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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