Genome-wide Gene Expression Data Research Articles

Worldwide patterns of human epigenetic variation.

DNA methylation is an epigenetic modification, influenced by both genetic and environmental variation, that plays a key role in transcriptional regulation and many organismal phenotypes. Although patterns of DNA methylation have been shown to differ between human populations, it remains to be determined how epigenetic diversity relates to the patterns of genetic and gene expression variation at a global scale. Here we measured DNA methylation at 485,000 CpG sites in five diverse human populations, and analysed these data together with genome-wide genotype and gene expression data. We found that population-specific DNA methylation mirrors genetic variation, and has greater local genetic control than mRNA levels. We estimated the rate of epigenetic divergence between populations, which indicates far greater evolutionary stability of DNA methylation in humans than has been observed in plants. This study provides a deeper understanding of worldwide patterns of human epigenetic diversity, as well as initial estimates of the rate of epigenetic divergence in recent human evolution.

Integrative epigenomics, transcriptomics and proteomics of patient chondrocytes reveal genes and pathways involved in osteoarthritis

Osteoarthritis (OA) is a common disease characterized by cartilage degeneration and joint remodeling. The underlying molecular changes underpinning disease progression are incompletely understood. We investigated genes and pathways that mark OA progression in isolated primary chondrocytes taken from paired intact versus degraded articular cartilage samples across 38 patients undergoing joint replacement surgery (discovery cohort: 12 knee OA, replication cohorts: 17 knee OA, 9 hip OA patients). We combined genome-wide DNA methylation, RNA sequencing, and quantitative proteomics data. We identified 49 genes differentially regulated between intact and degraded cartilage in at least two –omics levels, 16 of which have not previously been implicated in OA progression. Integrated pathway analysis implicated the involvement of extracellular matrix degradation, collagen catabolism and angiogenesis in disease progression. Using independent replication datasets, we showed that the direction of change is consistent for over 90% of differentially expressed genes and differentially methylated CpG probes. AQP1, COL1A1 and CLEC3B were significantly differentially regulated across all three –omics levels, confirming their differential expression in human disease. Through integration of genome-wide methylation, gene and protein expression data in human primary chondrocytes, we identified consistent molecular players in OA progression that replicated across independent datasets and that have translational potential.

A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease.

Integrating diverse genomics data can provide a global view of the complex biological processes related to the human complex diseases. Although substantial efforts have been made to integrate different omics data, there are at least three challenges for multi-omics integration methods: (i) How to simultaneously consider the effects of various genomic factors, since these factors jointly influence the phenotypes; (ii) How to effectively incorporate the information from publicly accessible databases and omics datasets to fully capture the interactions among (epi)genomic factors from diverse omics data; and (iii) Until present, the combination of more than two omics datasets has been poorly explored. Current integration approaches are not sufficient to address all of these challenges together. We proposed a novel integrative analysis framework by incorporating sparse model, multivariate analysis, Gaussian graphical model, and network analysis to address these three challenges simultaneously. Based on this strategy, we performed a systemic analysis for glioblastoma multiforme (GBM) integrating genome-wide gene expression, DNA methylation, and miRNA expression data. We identified three regulatory modules of genomic factors associated with GBM survival time and revealed a global regulatory pattern for GBM by combining the three modules, with respect to the common regulatory factors. Our method can not only identify disease-associated dysregulated genomic factors from different omics, but more importantly, it can incorporate the information from publicly accessible databases and omics datasets to infer a comprehensive interaction map of all these dysregulated genomic factors. Our work represents an innovative approach to enhance our understanding of molecular genomic mechanisms underlying human complex diseases.

Abstract 4957: Gene expression signature of Gleason score is associated with prostate cancer outcomes in a radical prostatectomy cohort

Abstract Background: Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide. Many men treated for clinically localized PCa will be cured, however, 20 to 30% of men will relapse and some will experience metastatic-lethal (ML) progression. Gleason score (GS) is one of the best predictors of PCa aggressiveness, but additional tumor biomarkers may improve its prognostic accuracy. We developed a gene expression signature of GS to enhance prediction of PCa outcomes. Methods: Elastic Net regularization was used to construct a gene expression signature by contrasting tumors with GS 8-10 (high) vs. ≤6 (low) in The Cancer Genome Atlas (TCGA). Tumor tissue samples obtained at radical prostatectomy for a Fred Hutchinson (FH) patient cohort of men with localized PCa were used to generate genome-wide gene expression data. The gene expression signature was then evaluated for its ability to predict recurrence and ML progression in the FH patient cohort (N=503; NRecurrence=106; NML progression=27; mean follow-up for recurrence=8 years). Results: The expression signature includes transcripts representing 49 genes. In the FH cohort, the signature was associated with recurrence and ML progression with hazard ratios (HRs) for a 25% increase in the signature of 1.51 (95% CI: 1.24-1.82; P=2.7×10-5) and 2.41 (95% CI: 1.51-3.85; P=0.0002), respectively. Among patients with GS 7 tumors, the signature was also significantly associated with PCa recurrence (HR=1.38, 95% CI: 1.09-1.76; P=0.008) and ML progression (HR=2.42, 95% CI: 1.30-4.52; P=0.006). The signature’s area under the curve (AUC) for predicting recurrence and ML progression was 0.68 and 0.76, respectively. Compared to a model with pathological stage and GS only, the gene expression signature significantly improved the AUC for overall recurrence (3%, P=0.0003) and ML progression (7%, P=0.0004), particularly among patients with GS 7 tumors (recurrence: 5%, P=0.01; ML progression: 13%, P=0.009). Higher levels of the signature were associated with increased expression of genes in cell cycle-related pathways including G2M checkpoint, epithelial mesenchymal transition, and E2F targets pathways, and decreased expression of genes in several pathways including androgen response, estrogen response, oxidative phosphorylation, and apoptosis. Conclusion: The gene expression signature based on GS may improve the prediction of overall recurrence as well as ML progression in PCa patients after radical prostatectomy, in particular among men with GS 7 tumors. Citation Format: Min A Jhun, Milan S. Geybels, Jonathan L. Wright, Suzanne Kolb, Craig April, Marina Bibikova, Elaine A. Ostrander, Jian-Bing Fan, Ziding Feng, Janet L. Stanford. Gene expression signature of Gleason score is associated with prostate cancer outcomes in a radical prostatectomy cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4957. doi:10.1158/1538-7445.AM2017-4957

TCL1A Single-Nucleotide Polymorphisms and Estrogen-Mediated Toll-Like Receptor-MYD88-Dependent Nuclear Factor-κB Activation: Single-Nucleotide Polymorphism- and Selective Estrogen Receptor Modulator-Dependent Modification of Inflammation and Immune Response.

In a previous genome-wide association study (GWAS) for musculoskeletal adverse events during aromatase inhibitor therapy for breast cancer, we reported that single nucleotide polymorphisms (SNPs) near the TCL1A gene were associated with this adverse drug reaction. Functional genomic studies showed that TCL1A expression was induced by estradiol, but only in cells with the variant sequence for the top GWAS SNP (rs11849538), a SNP that created a functional estrogen response element. In addition, TCL1A genotype influenced the downstream expression of a series of cytokines and chemokines and had a striking effect on nuclear factor κB (NF-κB) transcriptional activity. Furthermore, this SNP-dependent regulation could be reversed by selective ER modulators (SERMs). The present study was designed to pursue mechanisms underlying TCL1A SNP-mediated, estrogen-dependent NF-κB activation. Functional genomic studies were performed using a panel of 300 lymphoblastoid cell lines for which we had generated genome-wide SNP and gene expression data. It is known that toll-like receptors (TLRs) can regulate NF-κB signaling by a process that requires the adaptor protein MYD88. We found that TLR2, TLR7, TLR9, and TLR10 expression, as well as that of MYD88, could be modulated by TCL1A in a SNP and estrogen-dependent fashion and that these effects were reversed in the presence of SERMs. Furthermore, MYD88 inhibition blocked the TCL1A SNP and estrogen-dependent NF-κB activation, as well as protein-protein interaction between TCL1A and MYD88. These observations greatly expand the range of pathways influenced by TCL1A genotype and raise the possibility that this estrogen- and SNP-dependent regulation might be altered pharmacologically by SERMs.

Intertumoral Heterogeneity within Medulloblastoma Subgroups

While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials.

Abstract 204: Prioritizing Genome-Wide Association Study Lipid Loci With Global Gene Networks

Large genome-wide association studies (GWAS) conducted in humans by the Global Lipids Genetics Consortium (GLGC) have identified more than 150 genome-wide significant loci that are associated with variation in plasma lipid levels (cholesterol, LDL/HDL-cholesterol, and triglycerides). Some loci contain genes with well-described roles in lipid metabolism, such as CETP , LDLR , and APOB ; however, for many genome-wide significant loci, there is no clear causal gene. To test the hypothesis that lipid genes in the liver are co-regulated, we constructed global co-expression gene networks from genome-wide gene expression data obtained from the livers of multiple independent mouse genetic crosses. All together, we constructed global gene networks from eight distinct studies, representing more than 800 unique mice of diverse genetic backgrounds. For all studies analyzed, we identified a module (or sub-network) of genes that is significantly enriched (p<1x10 -15 ) functionally for cholesterol biosynthesis and metabolism genes. This module ranges in size from 70 to 824 genes across the eight studies and contains all genes involved in the cholesterol biosynthesis pathway ( Acat2, Hmgcs2, Hmgcr, Lss, Sc5d, etc. ). This module also contains many genes involved in the regulation of cholesterol metabolism, such as Ldlr , Pcsk9 , and Insig1 . Because of the significant enrichment of cholesterol genes in this module, we have begun to cross-reference all genes in the module against the GLGC lipid GWAS data. Through this analysis, we have identified genes of unknown function that are clearly located within genome-wide significant lipid loci as well as sub-threshold (suggestive significant) lipid loci. Among the genes we identified was Sestrin1 , which was located within a clear sub-threshold locus associated with plasma cholesterol (rs12206606; p=1.4 x 10 -5 ). In conclusion, our studies provide a framework to identify causal genes within reported lipid GWAS loci as well as to identify novel sub-threshold loci associated with variations in lipids among humans. We illustrate the approach by identifying Sestrin1 within a sub-threshold locus associated with plasma cholesterol levels and show that Sestrin1 is transcriptionally regulated in the liver by dietary cholesterol.

Integrated analysis of genome-wide DNA methylation and gene expression data provide a regulatory network in intrauterine growth restriction

Intrauterine growth restriction (IUGR), a serious complication of perinatal period, results in metabolic and brain disorders. However, the exact cause of IUGR remains unclear. Recently, some evidences indicated that epigenetic modification plays an important role in IUGR. Hence, in this study the methylation status and gene expression profiles of IUGR were compared to investigate the changes in epigenetic regulation and gene expression induced by IUGR. DNA samples extracted from blood samples of infants with IUGR and controls appropriate for gestational age (AGA) were analyzed with Illumina Human Methylation 450k array to identify differences in genome-wide DNA methylation, and results were verified by Mass ARRAY. Moreover, an IUGR rat model was established by maternal malnutrition method, and gene expression profiles associated with progressive DNA methylation changes in brain tissue were detected using microarray Affymetrix Rat Gene 2.0ST. Based on DNA methylation array, 2265 genes are differentially methylated between IUGR and AGA 1338 genes in the promoter region. Genes with differentially methylated CpG loci in the promoter region enriched in 10 pathways.1311 differentially expressed genes were obtained by Microarray. 36 significantly enriched pathways were identified. After comparing DNA methylation data with gene expression data, 49 genes showed a negative correlation between DNA methylation and gene expression. 27 commonly enriched pathways were identified, which involved sugar, fat and protein metabolism, diseases of the nervous system, cancer, and immunomodulation and endocrine regulation. These findings suggest the epigenetic regulatory mechanisms on corresponding gene expression which may play a role in the adult-onset diseases induced by IUGR.

Genetic architecture of gene expression underlying variation in host response to porcine reproductive and respiratory syndrome virus infection

It has been shown that inter-individual variation in host response to porcine reproductive and respiratory syndrome (PRRS) has a heritable component, yet little is known about the underlying genetic architecture of gene expression in response to PRRS virus (PRRSV) infection. Here, we integrated genome-wide genotype, gene expression, viremia level, and weight gain data to identify genetic polymorphisms that are associated with variation in inter-individual gene expression and response to PRRSV infection in pigs. RNA-seq analysis of peripheral blood samples collected just prior to experimental challenge (day 0) and at 4, 7, 11 and 14 days post infection from 44 pigs revealed 6,430 differentially expressed genes at one or more time points post infection compared to the day 0 baseline. We mapped genetic polymorphisms that were associated with inter-individual differences in expression at each day and found evidence of cis-acting expression quantitative trait loci (cis-eQTL) for 869 expressed genes (qval < 0.05). Associations between cis-eQTL markers and host response phenotypes using 383 pigs suggest that host genotype-dependent differences in expression of GBP5, GBP6, CCHCR1 and CMPK2 affect viremia levels or weight gain in response to PRRSV infection.

Abstract P098: An Epigenome-wide Study of Obesity in African American Youth and Young Adults: Novel Findings, Replication in Neutrophils and Relationship With Gene Expression

Background: Several large-scale epigenome wide association studies on obesity-related DNA methylation changes have been published and in total identified 46 CpG sites. These studies were conducted in middle-aged and older adults of Caucasians and African Americans (AAs) using leukocytes. To what extend these signals are independent of cell compositions as well as to what extend they may influence gene expression have not been systematically investigated. Furthermore, the high prevalence of obesity comorbidities in middle-aged or older population may hide or bias obesity itself related DNA methylation changes. Methods: In this study of healthy AA youth and young adults, genome wide DNA methylation data from leukocytes were obtained from three independent studies: EpiGO study (96 obese cases vs. 92 lean controls, aged 14-21, 50% females, test of interest is obesity status), LACHY study (284 participants from general population, aged 14-18, 50% females, test of interest is BMI), and Georgia Stress and Heart study (298 participants from general population, aged 18-38, 52% females, test of interest is BMI) using the Infinium HumanMethylation450 BeadChip. Genome wide DNA methylation data from purified neutrophils as well as genome wide gene expression data from leukocytes using Illumina HT12 V4 array were also obtained for the EpiGO samples. Results: The meta-analysis on the 3 cohorts identified 76 obesity related CpG sites in leukocytes with p&lt;1х10 -7 . Out of the 46 previously identified CpG sites, 36 can be replicated in this AA youth and young adult sample with same direction and p&lt;0.05. Out of the 107 CpG sites including the 36 replicated ones and the 71 newly identified ones, 71 CpG sites (66%) had their relationship with obesity replicated in purified neutrophils (p&lt;0.05). The analysis on the cis regulation of the 107 CpG sites on gene expression showed that 59 CpG sites had at least one gene within 250kb having expression difference between obese cases and lean controls. Furthermore, out of the 59 CpG sites, 6 showed significantly negative correlations and 1 showed significantly positive correlation with the differentially expressed genes. These CpG sites located in SOCS3, CISH, ABCG1, PIM3 and PTGDS genes. Conclusion: In this study of AA youth and young adults, we identified novel CpG sites associated with obesity and replicated majority of the CpG sites previously identified in middle-aged and older adults. For the first time, we showed that majority of the obesity related CpG sites identified from leukocytes are not driven by cell compositions and provided the direct link between DNA methylation-gene expression-obesity status for 7 CpG sites in 5 genes.

Abstract P111: A Genome-Wide Gene Expression Study of Blood Pressure in Twins

Background: Recently, a genome wide gene expression study identified 34 genes that were differentially expressed in relation to blood pressure (BP). However, to what extend these BP related gene expression signatures are driven by genetic factors or environmental factors have not been investigated. Methods: In this study of 383 twins (84 DZ pairs, 105 MZ pairs and 5 singletons; age 32-69; 40% male) recruited from the Finland Twin Cohort Study, genome-wide gene expression data in peripheral leukocytes were obtained using illumine HT12 V4 array. Systolic and diastolic BP related gene expression changes were identified using linear mixed model. Structural equation modeling was used to estimate the genetic and environmental sources of variance of BP related gene expression signatures. Results: Our genome wide gene expression analysis identified 1 gene ( ECT2 , FDR=0.019) with its expression level associated with SBP and 1 gene ( LMNA , FDR=0.004) with its expression level associated with DBP at the cutoff of FDR&lt;0.05. Out of the previously identified SBP (N=21) and DBP related genes (N=20), 5 genes ( CD97, TAGLN2, MYADM, SL31A2 and TIPARP ) for SBP and 5 genes ( CD97, S100A10, MYADM, TIPARP and SLC31A) for DBPcan be replicated in the twin cohort with same directions and p&lt;0.05. Four genes showed association with both SBP and DBP. Structural equation modeling was conducted on the expression levels of the newly identified BP related genes as well as the ones that were previously identified but can been replicated in this study. Out of the 8 genes, genetic factors contributed significantly to the expression of 2 genes ( LMNA and S100A10 ) and shared environmental factors contributed significantly to the expression of 1 gene ( MYADM ), while for 5 genes, both genetic and shared environmental factors have significant contributions to their gene expression levels. Conclusion: In this study of twins, we identified novel genes with its expression level associated with BP and replicated several previously identified signals. Our study further provides new insights into the genetic and environmental sources of BP related gene expression signatures.

Analysis of Epidermal Growth Factor Receptor Related Gene Expression Changes in a Cellular and Animal Model of Parkinson's Disease.

We employed transcriptome analysis of epidermal growth factor receptor related gene expression changes in cellular and animal models of Parkinson’s disease (PD). We used a well-known Parkinsonian toxin 1-methyl-4-phenylpyridine (MPP+) to induce neuronal apoptosis in the human neuroblastoma SH-SY5Y cell line. The MPP+-treatment of SH-SY5Y cells was capable of inducing neuro-apoptosis, but it remains unclear what kinds of transcriptional genes are affected by MPP+ toxicity. Therefore the pathways that were significantly perturbed in MPP+ treated human neuroblastoma SH-SY5Y cells were identified based on genome-wide gene expression data at two time points (24 and 48 h). We found that the Epidermal Growth Factor Receptor (EGFR) pathway-related genes showed significantly differential expression at all time points. The EGFR pathway has been linked to diverse cellular events such as proliferation, differentiation, and apoptosis. Further, to evaluate the functional significance of the altered EGFR related gene expression observed in MPP+-treated SH-SY5Y cells, the EGFR related GJB2 (Cx26) gene expression was analyzed in an MPP+-intoxicated animal PD model. Our findings identify that the EGFR signaling pathway and its related genes, such as Cx26, might play a significant role in dopaminergic (DAergic) neuronal cell death during the process of neuro-apoptosis and therefore can be focused on as potential targets for therapeutic intervention.

The genetic regulation of transcription in human endometrial tissue.

Do genetic effects regulate gene expression in human endometrium? This study demonstrated strong genetic effects on endometrial gene expression and some evidence for genetic regulation of gene expression in a menstrual cycle stage-specific manner. Genetic effects on expression levels for many genes are tissue specific. Endometrial gene expression varies across menstrual cycle stages and between individuals, but there are limited data on genetic control of expression in endometrium. We analysed genome-wide genotype and gene expression data to map cis expression quantitative trait loci (eQTL) in endometrium. We recruited 123 women of European ancestry. DNA samples from blood were genotyped on Illumina HumanCoreExome chips. Total RNA was extracted from endometrial tissues. Whole-transcriptome profiles were characterized using Illumina Human HT-12 v4.0 Expression Beadchips. We performed eQTL mapping with ~8 000 000 genotyped and imputed single nucleotide polymorphisms (SNPs) and 12 329 genes. We identified a total of 18 595 cis SNP-probe associations at a study-wide level of significance (P < 1 × 10-7), which correspond to independent eQTLs for 198 unique genes. The eQTLs with the largest effect in endometrial tissue were rs4902335 for CHURC1 (P = 1.05 × 10-32) and rs147253019 for ZP3 (P = 8.22 × 10-30). We further performed a context-specific eQTL analysis to investigate if genetic effects on gene expression regulation act in a menstrual cycle-specific manner. Interestingly, five cis-eQTLs were identified with a significant stage-by-genotype interaction. The strongest stage interaction was the eQTL for C10ORF33 (PYROXD2) with SNP rs2296438 (P = 2.0 × 10-4), where we observe a 2-fold difference in the average expression levels of heterozygous samples depending on the stage of the menstrual cycle. The summary eQTL results are publicly available to browse or download. A limitation of the present study was the relatively modest sample size. It was not powered to identify trans-eQTLs and larger sample sizes will also be needed to provide better power to detect cis-eQTLs and cycle stage-specific effects, given the substantial changes in expression across the menstrual cycle for many genes. Identification of endometrial eQTLs provides a platform for better understanding genetic effects on endometriosis risk and other endometrial-related pathologies. Funding for this work was provided by NHMRC Project Grants GNT1026033, GNT1049472, GNT1046880, GNT1050208, GNT1105321 and APP1083405. There are no competing interests.

Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data

BackgroundWith the increase in the amount of DNA methylation and gene expression data, the epigenetic mechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene expression data into a network by specifying the anti-correlation between them. However, the correlation between methylation and expression is usually unknown and difficult to determine.ResultsTo address this issue, we present a novel multiple network framework for epigenetic modules, namely, Epigenetic Module based on Differential Networks (EMDN) algorithm, by simultaneously analyzing DNA methylation and gene expression data. The EMDN algorithm prevents the specification of the correlation between methylation and expression. The accuracy of EMDN algorithm is more efficient than that of modern approaches. On the basis of The Cancer Genome Atlas (TCGA) breast cancer data, we observe that the EMDN algorithm can recognize positively and negatively correlated modules and these modules are significantly more enriched in the known pathways than those obtained by other algorithms. These modules can serve as bio-markers to predict breast cancer subtypes by using methylation profiles, where positively and negatively correlated modules are of equal importance in the classification of cancer subtypes. Epigenetic modules also estimate the survival time of patients, and this factor is critical for cancer therapy.ConclusionsThe proposed model and algorithm provide an effective method for the integrative analysis of DNA methylation and gene expression. The algorithm is freely available as an R-package at https://github.com/william0701/EMDN.

Integration of Genome-Wide TF Binding and Gene Expression Data to Characterize Gene Regulatory Networks in Plant Development.

Key transcription factors (TFs) controlling the morphogenesis of flowers and leaves have been identified in the model plant Arabidopsis thaliana. Recent genome-wide approaches based on chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) enable systematic identification of genome-wide TF binding sites (TFBSs) of these regulators. Here, we describe a computational pipeline for analyzing ChIP-seq data to identify TFBSs and to characterize gene regulatory networks (GRNs) with applications to the regulatory studies of flower development. In particular, we provide step-by-step instructions on how to download, analyze, visualize, and integrate genome-wide data in order to construct GRNs for beginners of bioinformatics. The practical guide presented here is ready to apply to other similar ChIP-seq datasets to characterize GRNs of interest.

Genome-Wide Gene Expression Signature Of Depression

Background Many studies have attempted to identify the molecular signature of depression. The results, however, are inconsistent. The small sample sizes, choice of tissue/cell types and suboptimal statistical methods are the major limitations that might contribute to this inconsistency. In our study, to identify the whole blood transcriptome signature of geriatric depression, we utilized two large population cohorts aged over 65 -The Sydney Memory and Aging Study, MAS (N=521) and The Older Australian Twin Study, OATS (N=324) as discovery and replication cohorts, respectively. Major Depression was assessed according to DSM-IV criteria. Methods The genome-wide gene expression data were obtained using Illumina HT-12 v4. After quality control and pre-processing, the application of stringent filtering criteria (by detection p-value (p Results The eigengenes of two modules were associated with the depression phenotype (p=0.01 and p=0.02). Closer inspection of the modules of interest revealed that only 37 out of 82 genes in one module and 17 out of 64 genes in another module were significantly associated with the phenotype of depression. Correlational analyses of individual genes within the depression relevant modules revealed that 8 out of 37 significant genes in one module were protein coding genes, involved in various translational, metabolic and immune processes (PCYOX1L, RPL14, MCTS1, GIMAP7, NDUFB9, BOLA2, EIF3M, RPL7A). The second module contained 5 protein coding genes out of 17 associated with depression, the known molecular functions of which include catalytic, enzyme regulation, transcription factor and translational regulation activities (PRCP, POLR2J2, ATF4, TAOK3, EIF2B5). The two top genes from both modules are known to be involved in metabolic process regulation by reactive oxygen species (PCYOX1L, prenylcysteine oxidase-like (p Discussion Our results support the oxidative stress hypothesis of depression and provide new insights into pathophysiological mechanisms of geriatric depression. In addition, we will present the results from pathway analyses (Ingenuity Pathway Analysis software, IPA) performed on genes identified as relevant to depression in this study. To bridge genotype with whole blood transcriptome and identify genomic loci that influence the identified gene expression signature of depression, we will present results from genome-wide eQTL analyses, which are ongoing.

Integrated High Throughput Analysis Identifies GSK3 as a Crucial Determinant of p53-Mediated Apoptosis in Lung Cancer Cells

Background/Aims: p53 dysfunction is frequently observed in lung cancer. Although restoring the tumour suppressor function of p53 is recently approved as a putative strategy for combating cancers, the lack of understanding of the molecular mechanism underlying p53-mediated lung cancer suppression has limited the application of p53-based therapies in lung cancer. Methods and Results: Using RNA sequencing, we determined the transcriptional profile of human non-small cell lung carcinoma A549 cells after treatment with two p53-activating chemical compounds, nutlin and RITA, which could induce A549 cell cycle arrest and apoptosis, respectively. Bioinformatics analysis of genome-wide gene expression data showed that distinct transcription profiles were induced by nutlin and RITA and 66 pathways were differentially regulated by these two compounds. However, only two of these pathways, 'Adherens junction' and 'Axon guidance', were found to be synthetic lethal with p53 re-activation, as determined via integrated analysis of genome-wide gene expression profile and short hairpin RNA (shRNA) screening. Further functional protein association analysis of significantly regulated genes associated with these two synthetic lethal pathways indicated that GSK3 played a key role in p53-mediated A549 cell apoptosis, and then gene function study was performed, which revealed that GSK3 inhibition promoted p53-mediated A549 cell apoptosis in a p53 post-translational activity-dependent manner. Conclusion: Our findings provide us with new insights regarding the mechanism by which p53 mediates A549 apoptosis and may cast light on the development of more efficient p53-based strategies for treating lung cancer.

Role of major and brain-specific Sgce isoforms in the pathogenesis of myoclonus-dystonia syndrome

Loss-of-function mutations in SGCE, which encodes ε-sarcoglycan (ε-SG), cause myoclonus-dystonia syndrome (OMIM159900, DYT11). A “major” ε-SG protein derived from CCDS5637.1 (NM_003919.2) and a “brain-specific” protein, that includes sequence derived from alternative exon 11b (CCDS47642.1, NM_001099400.1), are reportedly localized in post- and pre-synaptic membrane fractions, respectively. Moreover, deficiency of the “brain-specific” isoform and other isoforms derived from exon 11b may be central to the pathogenesis of DYT11. However, no animal model supports this hypothesis. Gene-trapped ES cells (CMHD-GT_148G1-3, intron 9 of NM_011360) were used to generate a novel Sgce mouse model (C57BL/6J background) with markedly reduced expression of isoforms derived from exons 3′ to exon 9 of NM_011360. Among those brain regions analyzed in adult (2month-old) wild-type (WT) mice, cerebellum showed the highest relative expression of isoforms incorporating exon 11b. Homozygotes (SgceGt(148G1)Cmhd/Gt(148G1)Cmhd or SgceGt/Gt) and paternal heterozygotes (Sgcem+/pGt, m-maternal, p-paternal) showed 60 to 70% reductions in expression of total Sgce. Although expression of the major (NM_011360) and brain-specific (NM_001130189) isoforms was markedly reduced, expression of short isoforms was preserved and relatively small amounts of chimeric ε-SG/β-galactosidase fusion protein was produced by the Sgce gene-trap locus. Immunoaffinity purification followed by mass spectrometry assessments of Sgcem+/pGt mouse brain using pan- or brain-specific ε-SG antibodies revealed significant reductions of ε-SG and other interacting sarcoglycans. Genome-wide gene-expression data using RNA derived from adult Sgcem+/pGt mouse cerebellum showed that the top up-regulated genes were involved in cell cycle, cellular development, cell death and survival, while the top down-regulated genes were associated with protein synthesis, cellular development, and cell death and survival. In comparison to WT littermates, Sgcem+/pGt mice exhibited “tiptoe” gait and stimulus-induced appendicular posturing between Postnatal Days 14 to 16. Abnormalities noted in older Sgcem+/pGt mice included reduced body weight, altered gait dynamics, and reduced open-field activity. Overt spontaneous or stimulus-sensitive myoclonus was not apparent on the C57BL/6J background or mixed C57BL/6J-BALB/c and C57BL/6J-129S2 backgrounds. Our data confirm that mouse Sgce is a maternally imprinted gene and suggests that short Sgce isoforms may compensate, in part, for deficiency of major and brain-specific Sgce isoforms.

Removal of unwanted variation reveals novel patterns of gene expression linked to sleep homeostasis in murine cortex.

BackgroundWhy we sleep is still one of the most perplexing mysteries in biology. Strong evidence indicates that sleep is necessary for normal brain function and that sleep need is a tightly regulated process. Surprisingly, molecular mechanisms that determine sleep need are incompletely described. Moreover, very little is known about transcriptional changes that specifically accompany the accumulation and discharge of sleep need. Several studies have characterized differential gene expression changes following sleep deprivation. Much less is known, however, about changes in gene expression during the compensatory response to sleep deprivation (i.e. recovery sleep).ResultsIn this study we present a comprehensive analysis of the effects of sleep deprivation and subsequent recovery sleep on gene expression in the mouse cortex. We used a non-traditional analytical method for normalization of genome-wide gene expression data, Removal of Unwanted Variation (RUV). RUV improves detection of differential gene expression following sleep deprivation. We also show that RUV normalization is crucial to the discovery of differentially expressed genes associated with recovery sleep. Our analysis indicates that the majority of transcripts upregulated by sleep deprivation require 6 h of recovery sleep to return to baseline levels, while the majority of downregulated transcripts return to baseline levels within 1–3 h. We also find that transcripts that change rapidly during recovery (i.e. within 3 h) do so on average with a time constant that is similar to the time constant for the discharge of sleep need.ConclusionsWe demonstrate that proper data normalization is essential to identify changes in gene expression that are specifically linked to sleep deprivation and recovery sleep. Our results provide the first evidence that recovery sleep is comprised of two waves of transcriptional regulation that occur at different times and affect functionally distinct classes of genes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3065-8) contains supplementary material, which is available to authorized users.

Temporal dynamics of the developing lung transcriptome in three common inbred strains of laboratory mice reveals multiple stages of postnatal alveolar development.

To characterize temporal patterns of transcriptional activity during normal lung development, we generated genome wide gene expression data for 26 pre- and post-natal time points in three common inbred strains of laboratory mice (C57BL/6J, A/J, and C3H/HeJ). Using Principal Component Analysis and least squares regression modeling, we identified both strain-independent and strain-dependent patterns of gene expression. The 4,683 genes contributing to the strain-independent expression patterns were used to define a murine Developing Lung Characteristic Subtranscriptome (mDLCS). Regression modeling of the Principal Components supported the four canonical stages of mammalian embryonic lung development (embryonic, pseudoglandular, canalicular, saccular) defined previously by morphology and histology. For postnatal alveolar development, the regression model was consistent with four stages of alveolarization characterized by episodic transcriptional activity of genes related to pulmonary vascularization. Genes expressed in a strain-dependent manner were enriched for annotations related to neurogenesis, extracellular matrix organization, and Wnt signaling. Finally, a comparison of mouse and human transcriptomics from pre-natal stages of lung development revealed conservation of pathways associated with cell cycle, axon guidance, immune function, and metabolism as well as organism-specific expression of genes associated with extracellular matrix organization and protein modification. The mouse lung development transcriptome data generated for this study serves as a unique reference set to identify genes and pathways essential for normal mammalian lung development and for investigations into the developmental origins of respiratory disease and cancer. The gene expression data are available from the Gene Expression Omnibus (GEO) archive (GSE74243). Temporal expression patterns of mouse genes can be investigated using a study specific web resource (http://lungdevelopment.jax.org).