Download Weighted Gene Coexpression Network Analysis of blood gene

Weighted Gene Coexpression Network Analysis of blood
gene expression data correlates immune and other bloodrelated pathways with clinical parameters in subtypes of
Major Depressive Disorder
Lynn Yieh*, Jon Greene**, Tatiana Khasanova**, Stephen Wicks**, Julie Bryant**, and Gayle Wittenberg*
* Janssen Research & Development, LLC , ** Rancho BioSciences, LLC
INTRODUCTION
COMPARISON BETWEEN MD AND HC NETWORKS
Molecular blood biomarkers for Major Depressive Disorder (MDD) have been difficult to
reproduce across studies and suggest that MDD could be a heterogeneous disorder with
multiple underlying mechanisms contributing to its pathophysiology. Here we examine one
subtype, Melancholic Depression, in order to identify molecular pathways that may not be
associated with a broader diagnosis of MDD. The study cohort included a collection of blood
samples from healthy control subjects as well as unmedicated subjects suffering from MDD. RNA
samples were processed by hybridization to Affymetrix HGU133 Plus 2.0 chips and the resulting
data were analyzed using Weighted Gene Coexpression Network Analysis (WGCNA, P. Langfelder
and S. Horvath, BMC Bioinformatics 2008, 9:559).
CORRELATIONS WITH CLINICAL PARAMETERS
• Modules for each network are indicated on their respective axes along with their
number of gene components
Module Eigengenes in the MD network were correlated with clinical parameters CORE
Melancholia Score and MINI_GAD Score
• Numbers in table represent number of genes shared between intersecting modules
from the two networks
• Opposing correlations between GREEN and YELLOW modules with MINI_GAD Score
• Fisher Exact Test P-value (Log10-transformed) for module overlap is represented in
table by intensity of color
• Darker color indicates significant overlap, no color indicates no significant overlap
• Correlation between PINK module and CORE Melancholia Score
• Modules from each network that appear to have no correlate in the other network are
outlined
METHODS
• These modules may represent pathways that are disrupted or dysregulated in MD
individuals compared to HC’s
100 Healthy Controls (HC)
100 Major Depressive Disorder (Unmedicated)
Ham-D
Ham-A
MINI
SCID
CORE
Major Depressive Disorder (MDD):
HAMD-17 >= 18
Transcriptomics
Melancholic
Depression:
(MD)

CORE >=8
Design. Blood samples were collected from 100 healthy control subjects and 100 unmedicated (> 6 weeks
abstinence or treatment-naive) depressed subjects (HAMD-17 >= 18). Melancholic depression was assessed using
CORE (CORE >= 8). Samples were analyzed using transcriptomic (Affymetrix, HGU133 Plus 2.0) technology.
• Correlations are observed in MD network as well as for MD Cohort within Consensus
Networks
• Consensus Networks 1, 2, and 3 (CONS1, CONS2, and CONS3) were built by combining
MD with HC1, HC2, and HC3 cohorts respectively
Weighted Gene Coexpression Network Analysis
• Calculate Pearson correlation for genes across samples
• Transform correlations with “Power Adjacency Function”  Adjacency
• Amplifies strong connections and dampens weak connections
• This results in a more “hub and spoke” type of network
• Measure “Topological Overlap” dissimilarity (TOM) using Adjacency
• Who is connected to who and by how much
• Perform hierarchical clustering based on TOM
• Dendrogram
• Define modules by cutoff height in the dendrogram
• Modules are identified by color
• The R package “WGCNA” was used for all analyses (Langfelder P and Horvath S, WGCNA:
an R package for weighted correlation network analysis.
• BMC Bioinformatics 2008, 9:559 doi:10.1186/1471-2105-9-559, Peter Langfelder, Steve
Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering.
Journal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/).
• Analysis workflows were based on those contained in the excellent tutorials that can be
found here:
http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials
/index.html
Network
Module
CONS2 - MD
Cohort
pink
CONS1 - MD
Cohort
red
• The MD Grey60, Magenta, and Midnight Blue Modules outlined above had no correlate
in any of the HC1, HC2, or HC3 networks
CONS1 - MD
Cohort
yellow
MD
pink
• Gene lists from dysregulated modules were subjected to enrichment analysis for
representation in GO Biological Process networks using methods contained in the
WGCNA R Package
CONS3 - MD
Cohort
CONS3 - MD
Cohort
• Comparisons as per above were performed between MD and each of the three HC
networks and dysregulated modules were observed
• While many of the modules displayed no enrichment for GO pathways, there were
some that reproduced across the HC cohorts
Network
Module
HC1
greenyellow
Agenda
Agenda
HC1
Identify and quantify relative path lengths
between neighbors to derive topological
overlap
HC2
HC3
MD
lightcyan
salmon
purple
lightgreen
GO Enrichment: Biologic Processes
respiratory electron transport chain, electron transport chain,
No MD
cellular respiration, energy derivation by oxidation of organic
correlate
compounds
MD
black
pink
yellow
Property
GO Enrichment
erythrocyte development, erythrocyte
homeostasis, porphyrin-containing compound
Correlation with CORE
metabolic process, myeloid cell homeostasis,
Melancholic Score
hemoglobin metabolic process, erythrocyte
differentiation
erythrocyte development, porphyrin-containing
Correlation with CORE
compound metabolic process,hemoglobin
Melancholic Score
metabolic process, erythrocyte homeostasis,
myeloid cell homeostasis
lymphocyte differentiation, T cell activation, T cell
Correlation with MINI_GAD
differentiation, lymphocyte activation
Correlation with CORE
porphyrin-containing compound biosynthetic
Melancholic Score
process
Correlation with CORE
porphyrin-containing compound metabolic
Melancholic Score
process
respiratory electron transport chain, electron
Correlation with MINI_GAD
transport chain
type I interferon signaling pathway, cellular
Correlation (negative) with response to type I interferon, defense response
MINI_GAD
to virus, response to cytokine, innate immune
response, response to other oranisms, etc.
Cor
P value
0.5
0.02
0.5
0.02
0.43
0.06
0.49
0.03
0.48
0.03
0.45
0.05
-0.49
0.03
Property
response to bacterium, defense response to fungus, response to
No MD
biotic stimulus, response to external stimulus, response to other
correlate
organism, response to fungus
No MD
killing by host of symbiont cells, defense response to fungus
correlate
defense response to fungus, defense response to bacterium,
No MD
response to fungus, killing of cells of other organisms, response to
correlate
bacterium
complement activation, classical pathway; humoral immune
No HC3 response mediated by circulating immunoglobin, complement
correlate activation, adaptive immune response base on somatic
recombination…
NETWORK MODULE VISUALIZATION
The RED module from the CONS1 consensus module can be visualized based on the
different topological overlap between the MD and HC cohorts
• GO Pathways for which the module is enriched are indicated by node shape as per the
legends
• The thickness of the edges indicate the degree of Topological Overlap between the nodes
• In the MD cohort, a lighter colored node indicates stronger correlation with the CORE
Melancholia Score
• E.g.: Note differences in topology between the ERAF, EBP42, and EBP49 nodes
suggesting the role of hemoglobin metabolism and erythrocyte shape in this analysis
Reprinted from Yip and Horvath, GTOM_tech_report.pdf
Reprinted from Linked: The New Science of Networks by Albert-Laszlo Barabasi
These results suggest involvement of immune related pathways as well as energy related pathways in MD
CONS1-HC Red Module
• For WGCNA data from 4 cohorts were selected
• MD Cohort: 20 individuals diagnosed with Melancholic Depression
• HC1, HC2, & HC3: Three distinct cohorts of 20 randomly selected Healthy Controls
• Gene expression data from these cohorts was filtered by variance result in a common
set of 12,072 probes that were used for the analysis
CONSENSUS NETWORK COMPARISONS
• The following networks were constructed:
• Individual networks for each of the MD, HC1, HC2, & HC3 cohorts
• SFT Power value = 12, DeepSplit value = 1
• Consensus networks for the MD cohort paired with each HC cohort
• SFT Power value = 12, DeepSplit value = 2
• SFT Power and DeepSplit values were empirically chosen to generate ~20 modules per
network
GO ID
• Network comparisons and correlations were calculated using standard methods as
implemented in the WGCNA R Package
GO:0006778
GO Term
porphyrin-containing compound
metabolic process
GO:0020027 hemoglobin metabolic process
GO:0048821 erythrocyte development
NETWORK DENDROGRAM – HEALTHY CONTROLS
CONS1-MD Red Module
CONCLUSIONS
• WGCNA provided definition of network modules that can be associated with various
parameters of Melancholic Depression as defined in this study including the CORE
Melancholia and MINI_GAD scores
• Our results suggest possible roles for biological processes involved with erythrocytes,
hemoglobin metabolism, immune function and respiratory metabolism that can potentially
mark subtypes of MDD
• Close inspection of network modules indicate individual genes that may be drivers for
dissimilarities in network modules between cohorts