Interactions between the tumor and the systemic response of breast cancer patients
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PROVIDER: EGAS00001001804 | EGA |
REPOSITORIES: EGA
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Project description:We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the “clustering behavior” of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is “data-driven” and does not involve manual selection of genes by the investigator. We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules.
2008-08-11 | E-GEOD-11908 | biostudies-arrayexpress
Project description:Here we utilized CRISPR/Cas9 editing and RNAi to disable SR protein expression in HeLa cells. Loss of SR heterodimer expression was confirmed by SILAC proteomic analyses in HeLa Cas9 parental and SR-deficient cells. These studies also demonstrated that the membrane protein proteome was largely unaltered by loss of SR, though reduced expression of a limited subset of membrane proteins was observed.
2021-08-16 | MSV000087992 | MassIVE
Project description:The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings. Experiment Overall Design: Experiment subseries GSE11908 regroups the profiles that have been used to construct the modular transcriptional framework: Experiment Overall Design: The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the âclustering behaviorâ of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is âdata-drivenâ and does not involve manual selection of genes by the investigator. Experiment Overall Design: We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). Experiment Overall Design: The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules. Experiment Overall Design: Experiment subseries GSE11909 regroups the profiles that have been used to identify and validate biomarkers of SLE disease activity: Experiment Overall Design: The proposed biomarker-selection strategy relies on modules for reducing highly dimensional microarray data sets in a stepwise manner. Starting from the full set of 28 modules, only those for which a set minimum proportion of transcripts are significantly changed between the study groups are selected (e.g., minimum proportion of differentially expressed transcripts at p < 0.05 = 15% overexpressed or underexpressed transcripts; in the example given, 11 SLE modules meet this criterion). This eliminates from the selection pool the modules registering fewer consistent changes that could be attributed to noise. Transcriptional vectors were derived for the entire cohort of 22 untreated pediatric SLE patients with the use of this set of 11 SLE modules. Patient profiles were also generated for an independent set of 31 children with SLE treated with steroids and/or cytotoxic drugs and/or hydroxychloroquine. A nonparametric method for analyzing multivariate ordinal data was used to score the patients. Lupus disease flares can lead to irreversible worsening of the patient's status. We tested the relevance of this multivariate transcriptional score for longitudinal monitoring of the disease activity in a cohort of 20 pediatric SLE patients (two to four time points/patient, intervals between each time point varied from one month to 18 months). Half of the patients had been included in our cross-sectional analysis before they were enrolled in this longitudinal study. Parallel trends were observed between multivariate transcriptional scores and a clinical severity score. The positive association was verified statistically with the use of a linear-regression model.
2008-10-04 | E-GEOD-11907 | biostudies-arrayexpress
Project description:The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification. We present a new approach, namely multi-level support vector regression (ml-SVR), to systematically identify conditionspecific regulatory modules. The approach is built upon a multi-level analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. We applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer.
2010-03-10 | GSE20700 | GEO
Project description:The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification. We present a new approach, namely multi-level support vector regression (ml-SVR), to systematically identify conditionspecific regulatory modules. The approach is built upon a multi-level analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. We applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. Three independent total RNA samples were extracted for each cell line (MCF-7 and MCF-7-stripped) and the samples were arrayed using Affymetrix GeneChip HG-U133A. MCF-7-stripped denotes estrogen-deprived MCF-7 human breast cancer cells, which were grown in the absence of estrogen for 96 hours. We analyzed the enriched motifs and their targets for the genes significantly down-regulated in MCF-7-stripped cells as compared to MCF-7 cells.
2010-03-23 | E-GEOD-20700 | biostudies-arrayexpress
Project description:Insulin resistance is necessary but not sufficient for the development of type 2 diabetes. Diabetes results when pancreatic beta-cells fail to compensate for insulin resistance by increasing insulin production through an expansion of beta-cell mass or increased insulin secretion. Communication between insulin target tissues and beta-cells may initiate this compensatory response. Correlated changes in gene expression between tissues can provide evidence for such intercellular communication. We profiled gene expression in six tissues of mice from an obesity-induced diabetes-resistant and a diabetes-susceptible strain before and after the onset of diabetes. We studied the correlation structure of mRNA abundance and identified 105 co-expression gene modules. We provide an interactive gene network model showing the correlation structure between the expression modules within and among the six tissues. This resource also provides a searchable database of gene expression profiles for all genes in six tissues in lean and obese diabetes-resistant and diabetes-susceptible mice, at 4 and 10 weeks of age. A cell cycle regulatory module in islets predicts diabetes susceptibility. The module predicts islet replication; we found a strong correlation between ^2 H_2 O incorporation into islet DNA /in vivo/ and the expression pattern of the cell cycle module. This pattern is highly correlated with that of several individual genes in insulin target tissues, including IGF2, which has been shown to promote beta-cell proliferation, suggesting that these genes may provide a link between insulin resistance and beta-cell proliferation. Keywords: time course, mouse strain comparison, effect of obesity, Type 2 diabetes is a disorder that involves an increased demand for insulin brought about by insulin resistance, together with a failure to compensate with sufficient insulin production. Although Insulin resistance occurs in most obese individuals, diabetes is generally forestalled through compensation with increased insulin. This increase in insulin occurs through an expansion of beta-cell mass and/or increased insulin secretion by individual beta-cells. Failure to compensate for insulin resistance leads to type 2 diabetes. One way to understand the pathophysiology of diabetes is to examine the coordinate changes in gene expression that occur in insulin-responsive tissues and pancreatic islets in obese animals that either compensate for insulin resistance or progress to type 2 diabetes. In each case, there are groups of genes that undergo changes in expression in a highly correlated fashion. By identifying groups of correlated transcripts (gene expression modules) during the compensation and development of diabetes, we can gain insight into potential pathways and regulatory networks in obesity-induced diabetes. We study two strains of mice that differ in obesity-induced diabetes susceptibility. In this study, we surveyed gene expression in six tissues of lean and obese C57BL/6 (B6) and BTBR mice aged 4 wks and 10 wks. B6 mice remain essentially non-diabetic at all ages, irrespective of obesity. When obese, BTBR mice become severely diabetic by 10 weeks of age. By analyzing the correlation structure of the genes under three contrast conditions, obesity, strain, and age, we identified gene expression modules associated with the onset of diabetes and provide an interactive co-expression network model of type 2 diabetes. We found a key module that is comprised of cell cycle regulatory genes. In the islet, the expression profile of these transcripts accurately predicts diabetes and is highly correlated with islet cell proliferation.
2008-03-14 | E-GEOD-10785 | biostudies-arrayexpress
Project description:Bacillus subtilis vegetative cells switch to sporulation upon nutrient limitation. To investigate the proteome changeover during sporulation, a time-lapse proteomic analysis was performed in a cell population that was induced to sporulate synchronously. Here, we are the first to comprehensively investigate the changeover of sporulation regulatory proteins, coat proteins and other proteins involved in sporulation and spore biogenesis. Protein co-expression analysis revealed four co-expressed modules. Modules brown and green are upregulated during sporulation and are annotated as associated with sporulation. Module blue, which is negatively correlated with modules brown and green, comprised ribosomal and metabolic proteins, and module yellow is co-expressed with module blue and modules brown & green. Notably, several proteins not belonging to any of the known transcription regulons were identified as co-expressed with modules brown and green. We speculate that they may also play roles during sporulation. Finally, amounts of some coat proteins, for example morphogenetic coat proteins, decreased late in sporulation. While we speculate on their possible role in guiding or helping assembly of other coat proteins, after which they can be disposed of, such a hypothesis remains to be experimentally addressed.
2021-04-03 | MSV000087154 | MassIVE
Project description:Background: The clinical and pathologic diversity of systemic lupus erythematosus (SLE) has hindered diagnosis, management, and treatment development. This study clustered adult SLE patients through comprehensive molecular phenotyping to improve distinctions with prognostic and therapeutic relevance. Methods: Plasma, serum, and RNA were collected from 198 adult SLE patients. Disease activity was scored by modified SELENA-SLEDAI. Twenty-nine co-expression module scores were calculated from microarray gene-expression data. Plasma soluble mediators (n=23) and autoantibodies (n=13) were assessed by multiplex bead-based assays and ELISAs. Phenotypic patient clusters were identified by machine learning combining K-means clustering and random forest analysis of co-expression module scores and soluble mediators. Findings: SLEDAI scores correlated strongly with interferon module scores, more modestly with plasma cell and select cell cycle modules, and with circulating IFNα, IL21, IL1α, IL17A, IP10, and MIG levels. Co-expression modules and soluble mediators differentiated seven clusters of SLE patients with unique molecular phenotypes. Inflammation and interferon modules were elevated in Clusters 1 (moderately) and 4 (strongly), with decreased T cell modules in Cluster 4. The other clusters differed in monocyte, neutrophil, plasmablast, B cell, and T cell modules. Clusters 1 and 4 had higher SLEDAI scores, and more frequent anti-dsDNA, low complement, and renal activity. These features were also prominent in Cluster 3, which lacked the interferon and inflammation signatures. Arthritis and rashes were common in all clusters. Interpretation: Molecular profiles can distinguish SLE subsets. Prospective longitudinal studies of these profiles may help to improve prognostic evaluation, clinical trial design, and precision medicine approaches.
2020-03-16 | GSE138458 | GEO
Project description:We used GFP-tagged SR proteins expressed at endogenous levels and iCLIP to compare the extent and pattern of SR protein binding to expression-matched lincRNAs and protein-coding RNAs
2018-10-31 | GSE113813 | GEO
Project description:Gene regulatory networks are pivotal for many biological processes. In mouse embryonic stem cells (mESCs) the transcriptional network can be divided into three functionally distinct modules: Polycomb, Core and Myc. The Polycomb module represses developmental genes, while the Myc module is associated with proliferative functions and its mis-regulation is linked to cancer development. Here, we show that in mESCs the Polycomb Repressive Complex 2 (PRC2) associated protein EPOP (a.k.a C17orf96, esPRC2p48, E130012A19Rik) co-localizes at chromatin with members of the Myc and Polycomb module. EPOP interacts with the transcription elongation factor Elongin BC and the H2B deubiquitinase USP7 to modulate transcriptional processes in mESCs similar to MYC. EPOP is commonly up-regulated in human cancer and we demonstrate that its loss impairs the proliferation of several human cancer cell lines. Our findings establish EPOP as a unique modulator of transcriptional processes, impacting both Polycomb and active gene transcription in mammalian cells.
2016-11-17 | GSE85681 | GEO