Distinct Co-expression Modules Research Articles

Deciphering the dynamic expression network of fiber elongation and the functional role of the GhTUB5 gene for fiber length in cotton based on an introgression population of upland cotton

IntroductionInterspecific introgression between Gossypium hirsutum and G. barbadense allows breeding cotton with outstanding fiber length (FL). However, the dynamic gene regulatory network of FL-related genes has not been characterized, and the functional mechanism through which the hub gene GhTUB5 mediates fiber elongation has yet to be determined. MethodsCoexpression analyses of 277 developing fiber transcriptomes integrated with QTL mapping using 250 introgression lines of different FL phenotypes were conducted to identify genes related to fiber elongation. The function of GhTUB5 was determined by ectopic expression of two TUB5 alleles in Arabidopsis and knockout of GhTUB5 in upland cotton. Yeast two-hybrid, split-luciferase and pull-down assays were conducted to screen for interacting proteins, and upstream genes were identified by yeast one-hybrid, dual-LUC and electrophoretic mobility shift assays. ResultsThe 32,612, 30,837 and 30,277 genes expressed at 5, 10 and 15 days postanthesis (dpa) were grouped into 19 distinct coexpression modules, and 988 genes in the MEblack module were enriched in the cell wall process and exhibited significant associations with FL. A total of 20 FL-QTLs were identified, each explaining 3.34–16.04 % of the phenotypic variance in the FL. Furthermore, several FL-QTLs contained 15 genes that were differentially expressed in the MEblack module including the tubulin beta gene (TUB5). Compared with the wild type, the overexpression of GhTUB5 and GbTUB5 in Arabidopsis suppressed root cell length but promoted cellulose synthesis. Knockout of GhTUB5 resulted in longer fiber lines. Protein-based experiments revealed that GhTUB5 interacts with GhZFP6. Additionally, GhTUB5 was directly activated by GhHD-ZIP7, a homeobox-leucine zipper transcription factor, and its paralogous gene was previously reported to mediate fiber elongation. ConclusionThis study opens a new avenue to dissect functional mechanism of cotton fiber elongation. Our findings provide some molecular details on how GhTUB5 mediates the FL phenotype in cotton.

Disruption of metazoan gene regulatory networks in cancer alters the balance of co-expression between genes of unicellular and multicellular origins

BackgroundMetazoans inherited genes from unicellular ancestors that perform essential biological processes such as cell division, metabolism, and protein translation. Multicellularity requires careful control and coordination of these unicellular genes to maintain tissue integrity and homeostasis. Gene regulatory networks (GRNs) that arose during metazoan evolution are frequently altered in cancer, resulting in over-expression of unicellular genes. We propose that an imbalance in co-expression of unicellular (UC) and multicellular (MC) genes is a driving force in cancer.ResultsWe combine gene co-expression analysis to infer changes to GRNs in cancer with protein sequence conservation data to distinguish genes with UC and MC origins. Co-expression networks created using RNA sequencing data from 31 tumor types and normal tissue samples are divided into modules enriched for UC genes, MC genes, or mixed UC-MC modules. The greatest differences between tumor and normal tissue co-expression networks occur within mixed UC-MC modules. MC and UC genes not commonly co-expressed in normal tissues form distinct co-expression modules seen only in tumors. The degree of rewiring of genes within mixed UC-MC modules increases with tumor grade and stage. Mixed UC-MC modules are enriched for somatic mutations in cancer genes, particularly amplifications, suggesting an important driver of the rewiring observed in tumors is copy number changes.ConclusionsOur study shows the greatest changes to gene co-expression patterns during tumor progression occur between genes of MC and UC origins, implicating the breakdown and rewiring of metazoan gene regulatory networks in cancer development and progression.

Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics data

Abiotic stresses limit the quantity and quality of rice grain production, which is considered a strategic crop in many countries. In this study, a meta-analysis of different microarray data at seedling stage was performed to investigate the effects of multiple abiotic stresses (drought, salinity, cold situation, high temperature, alkali condition, iron, aluminum, and heavy metal toxicity, nitrogen, phosphorus, and potassium deficiency) on rice. Comparative analysis between multiple abiotic stress groups and their control groups indicated 561 differentially expressed genes (DEGs), among which 422 and 139 genes were up-regulated and down-regulated, respectively. Gene Ontology analysis showed that the process of responding to stresses and stimuli was significantly enriched. In addition, pathways such as metabolic process and biosynthesis of secondary metabolites were identified by KEGG pathway analysis. Weighted correlation network analysis (WGCNA) uncovered 17 distinct co-expression modules. Six modules were significantly associated with genes involved in response to abiotic stresses. Finally, to validate the results of the meta-analysis, five genes, including TIFY9 (JAZ5), RAB16B, ADF3, Os01g0124650, and Os05g0142900 selected for qRT-PCR analysis. Expression patterns of selected genes confirmed the results of the meta-analysis. The outcome of this study could help introduce candidate genes that may be beneficial for use in genetic engineering programs to produce more tolerant crops or as markers for selection.

Identification and Verification of Hub Mitochondrial Dysfunction Genes in Osteoarthritis Based on Bioinformatics Analysis.

Age-related mitochondrial dysfunction and associated oxidative stress may contribute to the development of osteoarthritis. The aim of this study was to identify hub genes associated with mitochondrial dysfunction in osteoarthritis (OA) patients, helping predict the risk of OA, and revealing the mechanism of OA progression. OA expression data and mitochondrial dysfunction genes were downloaded from GEO (GSE55235, GSE82107, and GSE114007) and GeneCard databases. The differentially expressed mitochondrial dysfunction genes (DEMDFGs) between OA and control samples were screened. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathways were analyzed for DEMDFGs. The hub genes were determined by WGCNA and LASSO regression analysis. ROC curves manifested the diagnostic efficacy of each hub gene. A nomogram model was constructed and validated to predict OA risk. The expression of hub genes in OA and normal chondrocytes was verified by external datasets, qRT-PCR and western blotting. A total of 31 DEMDFGs were identified, with 15 genes upregulated and 16 genes downregulated. GO functional enrichment analysis revealed that DEMDFGs were enriched in biological processes related to energy metabolism and cellular respiration. By employing weighted gene coexpression network analysis, we identified four distinct coexpression modules, among which the blue module exhibited the strongest correlation with OA. The intersection between DEMDFGs and this module yielded eight candidate genes. After LASSO analysis of the data, four hub genes (ACADL, CYBA, SLC19A2, and UCP2) were identified as potential biomarkers for OA. The expression levels of these four genes were externally validated in the GSE114007 dataset. And the biologically differential expression of these four genes has been verified in OA and normal chondrocytes. Moreover, the four hub genes had good sensitivity and specificity by ROC curve analysis, and the risk model constructed with these four genes showed promising performance. In conclusion, our study may provide novel mitochondrial dysfunction hub genes with potential clinical applications for understanding the pathology, diagnosis, and treatment of OA.

Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning.

Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers. We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC). The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979. We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.

Comparative transcriptome analysis of compatible and incompatible Brassica napus-Xanthomonas campestris interactions.

Black rot caused by the vascular pathogenic bacterium Xanthomonas campestris pv. campestris (Xcc) is widespread in Brassicaceae plants and an infectious disease that causes large yield losses in oil seed rape (Brassica napus L.). Improvement of resistance through breeding is a crucial strategy to prevent black rot disease in B. napus, but presently hampered by insufficient understanding of Xcc-Brassica interactions. This study compares two EMS-mutagenized B. napus lines that show contrasting resistance levels to their susceptible progenitor. Patterns of differential gene expression between these B. napus lines were evaluated at three time points post inoculation by comparative RNA-seq analysis. In line with the observed disease phenotypes, the susceptible line ZS9mXccS-1 displayed a steady amount of differentially expressed genes (DEGs) at different time points of infection, whereas the resistant line ZS9mXccR-1 displayed a gradual increase in DEGs throughout the course of infection. Weighted gene co-expression network analysis (WGCNA) pinpointed multiple defense-related hub genes with potential central roles in immunity, including the cell surface receptor genes CRK11 and BIR1, and the associated downstream regulatory genes WRKY11 and PBL30. KEGG analysis of DEGs belonging to two distinct co-expression modules revealed enriched pathways associated with defense, including Ca2+-signaling, receptor-mediated immunity, and phytohormone balance. Taken together, our comparative transcriptome analysis provides new avenues to unravel the mechanisms underlying black rot resistance in B. napus.

Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease.

Background Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD. Methods In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣log2FoldChange | >0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve. Results In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD (r = 0.64, P = 3e − 20), and it was visualized by establishing a protein–protein interaction network. Moreover, this module was particularly enriched in “pathways of neurodegeneration—multiple diseases,” “Alzheimer disease,” “oxidative phosphorylation,” and “proteasome.” Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve (AUC = 0.940). Conclusions Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.

Genome-wide association analysis of hippocampal volume identifies enrichment of neurogenesis-related pathways

Adult neurogenesis occurs in the dentate gyrus of the hippocampus during adulthood and contributes to sustaining the hippocampal formation. To investigate whether neurogenesis-related pathways are associated with hippocampal volume, we performed gene-set enrichment analysis using summary statistics from a large-scale genome-wide association study (N = 13,163) of hippocampal volume from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and two year hippocampal volume changes from baseline in cognitively normal individuals from Alzheimer’s Disease Neuroimaging Initiative Cohort (ADNI). Gene-set enrichment analysis of hippocampal volume identified 44 significantly enriched biological pathways (FDR corrected p-value < 0.05), of which 38 pathways were related to neurogenesis-related processes including neurogenesis, generation of new neurons, neuronal development, and neuronal migration and differentiation. For genes highly represented in the significantly enriched neurogenesis-related pathways, gene-based association analysis identified TESC, ACVR1, MSRB3, and DPP4 as significantly associated with hippocampal volume. Furthermore, co-expression network-based functional analysis of gene expression data in the hippocampal subfields, CA1 and CA3, from 32 normal controls showed that distinct co-expression modules were mostly enriched in neurogenesis related pathways. Our results suggest that neurogenesis-related pathways may be enriched for hippocampal volume and that hippocampal volume may serve as a potential phenotype for the investigation of human adult neurogenesis.

Comparative Transcriptomic Analysis Provides Insight into the Domestication and Improvement of Pear (P. pyrifolia) Fruit.

Knowledge of the genetic changes that occurred during the domestication and improvement of perennial trees at the RNA level is limited. Here, we used RNA sequencing analysis to compare representative sets of wild, landrace, and improved accessions of pear (Pyrus pyrifolia) to gain insight into the genetic changes associated with domestication and improvement. A close population relationship and similar nucleotide diversity was observed between the wild and landrace groups, whereas the improved group had substantially reduced nucleotide diversity. A total of 11.13 Mb of genome sequence was identified as bearing the signature of selective sweeps that occurred during pear domestication, whereas a distinct and smaller set of genomic regions (4.04 Mb) was identified as being associated with subsequent improvement efforts. The expression diversity of selected genes exhibited a 20.89% reduction from the wild group to the landrace group, but a 23.13% recovery was observed from the landrace to the improved group, showing a distinctly different pattern with variation of sequence diversity. Module-trait association analysis identified 16 distinct coexpression modules, six of which were highly associated with important fruit traits. The candidate trait-linked differentially expressed genes associated with stone cell formation, fruit size, and sugar content were identified in the selected regions, and many of these could also be mapped to the previously reported quantitative trait loci. Thus, our study reveals the specific pattern of domestication and improvement of perennial trees at the transcriptome level, and provides valuable genetic sources of fruit traits that could contribute to pear breeding and improvement.

Integrative meta-analysis of transcriptomic responses to abiotic stress in cotton

Abiotic environmental stresses are important factors that limit the growth, fiber yield, and quality of cotton. In this study, an integrative meta-analysis and a system-biology analysis were performed to explore the underlying transcriptomic mechanisms that are critical for response to stresses. From the meta-analysis, it was observed that a total of 1465 differentially expressed genes (DEGs) between normal and stress conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that DEGs were significantly enriched in the ubiquitin-dependent process, biosynthesis of secondary metabolites, plant hormone, and signaled transduction. The results also indicated that some of DEGs were assigned to transcription factors (TFs). A total of 148 TFs belonged to 25 conserved families were identified that among them S1Fa-like, ERF, NAC, bZIP families, were the most abundant groups. Moreover, we searched in upstream regions of DEGs for over-represented DNA motifs and were able to identify 11 conserved sequence motifs. The functional analysis of these motifs revealed that they were involved in regulation of transcription, DNA replication, cytoskeleton organization, and translation. Weighted gene co-expression network analysis (WGCNA) uncovered 12 distinct co-expression modules. Four modules were significantly associated with genes involved in response to stress and cell wall organization. The network analysis also identified hub genes such as RTNLB5 and PRA1, which may be involved in regulating stress response. The findings could help to understand the mechanisms of response to abiotic stress and introduce candidate genes that may be beneficial to cotton plant breeding programs.

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

The human papillomavirus (HPV) is present in a significant fraction of head-and-neck squamous cell cancer (HNSCC). The main goal of this study was to identify distinct co-expression patterns between HPV+ and HPV− HNSCC and to provide insights into potential regulatory mechanisms/effects within the analyzed networks. We selected cases deposited in The Cancer Genome Atlas database comprising data of gene expression, methylation profiles and mutational patterns, in addition to clinical information. The intersection among differentially expressed and differentially methylated genes showed the negative correlations between the levels of methylation and expression, suggesting that these genes have their expression levels regulated by methylation alteration patterns in their promoter. Weighted correlation network analysis was used to identify co-expression modules and a systematic approach was applied to refine them and identify key regulatory elements integrating results from the other omics. Three distinct co-expression modules were associated with HPV status and molecular signatures. Validation using independent studies reporting biological experimental data converged for the most significant genes in all modules. This study provides insights into complex genetic and epigenetic particularities in the development and progression of HNSCC according to HPV status, and contribute to unveiling specific genes/pathways as novel therapeutic targets in HNSCC.

A network-based signature to predict the survival of non-smoking lung adenocarcinoma.

BackgroundA substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients has been drawing extensive attention in the past decade. However, effective biomarkers, which could guide the precise treatment, are still limited for identifying high-risk patients. Here, we provide a network-based signature to predict the survival of non-smoking LAC.Materials and methodsGene expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis. Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology. The predictive signature was constructed by penalized Cox regression analysis and tested in two independent datasets.ResultsTwo distinct co-expression modules were significantly correlated with the non-smoking status across 4 Gene Expression Omnibus datasets. Gene Ontology revealed that nuclear division and cell cycle pathways were main mechanisms of the blue module and that genes in the turquoise module were involved in lymphocyte activation and cell adhesion pathways. Seventeen genes were selected from hub genes at an optimal lambda value and built the prognostic signature. The prognostic signature distinguished the survival of non-smoking LAC (training: hazard ratio [HR]=3.696, 95% CI: 2.025–6.748, P<0.001; testing: HR=2.9, 95% CI: 1.322–6.789, P=0.006; HR=2.78, 95% CI: 1.658–6.654, P=0.022) and had moderate predictive abilities in the training and validation datasets.ConclusionThe prognostic signature is a promising predictor of non-smoking LAC patients, which might benefit clinical practice and precision therapeutic management.

Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI

BackgroundThe therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins.ResultsIn the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly correlated with BMI (r = 0.56, P = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed.ConclusionWe identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired.

Co-expression modules identified from published immune signatures reveal five distinct immune subtypes in breast cancer.

There is a growing body of literature demonstrating that immune-related expression signatures predict breast cancer prognosis and chemo-/targeted-therapy responsiveness. However, it is unclear whether these signatures correlate with each other or represent distinct immune-related signals. We evaluated 57 published immune-related expression signatures in four public breast cancer datasets totaling 3295 samples. For each dataset, we used consensus clustering to group signatures together based on their co-expression pattern. Signatures that were in the same consensus cluster across all four datasets were used to define immune modules. Tumors were then classified into immune subtypes based on their module scores using consensus clustering. Survival analysis was conducted using Cox proportional hazards modeling. Consensus clustering consistently yields four distinct co-expression modules across the datasets. These modules appear to represent distinct immune components and signals, where constituent signatures relate to (1) T-cells and/or B-cells (T/B-cell), (2) interferon (IFN), (3) transforming growth factor beta (TGFB), and (4) core-serum response, dendritic cells, and/or macrophages (CSR). Subtyping of tumors based on these co-expression modules consistently yields subsets that fall into five major immune subtypes: T/B-cell/IFN High, IFN/CSR High, CSR High, TGFB High, and Immune Low. Basal and/or triple-negative breast cancer patients with CSR High tumors have significantly worse outcome relative to those within the T/B-cell/IFN High subtype. Our exploratory study identified four distinct immune co-expression modules (T/B-cell, IFN, TGFB, or CSR) from published immune signatures. Using these modules, we identified five immune subtypes with significant outcome differences in basal breast cancers.

Abstract P5-08-12: Co-expression modules identified from published immune signatures reveals five distinct immune subtypes in breast cancer

Abstract Introduction Immune modulating therapies offer an attractive novel approach in the treatment of breast cancer. There is a growing body of literature demonstrating that immune-related expression signatures predict breast cancer prognosis and chemo- and/or targeted therapy responsiveness. However, it is unclear how these signatures relate to one another. Here we evaluated 58 immune signatures in breast cancer and generated co-expression modules to classify patients into immune subtypes. Methods We evaluated 58 published expression signatures related to immune function in 5 breast cancer gene expression datasets (TCGA (n=817), METABRIC (n=1992), EMC344 (n=344), pooled triple negative: GSE31519 (n=579), pooled neoadjuvant chemotherapy treated: GSE25066 (n=508)). For each dataset, consensus clustering was used to subset the signatures based on their co-expression pattern. Signatures in the same consensus cluster across all 5 datasets were used to define immune modules. Module scores were computed as the average across their constituent signatures. Patients were classified into immune subtypes based on their module scores using consensus clustering. Overall survival (OS) differences between immune subtypes were assessed using Cox proportional hazard modeling in basal breast cancers from the METABRIC dataset (n=329). Results Consensus clustering of the 58 expression signatures consistently yields four distinct co-expression modules across the five datasets. These modules appear to represent distinct immune components and signals, with constituent signatures relating to: 1) T-cells and/or B-cells (T/B-cell), 2) interferon (IFN), 3) transforming growth factor beta (TGFB), 4) core serum response, dendritic cells and/or macrophages (CSR). Of note, the T/B-cell module contains 20 of the 58 signatures evaluated; and the CSR module is highly correlated to proliferation (r=0.81). Subtyping of patients based on these co-expression modules consistently yields subsets that fall into five major immune subtypes. The expression pattern of the four modules within each immune subtype is summarized below: T/B-cellIFNTGFBCSRT/B-cell/IFN HighHighHighIntermediateIntermediateIFN/CSR HighLowHighLowHighImmune LowLowLowLowLowCSR HighLowLowLowHighTGFB HighLowLowHighLowImmune Co-expression Modules (columns); Immune Subtypes (rows) These immune subtypes are associated with differences in overall survival in the METABRIC basal breast cancer cases, where the CSR High subtype has the worst outcome (10-year OS: 23%). In comparison, the subsets corresponding to the T/B-cell/IFN High subtype have better outcomes (Hazard ratio: 0.43, p = 0.018). In contrast, no significant outcome differences were observed between the poor outcome CSR-High subtype and the remaining three immune subtypes (p&amp;gt;0.05). Conclusion Our exploratory study identified four distinct immune co-expression modules (T/B-cell, IFN, TGFB, or CSR) from a collection of published immune signatures. Using these modules, we identified 5 immune subtypes with prognostic significance in basal breast cancers. We propose to test representative signatures from the 4 modules and the combined immune subtypes as predictive biomarkers of response to immunotherapies. Citation Format: Amara D, Wolf D, van 't Veer L, Esserman LJ, Campbell MJ, Yau C. Co-expression modules identified from published immune signatures reveals five distinct immune subtypes in breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-08-12.

Genomic Analysis Reveals Disruption of Striatal Neuronal Development and Therapeutic Targets in Human Huntington’s Disease Neural Stem Cells

SummaryWe utilized induced pluripotent stem cells (iPSCs) derived from Huntington’s disease (HD) patients as a human model of HD and determined that the disease phenotypes only manifest in the differentiated neural stem cell (NSC) stage, not in iPSCs. To understand the molecular basis for the CAG repeat expansion-dependent disease phenotypes in NSCs, we performed transcriptomic analysis of HD iPSCs and HD NSCs compared to isogenic controls. Differential gene expression and pathway analysis pointed to transforming growth factor β (TGF-β) and netrin-1 as the top dysregulated pathways. Using data-driven gene coexpression network analysis, we identified seven distinct coexpression modules and focused on two that were correlated with changes in gene expression due to the CAG expansion. Our HD NSC model revealed the dysregulation of genes involved in neuronal development and the formation of the dorsal striatum. The striatal and neuronal networks disrupted could be modulated to correct HD phenotypes and provide therapeutic targets.

A regulatory network-based approach dissects late maturation processes related to the acquisition of desiccation tolerance and longevity of Medicago truncatula seeds.

In seeds, desiccation tolerance (DT) and the ability to survive the dry state for prolonged periods of time (longevity) are two essential traits for seed quality that are consecutively acquired during maturation. Using transcriptomic and metabolomic profiling together with a conditional-dependent network of global transcription interactions, we dissected the maturation events from the end of seed filling to final maturation drying during the last 3 weeks of seed development in Medicago truncatula. The network revealed distinct coexpression modules related to the acquisition of DT, longevity, and pod abscission. The acquisition of DT and dormancy module was associated with abiotic stress response genes, including late embryogenesis abundant (LEA) genes. The longevity module was enriched in genes involved in RNA processing and translation. Concomitantly, LEA polypeptides accumulated, displaying an 18-d delayed accumulation compared with transcripts. During maturation, gulose and stachyose levels increased and correlated with longevity. A seed-specific network identified known and putative transcriptional regulators of DT, including ABSCISIC ACID-INSENSITIVE3 (MtABI3), MtABI4, MtABI5, and APETALA2/ ETHYLENE RESPONSE ELEMENT BINDING PROTEIN (AtAP2/EREBP) transcription factor as major hubs. These transcriptional activators were highly connected to LEA genes. Longevity genes were highly connected to two MtAP2/EREBP and two basic leucine zipper transcription factors. A heat shock factor was found at the transition of DT and longevity modules, connecting to both gene sets. Gain- and loss-of-function approaches of MtABI3 confirmed 80% of its predicted targets, thereby experimentally validating the network. This study captures the coordinated regulation of seed maturation and identifies distinct regulatory networks underlying the preparation for the dry and quiescent states.