dataset. GSVA
enrichment scores were calculated using the up- and down-regulated poly I:C
genes from the in vivo murine data as gene sets. Relative enrichment of these
gene sets was then calculated on a per-sample basis in 2 clinical microarray
datasets available on the Gene Expression Omnibus (GEO): GSE1122
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and
GSE10667 7. GSE1122 consisted of lung tissue samples from COPD and nonCOPD lungs, while GSE10667 was comprised of lung tissue from stable and
acute exacerbations of IPF and non-IPF controls. Statistical significance of gene
set enrichment was determined by applying a linear model to the GSVA
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enrichment scores within each dataset and determining FDR for pairwise
differences between sample groups (e.g. COPD vs. Control in GSE1122, Stable
IPF v. Control in GSE10667, etc.). Unsupervised hierarchical clustering was also
performed on the enrichment scores for each dataset to determine clusters of
samples within a dataset which showed similar enrichment of poly I:C signatures.
Non-invasive airway hyper-responsiveness (AHR)
To determine if AHR was a feature of the model, AHR was measured at 6, 24 or
48 hours after poly I:C insult. Mice were placed into a whole–body
plethysmograph (WBP) interfaced with computers using differential pressure
transducers (BUXCO system). After at least a 5 minute acclimation period, mice
were exposed to aerosolized normal saline, followed by increasing doses of
methacholine (MCh)solution (5, 10, 20, 40, 80 mg/ml, Sigma-Aldrich). Beginning
after each aerosol challenge, enhanced pause (Penh) readings were measured
for 5 minutes and are represented as averaged values for each dose.
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Supplementary Figure Legends
Figure S1. Poly I:C dose dependently increases the numbers of total cells (A),
neutrophils (B), and lymphocytes (C) in the BALF. Neutrophil and lymphocyte
numbers were determined by performing differential cell counts. Data are
expressed as mean ± SEM n = 8 - 10 mice. Significance (relative to the poly I:C
vehicle control) was determined using a Student's t-test and is denoted as
follows: *p < 0.05; **p< 0.01; and ***p< 0.001.
Figure S2. The major inflammatory features induced by poly I:C instillation
include peribronchiolar (A), perivascular (B) and interstitial (C) inflammatory cell
infiltrate. Representative images for control samples are provided for reference
(D - F). Lung section were stained with H&E and pictures taken at 400x
magnification.
Figure S3. Poly I:C induces AHR to nebulized methacholine challenges at 24
hours. Airway responses to increasing doses of methacholine were measured
using conscious, whole body plethsmography at 6 (A), 24 (B), and 48 (C) hours
post poly I:C administration. Data are expressed as mean ± SEM of n = 8 mice.
Significance was determined using the AUC values relative to the saline control
group.
Figure S4. Monocyte, dendritic cell, and NK cell markers enriched in response to
poly I:C treatment. (A) Gene set enrichment analysis of blood cell marker gene
panels from a previously published study (Abbas et al., 2000) were applied to this
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murine poly I:C data-set. Columns represent the entire ranked gene list based
on pairwise comparisons of poly I:C versus saline treatment at each time point.
Each row represents individual gene sets. Each box denotes the enrichment
score of the gene set against the poly I:C data at each timepoint. Up-regualted
gene sets are shaded red, while down-regulated gene sets are shaded blue.
Inflammatory modules based on analysis of blood transcriptional modules from a
previously published study (Chaussabel et al,. 2008) were enriched in response
to poly I:C treatment (B). Columns represent comparisons of poly I:C versus
saline treatment at each time point, while rows represent individual transcriptional
modules, Circles are shaded red if there is a high degree of overlap between
genes in a module and up-regulated poly I:C genes at a specific time-point.
Circles are shaded blue if there is a high degree of overlap between genes in a
module and down-regulated poly I:C genes at a time-point. Asterisks indicate
statistically significant (hypergeometric p < 0.05) overlaps between module genes
and differentially expressed poly I:C genes.
Figure S5. Poly I:C induces the expression of cytokines and chemokines as
measured by microarray analysis. TNFalpha (A), KC (B), MIP-1beta (C),
RANTES (D), and total IL-12 (E) were measured by microarray. Data are
expressed as mean ± SEM of n = 6 mice. Significance (relative to the poly I:C
vehicle control) was determined using a Student's t-test and is denoted as
follows: *p < 0.05; **p< 0.01; and ***p< 0.001.
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Figure S6. Flow of gene set variation analysis. Two publically available data
sets (GSE1122 and GSE10667) were used to test per-patient enrichment of poly
I:C signatures using gene set variation analysis.
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